diff --git a/announcements/correlationcartoon.jpeg b/announcements/correlationcartoon.jpeg
new file mode 100644
index 0000000..a9b3823
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diff --git a/announcements/evalutation.pdf b/announcements/evalutation.pdf
new file mode 100644
index 0000000..ec41ac7
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diff --git a/announcements/evalutation.tex b/announcements/evalutation.tex
new file mode 100644
index 0000000..c528f4f
--- /dev/null
+++ b/announcements/evalutation.tex
@@ -0,0 +1,60 @@
+\documentclass[pdftex]{beamer}
+\usepackage{beamerthemedefault}
+\usepackage{multimedia}
+\usepackage{wasysym}
+\useoutertheme[subsection=false]{smoothbars}
+\useinnertheme[shadow=true]{rounded}
+
+\setbeamercolor{block title}{fg=black,bg=gray}
+\setbeamercolor{block title alerted}{use=alerted text,fg=black,bg=alerted text.fg!75!bg}
+\setbeamercolor{block title example}{use=example text,fg=black,bg=example text.fg!75!bg}
+\setbeamercolor{block body}{parent=normal text,use=block title,bg=block title.bg!25!bg}
+\setbeamercolor{block body alerted}{parent=normal text,use=block title alerted,bg=block title alerted.bg!25!bg}
+\setbeamercolor{block body example}{parent=normal text,use=block title example,bg=block title example.bg!25!bg}
+
+\usepackage{hyperref}
+\usepackage[english]{babel}			% language set to new-german
+\usepackage[utf8]{inputenc} 	% coding of german special characters
+\usepackage{graphicx}  % \includegraphics[options]{file.eps}
+\usepackage{array}
+\usepackage{setspace}
+\usepackage{color}
+\usepackage{eqlist}
+\usepackage{wallpaper}
+
+\definecolor{tug}{HTML}{CC0000}
+\newcommand{\rot}[1]{{\color{tug} #1}}
+\definecolor{green}{HTML}{1C8C1C}
+\newcommand{\gruen}[1]{{\color{green} #1}}
+\definecolor{blue}{HTML}{2424AA}
+\newcommand{\blau}[1]{{\color{blue} #1}}
+\definecolor{gray}{rgb}{0.8,0.8,0.8}
+\definecolor{gray2}{rgb}{0.5,0.5,0.5}
+\definecolor{gray2}{rgb}{0.5,0.5,0.5}
+\newcommand{\grau}[1]{{\color{gray2} #1}}
+
+\setbeamercolor{footline}{fg=black,bg=gray}
+
+%\setbeamertemplate{headline}[text line]{
+%  \begin{beamercolorbox}[wd=\paperwidth,ht=8ex,dp=4ex]{}%\vskip-4pt
+%    \insertnavigation{0.85\paperwidth} 
+%    \hskip-1pt\rule{\paperwidth}{0.3pt}
+%  \end{beamercolorbox}
+%}
+
+\setlength{\unitlength}{0.01\paperwidth}
+
+\setbeamercolor{background canvas}{bg=}
+
+ 
+\begin{document} 
+
+\begin{frame}
+  \frametitle{Evalutation}
+  In Ilias:\\[2ex]
+  Go to {\em Evaluation} and fill out the forms!\\[6ex]
+  \includegraphics[width=0.8\textwidth]{correlationcartoon}  
+\end{frame}
+
+
+\end{document}
diff --git a/linearalgebra/code/Chorthippus_biguttulus.wav b/linearalgebra/code/Chorthippus_biguttulus.wav
new file mode 100644
index 0000000..2c57068
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diff --git a/linearalgebra/code/PeterUndDerWolf.wav b/linearalgebra/code/PeterUndDerWolf.wav
new file mode 100644
index 0000000..80aab98
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diff --git a/linearalgebra/code/Tielli-Kraulis.wav b/linearalgebra/code/Tielli-Kraulis.wav
new file mode 100644
index 0000000..8ad071a
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diff --git a/linearalgebra/code/coordinaterafo2.m b/linearalgebra/code/coordinaterafo2.m
new file mode 100644
index 0000000..054ac6d
--- /dev/null
+++ b/linearalgebra/code/coordinaterafo2.m
@@ -0,0 +1,52 @@
+% some vectors:
+x = [ -1:0.02:1 ];
+y = x*0.5 + 0.1*randn( size(x) );
+plot( x, y, '.b' );
+hold on
+plot( x(10), y(10), '.r' );
+
+% new coordinate system:
+%e1 = [ 3/5 4/5 ];
+%e2 =  [ -4/5 3/5 ];
+e1 = [ 3 4 ];
+e2 =  [ -4 3 ];
+me1 = sqrt( e1*e1' );
+e1 = e1/me1;
+me2 = sqrt( e2*e2' );
+e2 = e2/me2;
+quiver( 0.0, 0.0 , e1(1), e1(2), 1.0, 'r' )
+quiver( 0.0, 0.0 , e2(1), e2(2), 1.0, 'g' )
+axis( 'equal' )
+
+% project [x y] onto e1 and e2:
+
+% % the long way:
+% nx = zeros( size( x ) );  % new x coordinate
+% ny = zeros( size( y ) );  % new y coordinates
+% for k=1:length(x)
+%     xvec = [ x(k) y(k) ];
+%     nx(k) = xvec * e1';
+%     ny(k) = xvec * e2';
+% end
+% plot( nx, ny, '.g' );
+
+% the short way:
+%nx = [ x' y' ] * e1';
+%nx = [x; y]' * e1';
+% nx = e1 * [ x; y ];
+% ny = e2 * [ x; y ];
+% plot( nx, ny, '.g' );
+
+% even shorter:
+n = [e1; e2 ] * [ x; y ];
+plot( n(1,:), n(2,:), '.g' );
+
+
+
+
+
+
+
+
+
+hold off
diff --git a/linearalgebra/code/coordinatetrafo.m b/linearalgebra/code/coordinatetrafo.m
new file mode 100644
index 0000000..7c8507c
--- /dev/null
+++ b/linearalgebra/code/coordinatetrafo.m
@@ -0,0 +1,37 @@
+% some vectors:
+x=[-1:0.02:1]';  % column vector!
+y=4*x/3 + 0.1*randn( size( x ) );
+plot( x, y, '.b' );
+axis( 'equal' );
+hold on;
+
+% new coordinate system:
+% e1 = [ 3 4 ]
+% e2 = [ -4 3 ]
+% and normalized to unit length (factor 1/5):
+e = [ 3/5 -4/5; 4/5 3/5 ];
+quiver( [0 0], [0 0], e(1,:), e(2,:), 1.0, 'r' );
+
+% project [x y] onto e1 and e2:
+
+% % the long way:
+% nx = x;
+% ny = y;
+% for k=1:length(x)
+%     nx(k) = [x(k) y(k)]*e(:,1);
+%     ny(k) = [x(k) y(k)]*e(:,2);
+% end
+% plot( nx, ny, '.g' );
+
+% the short way
+% nx = [x y]*e(:,1);
+% ny = [x y]*e(:,2);
+% plot( nx, ny, '.g' );
+
+% even shorter:
+n = [x y]*e;
+plot( n(:,1), n(:,2), '.g' );
+xlabel( 'x' );
+ylabel( 'y' );
+
+hold off;
diff --git a/linearalgebra/code/correlationcoefficient.m b/linearalgebra/code/correlationcoefficient.m
new file mode 100644
index 0000000..55472ab
--- /dev/null
+++ b/linearalgebra/code/correlationcoefficient.m
@@ -0,0 +1,29 @@
+set( 0, 'DefaultTextFontSize', 22.0 );
+set( 0, 'DefaultAxesFontSize', 22.0 );
+
+n = 10000;
+x = 1.0*randn( n, 1 );
+z = 1.0*randn( n, 1 );
+for r=-1:0.2:1
+    y = r*x + sqrt(1.0-r^2.0)*z;
+    cv = cov( x, y );
+    cr = corrcoef( x, y );
+    scatter( x, y, 'filled', 'MarkerEdgeColor', 'white' );
+    xlabel( 'x' );
+    ylabel( 'y' );
+    text( 0.05, 0.9, sprintf( 'r = %.2f', r ), 'Units', 'normalized' )
+    text( 0.05, 0.8, sprintf( 'cor = %.2f   %.2f', cr(1,1), cr(1,2) ), 'Units', 'normalized' )
+    text( 0.05, 0.7, sprintf( 'cov = %.2f   %.2f', cv(1,1), cv(1,2) ), 'Units', 'normalized' )
+    waitforbuttonpress;
+end
+
+y = x.^2.0 + 0.5*z;
+cv = cov( x, y );
+cr = corrcoef( x, y );
+scatter( x, y, 'filled', 'MarkerEdgeColor', 'white' );
+xlabel( 'x' );
+ylabel( 'y' );
+text( 0.05, 0.9, sprintf( 'x^2', r ), 'Units', 'normalized' )
+text( 0.05, 0.8, sprintf( 'cor = %.2f   %.2f', cr(1,1), cr(1,2) ), 'Units', 'normalized' )
+text( 0.05, 0.7, sprintf( 'cov = %.2f   %.2f', cv(1,1), cv(1,2) ), 'Units', 'normalized' )
+
diff --git a/linearalgebra/simulations/covareigen.m b/linearalgebra/code/covareigen.m
similarity index 83%
rename from linearalgebra/simulations/covareigen.m
rename to linearalgebra/code/covareigen.m
index 1acf330..cd6025f 100644
--- a/linearalgebra/simulations/covareigen.m
+++ b/linearalgebra/code/covareigen.m
@@ -44,11 +44,16 @@ function covareigen( x, y, pm, w, h )
     % plot eigenvectors:
     quiver( ones( 1, 2 ).*mean( x ), ones( 1, 2 )*mean(y), v(1,:).*sqrt(diag(d))', v(2,:).*sqrt(diag(d))', 'r', 'LineWidth', 3, 'AutoScale', 'off', 'AutoScaleFactor', 1.0, 'MaxHeadSize', 0.7 )
 
+    xlabel( 'x' );
+    ylabel( 'y' );
     axis( 'equal' );
     hold off;
 
+    % histogram of x values:
     subplot( 2, 2, 3 );
     hist( x, 50, 'b' );
+    xlabel( 'x' );
+    ylabel( 'n_x' );
 
     % sort the eigenvalues:
     [d,inx] = sort( diag(d), 'descend' );
@@ -59,24 +64,28 @@ function covareigen( x, y, pm, w, h )
     x = x - mean( x );
     y = y - mean( y );
     % project onto eigenvectors:
-    nx = [ x y ] * v(:,inx(1));
-    ny = [ x y ] * v(:,inx(2));
-    cv = cov( [nx, ny] )
+    nc = [ x y ] * v(:,inx);
+    cv = cov( nc )
     [ v , d] = eig( cv )
     if (pm > 0) & (nargin == 5)
         if pm == 1
-            [n,c] = hist3([nx ny], { xp, yp } );
+            [n,c] = hist3( nc, { xp, yp } );
             contourf( c{1}, c{2}, n' );
         else
             gauss = reshape( exp(-0.5*diag(xy*inv(cv)*xy'))/sqrt((2.0*pi)^2.0*det(cv)), size( xg ) );
             contourf( xp, yp, gauss )
         end
     else
-        scatter( nx, ny, 'b', 'filled', 'MarkerEdgeColor', 'white' );
+        scatter( nc(:,1), nc(:,2), 'b', 'filled', 'MarkerEdgeColor', 'white' );
     end
+    xlabel( 'x' );
+    ylabel( 'y' );
     axis( 'equal' );
     hold off;
 
+    % histogram of new x values:
     subplot( 2, 2, 4 );
-    hist( nx, 50, 'b' );
+    hist( nc(:,1), 50, 'b' );
+    xlabel( 'new x' );
+    ylabel( 'n_newx' );
 end
diff --git a/linearalgebra/simulations/covareigen3.m b/linearalgebra/code/covareigen3.m
similarity index 64%
rename from linearalgebra/simulations/covareigen3.m
rename to linearalgebra/code/covareigen3.m
index c11f2de..636d9cb 100644
--- a/linearalgebra/simulations/covareigen3.m
+++ b/linearalgebra/code/covareigen3.m
@@ -20,25 +20,40 @@ function covareigen3( x, y, z )
     ylabel( 'y' );
     zlabel( 'z' );
     grid on;
-
+    
     % plot eigenvectors:
     quiver3( ones( 1, 3 ).*mean( x ), ones( 1, 3 )*mean(y), ones( 1, 3 )*mean(z), v(1,:).*sqrt(diag(d))', v(2,:).*sqrt(diag(d))', v(3,:).*sqrt(diag(d))', 'r', 'LineWidth', 3, 'AutoScale', 'off', 'AutoScaleFactor', 1.0, 'MaxHeadSize', 0.7 )
     
     %axis( 'equal' );
     hold off;
 
+    % 2D scatter plots:
+    subplot( 2, 6, 4 );
+    scatter( x, y, 'b', 'filled', 'MarkerEdgeColor', 'white' );
+    xlabel( 'x' )
+    ylabel( 'y' )
+    subplot( 2, 6, 5 );
+    scatter( x, z, 'b', 'filled', 'MarkerEdgeColor', 'white' );
+    xlabel( 'x' )
+    ylabel( 'z' )
+    subplot( 2, 6, 6 );
+    scatter( y, z, 'b', 'filled', 'MarkerEdgeColor', 'white' );
+    xlabel( 'y' )
+    ylabel( 'z' )
+
     % sort the eigenvalues:
     [d,inx] = sort( diag(d), 'descend' );
     
-    subplot( 2, 2, 2 );
+    subplot( 2, 2, 4 );
     hold on;
     % subtract means:
     x = x - mean( x );
     y = y - mean( y );
     % project onto eigenvectors:
-    nx = [ x y z ] * v(:,inx(1));
-    ny = [ x y z ] * v(:,inx(2));
-    scatter( nx, ny, 'b', 'filled', 'MarkerEdgeColor', 'white' );
+    nx = [ x y z ] * v(:,inx);
+    scatter( nx(:,1), nx(:,2), 'b', 'filled', 'MarkerEdgeColor', 'white' );
+    xlabel( 'ex' )
+    ylabel( 'ey' )
     axis( 'equal' );
     hold off;
 
diff --git a/linearalgebra/code/covareigen3examples.m b/linearalgebra/code/covareigen3examples.m
new file mode 100644
index 0000000..f27c76c
--- /dev/null
+++ b/linearalgebra/code/covareigen3examples.m
@@ -0,0 +1,19 @@
+scrsz = get( 0, 'ScreenSize' );
+set( 0, 'DefaultFigurePosition', [ scrsz(3)/2 scrsz(4)/2 scrsz(3)/2 scrsz(4)/2 ] );
+
+n = 10000;
+
+% three distributions:
+x = randn( n, 1 );
+y = randn( n, 1 );
+z = randn( n, 1 );
+dx = [ 0 8 0 0 ];
+dy = [ 0 0 8 0 ];
+dz = [ 0 0 0 8 ];
+for k = 1:4
+    x((k-1)*n/4+1:k*n/4) = x((k-1)*n/4+1:k*n/4) + dx(k);
+    y((k-1)*n/4+1:k*n/4) = y((k-1)*n/4+1:k*n/4) + dy(k);
+    z((k-1)*n/4+1:k*n/4) = z((k-1)*n/4+1:k*n/4) + dz(k);
+end
+f = figure( 1 );
+covareigen3( x, y, z );
diff --git a/linearalgebra/simulations/covareigenexamples.m b/linearalgebra/code/covareigenexamples.m
similarity index 88%
rename from linearalgebra/simulations/covareigenexamples.m
rename to linearalgebra/code/covareigenexamples.m
index f360e8c..2f7a480 100644
--- a/linearalgebra/simulations/covareigenexamples.m
+++ b/linearalgebra/code/covareigenexamples.m
@@ -5,11 +5,11 @@ set( 0, 'DefaultFigurePosition', [ scrsz(3)/2 scrsz(4)/2 scrsz(3)/2 scrsz(4)/2 ]
 n = 10000;
 x = randn( n, 1 );
 f = figure( 1 );
-for r = 0.01:0.199:1
+for r = 0.01:0.19:1
     fprintf( 'Correlation = %g\n', r );
     clf( f );
     y = r*x + sqrt(1-r^2)*randn( n, 1 );
-    covareigen( x, y, 2, 5.0, 3.0 );
+    covareigen( x, y, 0, 5.0, 3.0 );
     key = waitforbuttonpress;
 end
 return
@@ -29,7 +29,7 @@ for d = 0:1:5
     x = [ x1; x2 ];
     y = [ y1+d2; y2-d2 ];
     scrsz = get(0,'ScreenSize');
-    covareigen( x, y, 1, 10.0, 7.0 );
+    covareigen( x, y, 0, 10.0, 7.0 );
     %key = waitforbuttonpress;
     pause( 1.0 );
 end
diff --git a/linearalgebra/code/extdata.mat b/linearalgebra/code/extdata.mat
new file mode 100644
index 0000000..619c0c1
Binary files /dev/null and b/linearalgebra/code/extdata.mat differ
diff --git a/linearalgebra/code/extracellularrecording.m b/linearalgebra/code/extracellularrecording.m
new file mode 100644
index 0000000..8488cd1
--- /dev/null
+++ b/linearalgebra/code/extracellularrecording.m
@@ -0,0 +1,54 @@
+% this script simulates extracellularly recorded 
+% spike waveforms and saves them in extdata.mat.
+
+% generate spikes:
+n = 1000;
+misi = 0.01;
+isis = exprnd( misi, n, 1 );
+isis = isis + 0.01;
+spikes = cumsum( isis );
+p = rand( size( spikes ) );
+
+% spike waveforms:
+dt = 0.0001;
+waveformt = -0.01:dt:0.01;
+waveform1 = 2.0*exp( -(waveformt-0.0003).^2/2.0/0.0005^2 ) - 1.4*exp( -(waveformt-0.0005).^2/2.0/0.0005^2 );
+waveform2 = exp( -waveformt.^2/2.0/0.002^2 ).*cos(2.0*pi*waveformt/0.005);
+p12 = 0.5;
+
+% voltage trace:
+noise = 0.2;
+time = 0:dt:spikes(end)+3.0*waveformt(end);
+voltage = noise*randn( 1, length( time ) );
+for k = 1:length( spikes )
+    inx = ceil( spikes(k)/dt );
+    if p(k) < p12
+        voltage(inx:inx+length(waveformt)-1) = voltage(inx:inx+length(waveformt)-1) + waveform1; 
+    else
+        voltage(inx:inx+length(waveformt)-1) = voltage(inx:inx+length(waveformt)-1) + waveform2; 
+    end
+end
+
+figure( 1 );
+clf;
+plot( time(time<1.0), voltage(time<1.0) );
+hold on;
+
+% find peaks:
+thresh = 0.7;
+inx = find( voltage(2:end-1) > thresh & voltage(1:end-2)<voltage(2:end-1) & voltage(2:end-1) > voltage(3:end) ) + 1;
+spiketimes = time(inx);
+spikevoltage = voltage(inx);
+tinx = inx;
+for k=1:2
+    inx = find( ( spiketimes(2:end-1)-spiketimes(1:end-2)>0.005 | spikevoltage(2:end-1) > spikevoltage(1:end-2) ) & ( spiketimes(3:end)-spiketimes(2:end-1)>0.005 | spikevoltage(2:end-1) > spikevoltage(3:end) ) )+1;
+    spiketimes = spiketimes(inx);
+    spikevoltage = spikevoltage(inx);
+    tinx = tinx(inx);
+end
+scatter( spiketimes(spiketimes<1.0), spikevoltage(spiketimes<1.0), 100.0, 'r', 'filled' );
+%scatter( t(tinx), v(tinx), 100.0, 'r', 'filled' );
+hold off;
+
+% save data to file:
+save( 'extdata', 'time', 'voltage', 'spiketimes', 'waveformt', 'waveform1', 'waveform2' );
diff --git a/linearalgebra/code/gaussian.m b/linearalgebra/code/gaussian.m
new file mode 100644
index 0000000..cdac6d5
--- /dev/null
+++ b/linearalgebra/code/gaussian.m
@@ -0,0 +1,25 @@
+% Gaussian density from histogram:
+x = randn( 1000000, 1 );
+[ n, c ] = hist( x, 100 );
+n = n/sum(n)/(c(2)-c(1));
+bar( c, n );
+hold on;
+% equation p(x):
+xx = -5:0.01:5;
+p=exp(-xx.^2/2.0)/sqrt(2.0*pi);
+plot( xx, p, 'r', 'LineWidth', 3 )
+
+% with mean=2 and sigma=0.5:
+mu = 2.0;
+sig = 0.5;
+x = sig*x + mu;
+[ n, c ] = hist( x, 100 );
+n = n/sum(n)/(c(2)-c(1));
+bar( c, n );
+% equation:
+p=exp(-(xx-mu).^2/2.0/sig^2)/sqrt(2.0*pi*sig^2);
+plot( xx, p, 'r', 'LineWidth', 3 )
+hold off;
+xlabel( 'x' );
+ylabel( 'p(x)' );
+title( 'Gaussian distribution' );
diff --git a/linearalgebra/code/matrix2dtrafos.m b/linearalgebra/code/matrix2dtrafos.m
new file mode 100644
index 0000000..2bd7546
--- /dev/null
+++ b/linearalgebra/code/matrix2dtrafos.m
@@ -0,0 +1,21 @@
+% Visualize some common matrix transformations
+% in a 2D coordinate system
+
+disp( 'Click into the plot to advance to the next matrix' )
+matrixbox( [ 1 0; 0 1], 'Identity' );
+matrixbox( [ 2 0; 0 1], 'Scale x' );
+matrixbox( [ 1 0; 0 2], 'Scale y' );
+matrixbox( [ 2 0; 0 2], 'Scale x and y' );
+matrixbox( [ 0.5 0; 0 0.5], 'Scale x and y' );
+matrixbox( [ -1 0; 0 1], 'Flip x' );
+matrixbox( [ 1 0; 0 -1], 'Flip y' );
+matrixbox( [ -1 0; 0 -1], 'Flip both' );
+matrixbox( [ 1 2; 0 1], 'Shear x' );
+matrixbox( [ 1 0; 2 1], 'Shear y' );
+matrixbox( [ 0.5 1; 1 0.5], 'Something A' );
+matrixbox( [ 0.5 1; 1.2 0.6], 'Something B' );
+% rotation matrices:
+for deg = 0:10:360
+    phi = deg*pi/180.0;
+    matrixbox( [ cos(phi) -sin(phi); sin(phi) cos(phi)], sprintf( 'Rotate %.0f', deg ) );
+end
diff --git a/linearalgebra/code/matrixbox.m b/linearalgebra/code/matrixbox.m
new file mode 100644
index 0000000..6cc0aa1
--- /dev/null
+++ b/linearalgebra/code/matrixbox.m
@@ -0,0 +1,45 @@
+function matrixbox( m, s )
+% visualizes the effect of a matrix m on a set of vectors forming a box
+% m: a 2x2 matrix
+% s: a string plotted as the title of the plot
+% this function is called by matrix2dtrafos.m
+
+    % the 5 vectors that point into the cornes of a unit square:
+    v = [ 0 1 1 0 0; 
+          0 0 1 1 0 ];
+    % transform v by means of m into new vector w:
+    w = m*v;
+    clf;
+    hold on;
+    % axis:
+    plot( [-2 2], [0 0], 'k', 'LineWidth', 1 );
+    plot( [0 0], [-2 2], 'k', 'LineWidth', 1 );
+    % old vectors:
+    plot( v(1,:), v(2,:), 'k', 'LineWidth', 1.0 )
+    quiver( v(1,1), v(2,1), v(1,3), v(2,3), 1.0, 'k', 'LineWidth', 2.0 );
+    scatter( v(1,2), v(2,2), 60.0, 'filled', 'k' );
+    % transformed vectors:
+    plot( w(1,:), w(2,:), 'b', 'LineWidth', 2.0 )
+    quiver( w(1,1), w(2,1), w(1,3), w(2,3), 1.0, 'b', 'LineWidth', 3.0 );
+    scatter( w(1,2), w(2,2), 100.0, 'filled', 'b' );
+    % eigenvectors:
+    [ev ed] = eig(m);
+    n = ev*ed;
+    if isreal( n )
+        quiver( [0 0], [0 0], n(1,:), n(2,:), 1.0, 'r', 'LineWidth', 2.0 );
+        text( 0.1, 0.2, sprintf( '\\lambda_1 = %.3g', ed(1,1) ), 'Units', 'normalized' )
+        text( 0.1, 0.1, sprintf( '\\lambda_2 = %.3g', ed(2,2) ), 'Units', 'normalized' )
+    end
+    hold off;
+    xlim( [ -2 2 ] );
+    ylim( [ -2 2 ] );
+    axis( 'equal' );
+    m( abs(m) < 1e-3 ) = 0.0;  % make zeros really a zero
+    text( 0.7, 0.15, 'A =', 'Units', 'normalized' )
+    text( 0.8, 0.2, sprintf( '%.3g', m(1,1) ), 'Units', 'normalized' )
+    text( 0.9, 0.2, sprintf( '%.3g', m(1,2) ), 'Units', 'normalized' )
+    text( 0.8, 0.1, sprintf( '%.3g', m(2,1) ), 'Units', 'normalized' )
+    text( 0.9, 0.1, sprintf( '%.3g', m(2,2) ), 'Units', 'normalized' )
+    title( s );
+    waitforbuttonpress;
+end
diff --git a/linearalgebra/code/matrixmultiplicationlatex.m b/linearalgebra/code/matrixmultiplicationlatex.m
new file mode 100644
index 0000000..dbb66de
--- /dev/null
+++ b/linearalgebra/code/matrixmultiplicationlatex.m
@@ -0,0 +1,85 @@
+% open a file to write in the LaTeX commands:
+f = fopen( 'matrices.tex','w');
+fprintf( f, '\\documentclass{exam}\n' );
+fprintf( f, '\\usepackage{amsmath}\n' );
+fprintf( f, '\\begin{document}\n' );
+
+for k = 1:40
+
+    % compute row and column numbers:
+    pd = rand( 1, 1 );
+    if pd < 0.05
+        % row vector multiplication that might not be possible:
+        am = 1;
+        an = randi( [ 2 4 ]);
+        bm = randi( [ 2 4 ]);
+        bn = 1;
+    elseif pd < 0.1
+        % column vector multiplication that might not be possible:
+        am = randi( [ 2 4 ]);
+        an = 1;
+        bm = 1;
+        bn = randi( [ 2 4 ]);
+    elseif pd < 0.4
+        % row vector multiplication that is possible:
+        am = 1;
+        an = randi( [ 2 4 ]);
+        bm = an;
+        bn = 1;
+    elseif pd < 0.5
+        % column vector multiplication that is possible:
+        am = randi( [ 2 4 ]);
+        an = 1;
+        bm = 1;
+        bn = am;
+    elseif pd < 0.6
+        % matrix multiplication that might not be possible:
+        am = randi( [ 2 4 ]);
+        an = randi( [ 2 4 ]);
+        bm = randi( [ 2 4 ]);
+        bn = randi( [ 2 4 ]);
+    else
+        % matrix multiplication that is possible:
+        am = randi( [ 2 4 ]);
+        an = randi( [ 2 4 ]);
+        bm = an;
+        bn = randi( [ 2 4 ]);
+    end
+    % generate the matrices:
+    a = randi( [-4 4], am, an );
+    b = randi( [-4 4], bm, bn );
+    % write them out as LaTeX code:
+    %   matrix a:
+    fprintf( f, '  \\[ \\begin{pmatrix}' );
+    for r = 1:size( a, 1 )
+        for c = 1:size( a, 2 )
+            if c > 1
+                fprintf( f, ' &' );
+            end
+            fprintf( f, ' %d', a(r,c) );
+        end
+        if r < size( a, 1 )
+            fprintf( f, ' \\\\' );
+        end
+    end
+    fprintf( f, ' \\end{pmatrix} \\cdot\n' );
+    %   matrix b:
+    fprintf( f, ' \\begin{pmatrix}' );
+    for r = 1:size( b, 1 )
+        for c = 1:size( b, 2 )
+            if c > 1
+                fprintf( f, ' &' );
+            end
+            fprintf( f, ' %d', b(r,c) );
+        end
+        if r < size( b, 1 )
+            fprintf( f, ' \\\\' );
+        end
+    end
+    fprintf( f, ' \\end{pmatrix} = \\]\n\n' );
+
+end
+
+% close the document and the file:
+fprintf( f, '\\end{document}\n' );
+fclose( f );
diff --git a/linearalgebra/code/p-unit_spike_times.mat b/linearalgebra/code/p-unit_spike_times.mat
new file mode 100644
index 0000000..75f53e6
Binary files /dev/null and b/linearalgebra/code/p-unit_spike_times.mat differ
diff --git a/linearalgebra/code/p-unit_stimulus.mat b/linearalgebra/code/p-unit_stimulus.mat
new file mode 100644
index 0000000..b6d0b25
Binary files /dev/null and b/linearalgebra/code/p-unit_stimulus.mat differ
diff --git a/linearalgebra/code/pca2d.m b/linearalgebra/code/pca2d.m
new file mode 100644
index 0000000..6879f8a
--- /dev/null
+++ b/linearalgebra/code/pca2d.m
@@ -0,0 +1,95 @@
+function pca2d( x, y, pm, w, h )
+% computes covariance matrix from the pairs x, y
+% diagonalizes covariance matrix, i.e. performs a PCA on [ x, y ]
+% x and y are column vectors
+% pm: 0 - scatter of data, 1 - histogram, 2 - multivariate gauss from
+% covariance matrix, 1 and 2 require w and h
+% w and h are the width and the height (in data units) for plotting
+% histograms instead of scatter
+
+    % covariance matrix:
+    cv = cov( [ x y ] );
+
+    % eigen values:
+    [ v , d] = eig( cv )
+    s = sign( v );
+    s(1,:) = s(2,:);
+    v = v .* s;
+
+    % plots:
+    subplot( 2, 2, 1 );
+    hold on;
+
+    if (pm > 0) & (nargin == 5)
+        if pm == 1
+            % histogram of data:
+            xp = -w:0.5:w;
+            yp = -h:0.5:h;
+            [n,c] = hist3([x y], { xp, yp } );
+            contourf( c{1}, c{2}, n' );
+        else
+            % bivariate Gaussian:
+            xp = -w:0.1:w;
+            yp = -h:0.1:h;
+            [xg,yg] = meshgrid( xp, yp );
+            xy = [ xg(:) yg(:) ];
+            gauss = reshape( exp(-0.5*diag(xy*inv(cv)*xy'))/sqrt((2.0*pi)^2.0*det(cv)), size( xg ) );
+            contourf( xp, yp, gauss )
+        end
+        colormap( 'gray' );
+    else
+        % scatter plot:
+        scatter( x, y, 'b', 'filled', 'MarkerEdgeColor', 'white' );
+    end
+
+    % plot eigenvectors:
+    quiver( ones( 1, 2 ).*mean( x ), ones( 1, 2 )*mean(y), v(1,:).*sqrt(diag(d))', v(2,:).*sqrt(diag(d))', 'r', 'LineWidth', 3, 'AutoScale', 'off', 'AutoScaleFactor', 1.0, 'MaxHeadSize', 0.7 )
+
+    xlabel( 'x' );
+    ylabel( 'y' );
+    axis( 'equal' );
+    hold off;
+
+    % histogram of x values:
+    subplot( 2, 2, 3 );
+    hist( x, 50, 'b' );
+    xlabel( 'x' );
+    ylabel( 'count' );
+
+    % sort the eigenvalues:
+    [d,inx] = sort( diag(d), 'descend' );
+
+    subplot( 2, 2, 2 );
+    hold on;
+    % subtract means:
+    x = x - mean( x );
+    y = y - mean( y );
+    % project onto eigenvectors:
+    nc = [ x y ] * v(:,inx);
+    cv = cov( nc )
+    [ v , d] = eig( cv )
+    if (pm > 0) & (nargin == 5)
+        if pm == 1
+            % histogram of data:
+            [n,c] = hist3( nc, { xp, yp } );
+            contourf( c{1}, c{2}, n' );
+        else
+            % bivariate Gaussian:
+            gauss = reshape( exp(-0.5*diag(xy*inv(cv)*xy'))/sqrt((2.0*pi)^2.0*det(cv)), size( xg ) );
+            contourf( xp, yp, gauss )
+        end
+    else
+        % scatter plot:
+        scatter( nc(:,1), nc(:,2), 'b', 'filled', 'MarkerEdgeColor', 'white' );
+    end
+    xlabel( 'n_x' );
+    ylabel( 'n_y' );
+    axis( 'equal' );
+    hold off;
+
+    % histogram of new x values:
+    subplot( 2, 2, 4 );
+    hist( nc(:,1), 50, 'b' );
+    xlabel( 'n_x' );
+    ylabel( 'count' );
+end
diff --git a/linearalgebra/code/pca2dexamples.m b/linearalgebra/code/pca2dexamples.m
new file mode 100644
index 0000000..252ceda
--- /dev/null
+++ b/linearalgebra/code/pca2dexamples.m
@@ -0,0 +1,33 @@
+scrsz = get( 0, 'ScreenSize' );
+set( 0, 'DefaultFigurePosition', [ scrsz(3)/2 scrsz(4)/2 scrsz(3)/2 scrsz(4)/2 ] );
+
+% correlation coefficients:
+n = 10000;
+x = randn( n, 1 );
+f = figure( 1 );
+for r = 0.01:0.19:1
+    fprintf( 'Correlation = %g\n', r );
+    clf( f );
+    y = r*x + sqrt(1-r^2)*randn( n, 1 );
+    pca2d( x, y, 0, 5.0, 3.0 );
+    waitforbuttonpress;
+end
+
+% two distributions:
+n = 10000;
+x1 = randn( n/2, 1 );
+y1 = randn( n/2, 1 );
+x2 = randn( n/2, 1 );
+y2 = randn( n/2, 1 );
+f = figure( 1 );
+pause( 'on' );
+for d = 0:1:5
+    fprintf( 'Distance = %g\n', d );
+    clf( f  );
+    d2 = d / sqrt( 2.0 );
+    x = [ x1; x2 ];
+    y = [ y1+d2; y2-d2 ];
+    scrsz = get(0,'ScreenSize');
+    pca2d( x, y, 0, 10.0, 7.0 );
+    waitforbuttonpress;
+end
diff --git a/linearalgebra/code/pca3d.m b/linearalgebra/code/pca3d.m
new file mode 100644
index 0000000..6f2a6bd
--- /dev/null
+++ b/linearalgebra/code/pca3d.m
@@ -0,0 +1,60 @@
+function pca3d( x, y, z )
+% computes covariance matrix from the triples x, y, z
+% diagonalizes covariance matrix, i.e. performs pca on [ x, y, z ]
+% x, y and z are column vectors
+
+    % covariance matrix:
+    cv = cov( [ x y z ] );
+
+    % eigen values:
+    [ v , d] = eig( cv )
+
+    % plots:
+    subplot( 1, 2, 1 );
+    hold on;
+
+    % scatter plot:
+    view( 3 );
+    scatter3( x, y, z, 0.1, 'b', 'filled', 'MarkerEdgeColor', 'blue' );
+    xlabel( 'x' );
+    ylabel( 'y' );
+    zlabel( 'z' );
+    grid on;
+    
+    % plot eigenvectors:
+    quiver3( ones( 1, 3 ).*mean( x ), ones( 1, 3 )*mean(y), ones( 1, 3 )*mean(z), v(1,:).*sqrt(diag(d))', v(2,:).*sqrt(diag(d))', v(3,:).*sqrt(diag(d))', 'r', 'LineWidth', 3, 'AutoScale', 'off', 'AutoScaleFactor', 1.0, 'MaxHeadSize', 0.7 )
+    
+    %axis( 'equal' );
+    hold off;
+
+    % 2D scatter plots:
+    subplot( 2, 6, 4 );
+    scatter( x, y, 'b', 'filled', 'MarkerEdgeColor', 'white' );
+    xlabel( 'x' )
+    ylabel( 'y' )
+    subplot( 2, 6, 5 );
+    scatter( x, z, 'b', 'filled', 'MarkerEdgeColor', 'white' );
+    xlabel( 'x' )
+    ylabel( 'z' )
+    subplot( 2, 6, 6 );
+    scatter( y, z, 'b', 'filled', 'MarkerEdgeColor', 'white' );
+    xlabel( 'y' )
+    ylabel( 'z' )
+
+    % sort the eigenvalues:
+    [d,inx] = sort( diag(d), 'descend' );
+    
+    subplot( 2, 2, 4 );
+    hold on;
+    % subtract means:
+    x = x - mean( x );
+    y = y - mean( y );
+    % project onto eigenvectors:
+    nx = [ x y z ] * v(:,inx);
+    scatter( nx(:,1), nx(:,2), 'b', 'filled', 'MarkerEdgeColor', 'white' );
+    xlabel( 'ex' )
+    ylabel( 'ey' )
+    axis( 'equal' );
+    hold off;
+
+end
diff --git a/linearalgebra/code/pca3dexamples.m b/linearalgebra/code/pca3dexamples.m
new file mode 100644
index 0000000..d2d790f
--- /dev/null
+++ b/linearalgebra/code/pca3dexamples.m
@@ -0,0 +1,19 @@
+scrsz = get( 0, 'ScreenSize' );
+set( 0, 'DefaultFigurePosition', [ scrsz(3)/2 scrsz(4)/2 scrsz(3)/2 scrsz(4)/2 ] );
+
+n = 10000;
+
+% three distributions:
+x = randn( n, 1 );
+y = randn( n, 1 );
+z = randn( n, 1 );
+dx = [ 0 8 0 0 ];
+dy = [ 0 0 8 0 ];
+dz = [ 0 0 0 8 ];
+for k = 1:4
+    x((k-1)*n/4+1:k*n/4) = x((k-1)*n/4+1:k*n/4) + dx(k);
+    y((k-1)*n/4+1:k*n/4) = y((k-1)*n/4+1:k*n/4) + dy(k);
+    z((k-1)*n/4+1:k*n/4) = z((k-1)*n/4+1:k*n/4) + dz(k);
+end
+f = figure( 1 );
+pca3d( x, y, z );
diff --git a/linearalgebra/simulations/plotvector.m b/linearalgebra/code/plotvector.m
similarity index 100%
rename from linearalgebra/simulations/plotvector.m
rename to linearalgebra/code/plotvector.m
diff --git a/linearalgebra/simulations/plotvector3.m b/linearalgebra/code/plotvector3.m
similarity index 100%
rename from linearalgebra/simulations/plotvector3.m
rename to linearalgebra/code/plotvector3.m
diff --git a/linearalgebra/code/simplematrix2dtrafos.m b/linearalgebra/code/simplematrix2dtrafos.m
new file mode 100644
index 0000000..0b0cfe4
--- /dev/null
+++ b/linearalgebra/code/simplematrix2dtrafos.m
@@ -0,0 +1,21 @@
+% Visualize some common matrix transformations
+% in a 2D coordinate system
+% this uses the simplematrixbox() function for visualization
+% use the matrix2dtrafos script for the the nicer matrixbox() function.
+
+disp( 'Click into the plot to advance to the next matrix' )
+simplematrixbox( [ 1 0; 0 1], 'Identity' );
+simplematrixbox( [ 2 0; 0 1], 'Scale x' );
+simplematrixbox( [ 1 0; 0 2], 'Scale y' );
+simplematrixbox( [ 2 0; 0 2], 'Scale x and y' );
+simplematrixbox( [ 0.5 0; 0 0.5], 'Scale x and y' );
+simplematrixbox( [ -1 0; 0 1], 'Flip x' );
+simplematrixbox( [ 1 0; 0 -1], 'Flip y' );
+simplematrixbox( [ -1 0; 0 -1], 'Flip both' );
+simplematrixbox( [ 1 1; 0 1], 'Shear x' );
+simplematrixbox( [ 1 0; 1 1], 'Shear y' );
+% rotation matrices:
+for deg = 0:10:360
+    phi = deg*pi/180.0;
+    simplematrixbox( [ cos(phi) -sin(phi); sin(phi) cos(phi)], sprintf( 'Rotate %.0f', deg ) );
+end
diff --git a/linearalgebra/code/simplematrixbox.m b/linearalgebra/code/simplematrixbox.m
new file mode 100644
index 0000000..bb94254
--- /dev/null
+++ b/linearalgebra/code/simplematrixbox.m
@@ -0,0 +1,21 @@
+function simplematrixbox( a, s )
+% visualizes the effect of a matrix a on a set of vectors forming a box
+% a: a 2x2 matrix
+% s: a string plotted as the title of the plot
+% this function is called by simplematrix2dtrafos.m
+% this is a simple version of the more elaborate matrixbox() function.
+
+    x = [ 0 1 1 0 0 1; 0 0 1 1 0 1 ];
+    plot( x(1,:), x(2,:), '-b' );
+    hold on;
+    y = a*x;
+    plot( y(1,:), y(2,:), '-r' );
+    [ ev ed ] = eig( a );
+    ev = ev * ed;
+    quiver( [0 0], [0 0], ev(1,:), ev(2,:), 1.0, 'g' )
+    hold off;
+    xlim( [-2 2 ] )
+    ylim( [-2 2] )
+    title( s );
+    waitforbuttonpress;
+end
diff --git a/linearalgebra/code/soundmixture.m b/linearalgebra/code/soundmixture.m
new file mode 100644
index 0000000..7cef0c8
--- /dev/null
+++ b/linearalgebra/code/soundmixture.m
@@ -0,0 +1,72 @@
+% read in wav files:
+[s1, fs1 ] = audioread( 'PeterUndDerWolf.wav' );
+[s2, fs2 ] = audioread( 'Tielli-Kraulis.wav' );
+[s3, fs3 ] = audioread( 'Chorthippus_biguttulus.wav' );
+
+% take out left channel and  minimum number of samples:
+n = min( [ size( s1, 1 ), size( s2, 1 ), size( s3, 1 ) ] );
+%n = 300000;
+x1 = s1(1:n,1)';
+x2 = s2(1:n,1)';
+x3 = s3(1:n,1)';
+clear s1 s2 s3;
+
+% plot the sound waves:
+% dt = 1/fs1;
+% time = 0:dt:(length(x1)-1)*dt;
+% plot( time, x1 );
+% plot( time, x2 );
+% plot( time, x3 );
+
+% play original sounds:
+% disp( 'Playing x1 (Peter und der Wolf)' )
+% a = audioplayer( x1, fs1 );
+% playblocking( a );
+% disp( 'Playing x2 (Martin Tielli)' )
+% a = audioplayer( x2, fs2 );
+% playblocking( a );
+% disp( 'Playing x3 (Grasshopper song)' )
+% a = audioplayer( x3, fs3 );
+% playblocking( a );
+
+% mix them:
+m = [ 1.0 0.3 0.4; 0.1 0.6 0.4; 0.7 0.1 0.7 ];
+y = m*[ x1; x2; x3 ];
+
+% add noise:
+%y = y + 0.03*randn(size(y));
+
+% play mixtures:
+% disp( 'Playing mixture y1' )
+% a = audioplayer( y(1,:), fs1 );
+% playblocking( a );
+% disp( 'Playing mixture y2' )
+% a = audioplayer( y(2,:), fs2 );
+% playblocking( a );
+% disp( 'Playing mixture y3' )
+% a = audioplayer( y(3,:), fs3 );
+% playblocking( a );
+
+% demix them:
+disp( 'Inverse mixing matrix:' )
+dm = inv(m)
+dx = dm*y;
+
+% estimate demixing matrix:
+size( y )
+cv = cov( y' );
+[ ev ed ] = eig( cv );
+disp( 'Estimated demixing matrix:' )
+ev
+
+%play mixtures:
+% disp( 'Playing x1 (Peter und der Wolf) recovered from mixtures' )
+% a = audioplayer( dx(1,:), fs1 );
+% playblocking( a );
+% disp( 'Playing x3 (Martin Tielli) recovered from mixtures' )
+% a = audioplayer( dx(2,:), fs2 );
+% playblocking( a );
+% disp( 'Playing x3 (Grasshopper song) recovered from mixtures' )
+% a = audioplayer( dx(3,:), fs3 );
+% playblocking( a );
+
diff --git a/linearalgebra/code/spikesortingwave.m b/linearalgebra/code/spikesortingwave.m
new file mode 100644
index 0000000..da7c251
--- /dev/null
+++ b/linearalgebra/code/spikesortingwave.m
@@ -0,0 +1,118 @@
+% load data into time, voltage and spiketimes
+load( 'extdata' );
+
+% indices into voltage trace of spike times:
+dt = time( 2) - time(1);
+tinx = round(spiketimes/dt)+1;
+
+% plot voltage trace with dettected spikes:
+figure( 1 );
+clf;
+plot( time, voltage, '-b' )
+hold on
+scatter( time(tinx), voltage(tinx), 'r', 'filled' );
+xlabel( 'time [ms]' );
+ylabel( 'voltage' );
+hold off
+
+% spike waveform snippets:
+w = ceil( 0.005/dt );
+vs = [];
+for k=1:length(tinx)
+    vs = [ vs; voltage(tinx(k)-w:tinx(k)+w-1) ];
+end
+ts = time(1:size(vs,2));
+ts = ts - ts(floor(length(ts)/2));
+
+% % plot snippets:
+figure( 2 );
+clf;
+hold on
+for k=1:size(vs,1)
+    plot( ts, vs(k,:), '-b' );
+end
+xlabel( 'time [ms]' );
+ylabel( 'voltage' );
+hold off
+
+% pca:
+cv = cov( vs );
+[ ev , ed ] = eig( cv );
+[d,dinx] = sort( diag(ed), 'descend' );
+
+figure( 3 );
+clf;
+subplot( 4, 2, 1 );
+imagesc( cv );
+xlabel( 'time bin' );
+ylabel( 'time bin' );
+title( 'covariance matrix' );
+caxis([-0.1 0.1])
+
+% spectrum of eigenvalues:
+subplot( 4, 2, 2 );
+scatter( 1:length(d), d, 'b', 'filled' );
+xlabel( 'index' );
+ylabel( 'eigenvalue' );
+
+% features:
+subplot( 4, 2, 5 );
+plot( 1000.0*ts, ev(:,dinx(1)), 'r', 'LineWidth', 2 );
+xlabel( 'time [ms]' );
+ylabel( 'eigenvector 1' );
+subplot( 4, 2, 6 );
+plot( 1000.0*ts, ev(:,dinx(2)), 'g', 'LineWidth', 2 );
+xlabel( 'time [ms]' );
+ylabel( 'eigenvector 2' );
+
+% project onto eigenvectors:
+nx = vs * ev(:,dinx(1));
+ny = vs * ev(:,dinx(2));
+%scatter( nx, ny, 'b', 'filled', 'MarkerEdgeColor', 'white' );
+
+% clustering (two clusters):
+%kx = kmeans( [ nx, ny ], 2 );
+% nx smaller or greater a threshold:
+kthresh = 1.6;
+kx = ones( size( nx ) );
+kx(nx<kthresh) = 2;
+
+subplot( 4, 1, 2 );
+scatter( nx(kx==1), ny(kx==1), 'r', 'filled', 'MarkerEdgeColor', 'white' );
+hold on;
+scatter( nx(kx==2), ny(kx==2), 'g', 'filled', 'MarkerEdgeColor', 'white' );
+hold off;
+xlabel( 'projection onto eigenvector 1' );
+ylabel( 'projection onto eigenvector 2' );
+
+% show sorted spike waveforms:
+subplot( 4, 2, 7 );
+hold on
+kinx1 = find(kx==1);
+for k=1:length(kinx1)
+    plot( 1000.0*ts, vs(kinx1(k),:), '-r' );
+end
+plot( 1000.0*waveformt, waveform2, '-k', 'LineWidth', 2 );
+xlim( [ 1000.0*ts(1) 1000.0*ts(end) ] )
+xlabel( 'time [ms]' );
+ylabel( 'waveform 1' );
+hold off
+
+subplot( 4, 2, 8 );
+hold on
+kinx2 = find(kx==2);
+for k=1:length(kinx2)
+    plot( 1000.0*ts, vs(kinx2(k),:), '-g' );
+end
+plot( 1000.0*waveformt, waveform1, '-k', 'LineWidth', 2 );
+xlim( [ 1000.0*ts(1) 1000.0*ts(end) ] )
+xlabel( 'time [ms]' );
+ylabel( 'waveform 2' );
+hold off
+
+% spike trains:
+figure( 1 );
+hold on;
+scatter( time(tinx(kinx1)), voltage(tinx(kinx1)), 100.0, 'r', 'filled' );
+scatter( time(tinx(kinx2)), voltage(tinx(kinx2)), 100.0, 'g', 'filled' );
+hold off;
diff --git a/linearalgebra/simulations/spiketime.m b/linearalgebra/code/spiketime.m
similarity index 100%
rename from linearalgebra/simulations/spiketime.m
rename to linearalgebra/code/spiketime.m
diff --git a/linearalgebra/code/sta.m b/linearalgebra/code/sta.m
new file mode 100644
index 0000000..1741a5a
--- /dev/null
+++ b/linearalgebra/code/sta.m
@@ -0,0 +1,227 @@
+function [ stavg, stavgtime, spikesnippets, stimsnippets, meanrate ] = sta( stimulus, spikes, left, right )
+% computes the spike-triggered average
+% stimulus: the stimulus as a nx2 matrix with the first column being time
+% in seconds and the second column being the actual stimulus
+% spikes: a cell array of vectors of spike times
+% left: the time to the left of each spike
+% right: the time to the right of each spike
+% returns
+% stavg: the spike-triggered average
+% stavgtime: the corresponding time axis
+% spikesnippets: the spike-triggered waveforms as a nspikes x stavgtimes
+% matrix
+% meanrate: the mean firing rate
+
+    % time indices:
+    dt = stimulus(2,1) - stimulus(1,1);
+    wl = round( left/dt );
+    wr = round( right/dt );
+    nw = wl+wr+1;
+    
+    % total number of spikes:
+    nspikes = 0;
+    for k = 1:length( spikes )
+        nspikes = nspikes + length( spikes{k} );
+    end
+    
+    % loop over trials:
+    spikesnippets = zeros( nspikes, nw );
+    nspikes = 0;
+    for k = 1:length( spikes )
+        times = spikes{k};
+        for j = 1:length(times)
+            % index of spike in stimulus:
+            inx = round(times(j)/dt);
+            if ( inx-wl > 0 ) & ( inx+wr <= size( stimulus, 1 ) ) 
+                nspikes = nspikes + 1;
+                snip = stimulus( inx-wl:inx+wr, 2 );
+                spikesnippets( nspikes, : ) = snip; % - mean(snip);
+            end
+        end
+    end
+    % delete not used snippets:
+    spikesnippets(nspikes+1:end,:) = [];
+    stavgtime = [-left:dt:right];
+    meanrate = nspikes/length(spikes)/(stimulus(end,1)-stimulus(1,1));
+
+    % spike-triggered average:
+    stavg = mean( spikesnippets, 1 );
+
+    % loop over stimulus:
+    nstim = size( stimulus, 1 )-nw+1;
+    stimsnippets = zeros( nstim, nw );
+    stimsnippetstime = zeros( nstim, 1 );
+    for j=1:nstim
+        snip = stimulus( j:j+wr+wl, 2 );
+        stimsnippets(j,:) = snip; % - mean(snip);
+        stimsnippetstime(j) = j*dt;
+    end
+
+    % projection onto sta:
+    spikeonsta = spikesnippets * stavg';
+    stimonsta = stimsnippets * stavg';
+
+    % projection onto orthogonal to sta:
+    orthos = null( stavg );
+    mixcoef = rand(size( orthos, 2 ), 1);
+    mixcoef(length(mixcoef)/10:end) = 0.0;
+    staortho = orthos*mixcoef;
+    % stavg*staortho
+    staortho = staortho/(staortho'*staortho);
+    spikeonstaortho = spikesnippets * staortho;
+    stimonstaortho = stimsnippets * staortho;
+    
+    % psth binned:
+    ratetime = 0:dt:stimulus(end,1);
+%     rate = zeros( size( ratetime ) );
+%     for k = 1:length( spikes )
+%         [n, ~] = hist( spikes{k}, ratetime );
+%         rate = rate + n/dt/length( spikes );
+%     end
+    % kernel psth:
+    kernelsigma = 0.001;
+    windowtime = -5.0*kernelsigma:dt:5.0*kernelsigma;
+    window = normpdf( windowtime, 0.0, kernelsigma )/length(spikes);
+%     plot( 1000.0*windowtime, window );
+    w2 = floor( length( windowtime )/2 );
+    kernelrate = zeros( size( ratetime ) );
+    for k = 1:length( spikes )
+        times = spikes{k};
+        for j = 1:length(times)
+            % index of spike in rate:
+            inx = round(times(j)/dt);
+            if ( inx - w2 > 0 ) & ( inx + w2 < length( kernelrate ) )
+                kernelrate(inx-w2:inx+w2) = kernelrate(inx-w2:inx+w2) + window;
+            end
+        end
+    end
+
+    if nargout == 0
+        % sta plot:
+        figure( 1 );
+        subplot( 3, 2, 1 )
+        plot( 1000.0*stavgtime, staortho, '-r', 'LineWidth', 3 )
+        hold on;
+        plot( 1000.0*stavgtime, stavg, '-b', 'LineWidth', 3 )
+        hold off;
+        xlabel( 'time [ms]' );
+        ylabel( 'stimulus' );
+        title( 'Spike-triggered average' );
+        legend( 'ortho', 'STA' );
+
+        % 2D scatter of projections:
+        subplot( 3, 2, 2 )
+        scatter( stimonsta, stimonstaortho, 40.0, 'b', 'filled', 'MarkerEdgeColor', 'white' )
+        hold on;
+        scatter( spikeonsta, spikeonstaortho, 20.0, 'r', 'filled', 'MarkerEdgeColor', 'white', 'LineWidth', 1.0 )
+        hold off;
+        xlabel( 'projection sta' );
+        ylabel( 'projection ortho' );
+        title( 'Projections' );
+
+        % histogram of projections onto sta:
+        subplot( 3, 2, 3 )
+        [ n, projections ] = hist( stimonsta, 50 );
+        bw = (projections(2)-projections(1));
+        pstim = n / sum(n)/bw;
+        bar( projections, pstim, 'b' );
+        hold on;
+        [ n, ~ ] = hist( spikeonsta, projections );
+        pstimspike = n / sum(n)/bw;
+        bar( projections, pstimspike, 'r' );
+        xlabel( 'projection x' );
+        title( 'Projection onto STA' );
+        xlim( [projections(1) projections(end)] );
+        hold off;
+        legend( 'p(x)', 'p(x|spikes)' );
+
+        % nonlinearity for orthogonal projections:
+        subplot( 3, 2, 5 )
+        nonlinearity = meanrate*pstimspike./pstim;
+        plot( projections(pstim>0.01), nonlinearity(pstim>0.01), '-r', 'LineWidth', 3 );
+        xlim( [projections(1) projections(end)] );
+        ylim( [0 1000 ])
+        xlabel( 'projection x' );
+        ylabel( 'meanrate*p(x|spikes)/p(x) [Hz]' );
+        title( 'Nonlinearity');
+
+        % histogram of projections onto orthogonal sta:
+        subplot( 3, 2, 4 )
+        [ n, ~] = hist( stimonstaortho, projections );
+        pstimortho = n / sum(n)/bw;
+        bar( projections, pstimortho, 'b' );
+        hold on;
+        [ n, ~ ] = hist( spikeonstaortho, projections );
+        pstimspikeortho = n / sum(n)/bw;
+        bar( projections, pstimspikeortho, 'r' );
+        xlim( [projections(1) projections(end)] );
+        xlabel( 'projection x' );
+        title( 'Projection onto orthogonal to STA' );
+        hold off;
+        legend( 'p(x)', 'p(x|spikes)' );
+
+        % nonlinearity for orthogonal projections:
+        subplot( 3, 2, 6 )
+        nonlinearityortho = meanrate*pstimspikeortho./pstimortho;
+        plot( projections(pstimortho>0.01), nonlinearityortho(pstimortho>0.01), '-r', 'LineWidth', 3 );
+        xlim( [projections(1) projections(end)] );
+        ylim( [0 1000 ])
+        xlabel( 'projection x' );
+        ylabel( 'meanrate*p(x|spikes)/p(x) [Hz]' );
+        title( 'Nonlinearity');
+    end
+  
+    % stimulus reconstruction with STA:
+    stareconstruction = zeros( size( stimulus, 1 ), 1 );
+    for k = 1:length( spikes )
+        times = spikes{k};
+        for j = 1:length(times)
+            % index of spike in stimulus:
+            inx = round(times(j)/dt);
+            if ( inx-wl > 0 ) & ( inx+wr <= size( stimulus, 1 ) ) 
+                stareconstruction( inx-wl:inx+wr ) = stareconstruction( inx-wl:inx+wr ) + stavg';
+            end
+        end
+    end
+    stareconstruction = stareconstruction/length(spikes);
+    
+    if nargout == 0
+        % linear stimulus reconstruction:
+        figure( 2 )
+        ax1 = subplot( 3, 1, 1 );
+        plot( 1000.0*stimulus(:,1), stimulus(:,2), 'g', 'LineWidth', 2 );
+        hold on;
+        plot( 1000.0*stimulus(:,1), stareconstruction, 'r', 'LineWidth', 2 );
+        ylabel( 'stimulus' );
+        hold off;
+        legend( 'stimulus', 'reconstruction' );
+
+        % stimulus projection onto sta:
+        ax2 = subplot( 3, 1, 2 );
+    %    plot( 1000.0*ratetime, rate, '-b', 'LineWidth', 2 )
+        plot( 1000.0*ratetime, kernelrate-mean(kernelrate), '-b', 'LineWidth', 2 )
+        hold on;
+        plot( 1000.0*(stimsnippetstime+left), 2.0*stimonsta*meanrate, 'r', 'LineWidth', 2 );
+        ylabel( 'rate [Hz]' );
+        hold off;
+        legend( 'rate', 'stimulus projection on STA' );
+
+        % sta with nonlinearity:
+        ax3 = subplot( 3, 1, 3 );
+        plot( 1000.0*ratetime, kernelrate, '-b', 'LineWidth', 2 )
+        hold on;
+        xmax = max(projections(pstim>0.01));
+        stimonstax = stimonsta;
+        stimonstax( stimonstax > xmax ) = xmax;
+        sinx = round((stimonstax-projections(1))/bw);
+        sinx( sinx < 1 ) = 1;
+        stastimulus = nonlinearity( sinx );
+        plot( 1000.0*(stimsnippetstime+left), stastimulus, 'r', 'LineWidth', 2 );
+        legend( 'rate', 'LNP' );
+        xlabel( 'time [ms]' );
+        ylabel( 'rate [Hz]' );
+        hold off;
+        linkaxes( [ax1 ax2 ax3], 'x' );
+    end
+       
+end
diff --git a/linearalgebra/code/stastcexample.m b/linearalgebra/code/stastcexample.m
new file mode 100644
index 0000000..32500fd
--- /dev/null
+++ b/linearalgebra/code/stastcexample.m
@@ -0,0 +1,31 @@
+clear; 
+addpath( '../../pointprocesses/simulations/' );
+
+set( 0, 'DefaultTextFontSize', 22.0 );
+set( 0, 'DefaultAxesFontSize', 22.0 );
+
+% load data
+load( 'p-unit_stimulus.mat' );
+load( 'p-unit_spike_times.mat' );
+%load( 'pyramidal_noise_2014-09-02-af_cutoff_50_contr_5.mat' );
+% load( 'pyramidal_noise_2014-09-02-ab_cutoff_300_contr_5.mat' );
+% for k = 1:length( spike_times )
+%     spike_times{k} = spike_times{k} * 0.001;
+% end
+% stimulus = stimulus(1:20000*5,:);
+
+%dt = stimulus(2,1) - stimulus(1,1);
+%spike_times = lifadaptspikes( 20, 50+100.0*stimulus(:,2), dt, 0.1, 0.04, 10.0 ); 
+%spike_times = lifspikes( 20, 12+30.0*stimulus(:,2), dt, 0.1 ); 
+%spike_times = lifspikes( 20, 20.0+30.0*stimulus(:,2), dt, 0.0001 ); 
+
+%figure( 1 );
+%clf;
+%spikeraster( spike_times );
+%isivec = isis( spike_times );
+%isihist( isivec );
+%isiserialcorr( isivec, 20 );
+%fano( spike_times );
+
+sta( stimulus(1:5:length(stimulus),:), spike_times, 0.05, 0.01 );
+%stc( stimulus(1:5:length(stimulus),:), spike_times, 0.05, 0.01 );
diff --git a/linearalgebra/code/stc.m b/linearalgebra/code/stc.m
new file mode 100644
index 0000000..c787b78
--- /dev/null
+++ b/linearalgebra/code/stc.m
@@ -0,0 +1,66 @@
+function stc( stimulus, spikes, left, right )
+% computes the spike-triggered covariance matrix
+% stimulus: the stimulus as a nx2 matrix with the first column being time
+% in seconds and the second column being the actual stimulus
+% spikes: a cell array of vectors of spike times
+% left: the time to the left of each spike
+% right: the time to the right of each spike
+
+    % time indices:
+    dt = stimulus(2,1) - stimulus(1,1);
+    wl = round( left/dt );
+    wr = round( right/dt );
+    nw = wl+wr+1;
+    
+    % spike-triggered average with snippets:
+    [ stavg, stavgtime, spikesnippets, stimsnippets, meanrate ] = sta( stimulus, spikes, left, right );
+ 
+    % spike-triggered covariance matrix:
+    figure( 3 );
+    subplot( 2, 2, 1 );
+    spikescv = cov( spikesnippets );
+    imagesc( spikescv );
+    caxis([-0.1 0.1])
+    
+    % stimulus covariance matrix:
+    subplot( 2, 2, 2 );
+    stimcv = cov( stimsnippets );
+    imagesc( stimcv );
+    caxis([-0.1 0.1])
+
+    subplot( 2, 1, 2 );
+    imagesc( spikescv-stimcv );
+    caxis([-0.01 0.01])
+
+    % eigenvalues:
+    %[ ev , ed ] = eig( spikescv-stimcv );
+    [ ev , ed ] = eig( spikescv );
+    [d,dinx] = sort( diag(ed), 'descend' );
+
+    % spectrum of eigenvalues:
+    figure( 4 );
+    subplot( 3, 1, 1 );
+   scatter( 1:length(d), d, 'b', 'filled' );
+%     scatter( 1:length(d), d/sum(abs(d)), 'b', 'filled' );
+    xlabel( 'index' );
+    ylabel( 'eigenvalue [% variance]' );
+    
+    % features:
+    subplot( 3, 2, 5 );
+    plot( 1000.0*stavgtime, ev(:,dinx(1)), 'g', 'LineWidth', 2 );
+    xlabel( 'time [ms]' );
+    ylabel( 'eigenvector 1' );
+    subplot( 3, 2, 6 );
+    plot( 1000.0*stavgtime, ev(:,dinx(2)), 'r', 'LineWidth', 2 );
+    xlabel( 'time [ms]' );
+    ylabel( 'eigenvector 2' );
+
+    % project onto eigenvectors:
+    nx = spikesnippets * ev(:,dinx(1));
+    ny = spikesnippets * ev(:,dinx(2));
+    subplot( 3, 1, 2 );
+    scatter( nx, ny, 'b', 'filled', 'MarkerEdgeColor', 'white' );
+    xlabel( 'projection onto eigenvector 1' );
+    ylabel( 'projection onto eigenvector 2' );
+
+end
diff --git a/linearalgebra/exercises/correlations.pdf b/linearalgebra/exercises/correlations.pdf
new file mode 100644
index 0000000..4ca95f8
Binary files /dev/null and b/linearalgebra/exercises/correlations.pdf differ
diff --git a/linearalgebra/exercises/correlations.tex b/linearalgebra/exercises/correlations.tex
new file mode 100644
index 0000000..222e15d
--- /dev/null
+++ b/linearalgebra/exercises/correlations.tex
@@ -0,0 +1,95 @@
+\documentclass[addpoints,10pt]{exam}
+\usepackage{url}
+\usepackage{color}
+\usepackage{hyperref}
+\usepackage{graphicx}
+\usepackage{amsmath}
+
+\pagestyle{headandfoot}
+\runningheadrule
+\firstpageheadrule
+
+\firstpageheader{Scientific Computing}{Principal Component Analysis}{Oct 29, 2014}
+%\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
+\firstpagefooter{}{}{}
+\runningfooter{}{}{}
+\pointsinmargin
+\bracketedpoints
+
+%\printanswers
+\shadedsolutions
+
+\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
+
+%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{listings}
+\lstset{
+ basicstyle=\ttfamily,
+ numbers=left,
+ showstringspaces=false,
+ language=Matlab,
+ breaklines=true,
+ breakautoindent=true,
+ columns=flexible,
+ frame=single,
+ captionpos=t,
+ xleftmargin=2em,
+ xrightmargin=1em,
+ aboveskip=10pt,
+ %title=\lstname,
+ title={\protect\filename@parse{\lstname}\protect\filename@base.\protect\filename@ext}
+ }
+
+
+\begin{document}
+
+\sffamily
+%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
+
+\begin{questions}
+  \question \textbf{Gaussian distribution}
+  \begin{parts}
+    \part Use \texttt{randn} to generate 1000000 normally (zero mean, unit variance) distributed random numbers.
+    \part Plot a properly normalized histogram of these random numbers.
+    \part Compare the histogram with the probability density of the Gaussian distribution
+    \[ p(x) = \frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}} \]
+    where $\mu$ is the mean and $\sigma^2$ is the variance of the Gaussian distribution.
+    \part Generate Gaussian distributed random numbers with mean $\mu=2$ and
+    standard deviation $\sigma=\frac{1}{2}$.
+  \end{parts}
+
+  \question \textbf{Covariance and correlation coefficient}
+  \begin{parts}
+    \part Generate two vectors $x$ and $z$ with Gausian distributed random numbers.
+    \part Compute $y$ as a linear combination of $x$ and $z$ according to
+    \[ y = r \cdot x + \sqrt{1-r^2}\cdot z \]
+    where $r$ is a parameter $-1 \le r \le 1$.
+    What does $r$ do?
+    \part Plot a scatter plot of $y$ versus $x$ for about 10 different values of $r$.
+    What do you observe?
+    \part Also compute the covariance matrix and the correlation
+    coefficient matrix between $x$ and $y$ (functions \texttt{cov} and
+    \texttt{corrcoef}). How do these matrices look like for different
+    values of $r$? How do the values of the matrices change if you generate
+    $x$ and $z$ with larger variances?
+    \part Do the same analysis (Scatter plot, covariance, and correlation coefficient)
+    for \[ y = x^2 + 0.5 \cdot z \]
+    Are $x$ and $y$ really independent?
+  \end{parts}
+
+  \question \textbf{Principal component analysis}
+  \begin{parts}
+    \part Generate pairs $(x,y)$ of Gaussian distributed random numbers such
+    that all $x$ values have zero mean, half of the $y$ values have mean $+d$
+    and the other half mean $-d$, with $d \ge0$.
+    \part Plot scatter plots of the pairs $(x,y)$ for $d=0$, 1, 2, 3, 4 and 5.
+    Also plot a histogram of the $x$ values.
+    \part Apply PCA on the data and plot a histogram of the data projected onto
+    the PCA axis with the largest eigenvalue.
+    What do you observe?
+  \end{parts}
+  
+\end{questions}
+
+
+\end{document}
diff --git a/linearalgebra/exercises/matrices.pdf b/linearalgebra/exercises/matrices.pdf
new file mode 100644
index 0000000..3d38055
Binary files /dev/null and b/linearalgebra/exercises/matrices.pdf differ
diff --git a/linearalgebra/exercises/matrices.tex b/linearalgebra/exercises/matrices.tex
new file mode 100644
index 0000000..4897644
--- /dev/null
+++ b/linearalgebra/exercises/matrices.tex
@@ -0,0 +1,205 @@
+\documentclass[addpoints,10pt]{exam}
+\usepackage{url}
+\usepackage{color}
+\usepackage{hyperref}
+\usepackage{graphicx}
+\usepackage{amsmath}
+
+\pagestyle{headandfoot}
+\runningheadrule
+\firstpageheadrule
+
+\firstpageheader{Scientific Computing}{Matrix multiplication}{Oct 28, 2014}
+%\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
+\firstpagefooter{}{}{}
+\runningfooter{}{}{}
+\pointsinmargin
+\bracketedpoints
+
+%\printanswers
+\shadedsolutions
+
+\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
+
+%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{listings}
+\lstset{
+ basicstyle=\ttfamily,
+ numbers=left,
+ showstringspaces=false,
+ language=Matlab,
+ breaklines=true,
+ breakautoindent=true,
+ columns=flexible,
+ frame=single,
+ captionpos=t,
+ xleftmargin=2em,
+ xrightmargin=1em,
+ aboveskip=10pt,
+ %title=\lstname,
+ title={\protect\filename@parse{\lstname}\protect\filename@base.\protect\filename@ext}
+ }
+
+
+\begin{document}
+
+\sffamily
+%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
+
+\begin{questions}
+  \question \textbf{Matrix multiplication} 
+  Calculate the results of the following matrix multiplications and
+  confirm the result using matlab.
+    \[ \begin{pmatrix} 2 \\ -4 \\ -1 \end{pmatrix} \cdot
+    \begin{pmatrix} 3 & -4 & -4 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 3 & -3 & -1 \end{pmatrix} \cdot
+    \begin{pmatrix} 2 \\ 3 \\ 0 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 4 & -1 & 2 \\ -1 & 3 & 1 \\ 4 & -2 & 1 \\ 4 & -3 & -2 \end{pmatrix} \cdot
+    \begin{pmatrix} -2 & -2 & 0 & -3 \\ 3 & -2 & 1 & 0 \\ 1 & -2 & -4 & 0 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 3 & 1 \\ 1 & 4 \end{pmatrix} \cdot
+    \begin{pmatrix} 0 & -3 & 4 & 1 \\ -2 & -1 & -2 & -3 \\ -3 & 1 & -2 & -3 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 1 & 1 & -4 \end{pmatrix} \cdot
+    \begin{pmatrix} -1 \\ 2 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 3 & 1 & -2 \\ 2 & 1 & 3 \\ 1 & 1 & 2 \end{pmatrix} \cdot
+    \begin{pmatrix} 2 & 2 \\ -3 & 3 \\ -4 & 1 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 3 \\ 2 \end{pmatrix} \cdot
+    \begin{pmatrix} -3 & 2 & -4 & 1 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -1 \\ -4 \\ -1 \end{pmatrix} \cdot
+    \begin{pmatrix} 0 & -4 & 1 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 4 & -2 & -2 & -4 \end{pmatrix} \cdot
+    \begin{pmatrix} 2 \\ 2 \\ 1 \\ -1 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -2 & -3 & -4 \\ 1 & 3 & 2 \\ -4 & -2 & 1 \end{pmatrix} \cdot
+    \begin{pmatrix} 1 & 2 & -2 & 4 \\ 3 & -1 & 1 & -1 \\ -3 & 2 & -1 & 2 \end{pmatrix} = \]
+
+   \[ \begin{pmatrix} 2 & -4 & 4 & 4 \\ -3 & 3 & 2 & 1 \end{pmatrix} \cdot
+    \begin{pmatrix} 0 & 3 & 4 & -2 \\ -4 & -2 & -1 & 0 \\ 1 & 2 & -4 & -4 \\ 3 & 2 & -2 & -4 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 3 & 1 & -2 & -2 \end{pmatrix} \cdot
+    \begin{pmatrix} -4 \\ 3 \\ -2 \\ 4 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -1 & 3 & 4 \end{pmatrix} \cdot
+    \begin{pmatrix} -1 \\ 4 \\ -3 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 1 & -4 & 3 & 3 \end{pmatrix} \cdot
+    \begin{pmatrix} 1 \\ 0 \\ -4 \\ -1 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -4 & -4 & -3 \\ -2 & -2 & 4 \\ -3 & 4 & -3 \end{pmatrix} \cdot
+    \begin{pmatrix} 0 & 3 & -4 & 4 \\ -1 & -2 & -3 & 1 \\ 1 & -2 & 2 & 0 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -3 & 0 & 4 & 1 \\ 0 & 1 & 1 & 4 \end{pmatrix} \cdot
+    \begin{pmatrix} -4 & 3 & 1 & 4 \\ 1 & -4 & 1 & -3 \\ -4 & 0 & -4 & -4 \\ 1 & -2 & 4 & 4 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 4 \\ 3 \\ 4 \\ -2 \end{pmatrix} \cdot
+    \begin{pmatrix} 2 & 4 & 3 & 3 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 1 & 2 & 0 & 3 \end{pmatrix} \cdot
+    \begin{pmatrix} -3 \\ 1 \\ 1 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -4 & 0 & -1 & 3 \\ 0 & -4 & 3 & -3 \end{pmatrix} \cdot
+    \begin{pmatrix} -1 & -4 & -1 \\ 3 & 2 & 0 \\ -2 & 3 & -2 \\ 1 & 2 & -2 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 2 & 0 & 3 \\ 1 & -4 & -1 \\ 3 & 0 & -2 \end{pmatrix} \cdot
+    \begin{pmatrix} 0 & 2 & -1 & -2 \\ -1 & -1 & -3 & 4 \\ 2 & 4 & -4 & 1 \end{pmatrix} = \]
+    \[ \begin{pmatrix} -1 & 4 \end{pmatrix} \cdot
+    \begin{pmatrix} 0 \\ 1 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -4 & 3 \\ -4 & 0 \\ -2 & -2 \end{pmatrix} \cdot
+    \begin{pmatrix} 0 & 1 & -4 & 2 \\ 2 & 3 & -2 & -1 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -2 & -1 \end{pmatrix} \cdot
+    \begin{pmatrix} 1 \\ -2 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -2 & 2 & -2 & -3 \\ 2 & -4 & -2 & 2 \\ 0 & 2 & -2 & -2 \\ 1 & -2 & -2 & -2 \end{pmatrix} \cdot
+    \begin{pmatrix} 1 & -2 & 2 \\ -4 & -2 & -2 \\ 3 & 1 & 4 \\ -4 & 1 & -2 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -1 & -3 & 0 & -1 \\ 4 & -2 & 1 & 2 \end{pmatrix} \cdot
+    \begin{pmatrix} -3 & -4 \\ -4 & 0 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -1 & 1 & -2 \\ -2 & 2 & -4 \\ 1 & -2 & -2 \end{pmatrix} \cdot
+    \begin{pmatrix} -1 & 2 & -4 \\ 1 & 3 & 0 \\ 1 & 4 & -4 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -3 & 3 \\ -3 & 2 \end{pmatrix} \cdot
+    \begin{pmatrix} 2 & -3 \\ -2 & -4 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 1 & 1 & -3 \end{pmatrix} \cdot
+    \begin{pmatrix} -1 \\ -2 \\ 3 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -4 & 2 & 1 \\ 4 & 0 & -2 \\ 2 & 3 & -3 \\ -2 & -2 & -2 \end{pmatrix} \cdot
+    \begin{pmatrix} -1 & 2 & 0 & -2 \\ 2 & -2 & 0 & -1 \\ -4 & 3 & -3 & 4 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -2 & -4 & 2 & 4 \\ 3 & -3 & 2 & 1 \end{pmatrix} \cdot
+    \begin{pmatrix} 0 & 4 & -1 & -4 \\ 2 & 3 & -4 & -1 \\ 3 & 2 & -2 & 4 \end{pmatrix} = \]
+
+   \[ \begin{pmatrix} -3 & -2 & -1 & -3 \end{pmatrix} \cdot
+    \begin{pmatrix} 2 \\ -2 \\ 3 \\ -2 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 4 & 4 & 2 & 3 \end{pmatrix} \cdot
+    \begin{pmatrix} 3 \\ 3 \\ -2 \\ 1 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 3 & 2 & -2 \end{pmatrix} \cdot
+    \begin{pmatrix} 2 \\ 4 \\ 3 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 2 & -1 & 0 & -2 \\ 0 & -4 & -3 & -1 \end{pmatrix} \cdot
+    \begin{pmatrix} 4 & -3 & 2 & 4 \\ -3 & -4 & 1 & 1 \\ 1 & 3 & -2 & 3 \\ -1 & -2 & 3 & 0 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -3 & -3 & 3 & 2 \\ 2 & 2 & -3 & 1 \end{pmatrix} \cdot
+    \begin{pmatrix} 0 & 1 \\ 4 & 2 \\ -3 & -1 \\ -3 & 4 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -4 & -3 \end{pmatrix} \cdot
+    \begin{pmatrix} -2 \\ 3 \\ 4 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 4 & 4 \end{pmatrix} \cdot
+    \begin{pmatrix} 1 \\ 4 \\ -1 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 1 & -2 & 3 \end{pmatrix} \cdot
+    \begin{pmatrix} 3 \\ 1 \\ 2 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -3 & 2 \end{pmatrix} \cdot
+    \begin{pmatrix} -1 \\ 1 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -2 & -4 & -4 & 0 \\ 0 & 3 & 4 & -4 \\ 4 & 2 & -2 & -4 \\ 0 & 0 & 4 & -1 \end{pmatrix} \cdot
+    \begin{pmatrix} 0 & -1 \\ -1 & 1 \\ -4 & -3 \\ 2 & 1 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -3 \\ 3 \\ -3 \\ -4 \end{pmatrix} \cdot
+    \begin{pmatrix} 2 & 4 & -2 & 1 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 2 \\ 0 \end{pmatrix} \cdot
+    \begin{pmatrix} -1 & -3 & -2 & 2 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 0 & -4 & -4 & 4 \end{pmatrix} \cdot
+    \begin{pmatrix} 1 \\ 4 \\ 0 \\ 4 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -3 & -1 \\ -3 & -1 \end{pmatrix} \cdot
+    \begin{pmatrix} 0 & -3 & 3 & -2 \\ -4 & 1 & -1 & 4 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 4 & 0 \\ -1 & 4 \\ 1 & -3 \end{pmatrix} \cdot
+    \begin{pmatrix} -4 & -4 \\ -4 & 2 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -1 \\ 3 \\ 2 \\ 4 \end{pmatrix} \cdot
+    \begin{pmatrix} 0 & -1 & 0 & 0 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 3 \\ -2 \\ 2 \\ 3 \end{pmatrix} \cdot
+    \begin{pmatrix} -2 & -3 & -4 & 2 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} 2 & -2 & -4 & 4 \\ 0 & 1 & -3 & -2 \\ -1 & 3 & 0 & -2 \end{pmatrix} \cdot
+    \begin{pmatrix} -4 & 1 \\ -4 & 3 \end{pmatrix} = \]
+
+    \[ \begin{pmatrix} -4 & -1 & 3 \end{pmatrix} \cdot
+    \begin{pmatrix} -4 \\ -3 \\ 3 \end{pmatrix} = \]
+
+  \question \textbf{Automatic generation of exercises}
+  Write some matlab code that generates exercises like this one automatically! :-) 
+  
+\end{questions}
+
+
+\end{document}
diff --git a/linearalgebra/resources/Pillow2007-STC-LNPmodel.pdf b/linearalgebra/resources/Pillow2007-STC-LNPmodel.pdf
new file mode 100644
index 0000000..da7133a
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diff --git a/linearalgebra/simulations/covareigen3examples.m b/linearalgebra/simulations/covareigen3examples.m
deleted file mode 100644
index 3d16628..0000000
--- a/linearalgebra/simulations/covareigen3examples.m
+++ /dev/null
@@ -1,14 +0,0 @@
-n = 10000;
-
-% three distributions:
-x = randn( n, 1 );
-y = randn( n, 1 );
-z = randn( n, 1 );
-f = figure( 'Position', [ scrsz(3)/2 scrsz(4)/2 scrsz(3)/2 scrsz(4)/2 ]);
-d = [ 0 4 -4 8 ];
-for k = 1:4
-    x((k-1)*n/4+1:k*n/4) = x((k-1)*n/4+1:k*n/4) + d(k);
-    y((k-1)*n/4+1:k*n/4) = y((k-1)*n/4+1:k*n/4) - d(k);
-    z((k-1)*n/4+1:k*n/4) = z((k-1)*n/4+1:k*n/4) + d(k);
-end
-covareigen3( x, y, z );
diff --git a/linearalgebra/simulations/matrix2dtrafos.m b/linearalgebra/simulations/matrix2dtrafos.m
deleted file mode 100644
index da6840e..0000000
--- a/linearalgebra/simulations/matrix2dtrafos.m
+++ /dev/null
@@ -1,74 +0,0 @@
-m = [ 1 0; 0 1];
-matrixbox( m );
-title( 'Identity' );
-waitforbuttonpress;
-
-m = [ 2 0; 0 1];
-matrixbox( m );
-title( 'Scale x' );
-waitforbuttonpress;
-
-m = [ 1 0; 0 2];
-matrixbox( m );
-title( 'Scale y' );
-waitforbuttonpress;
-
-m = [ 2 0; 0 2];
-matrixbox( m );
-title( 'Scale both' );
-waitforbuttonpress;
-
-m = [ -1 0; 0 1];
-matrixbox( m );
-title( 'Flip x' );
-waitforbuttonpress;
-
-m = [ 1 0; 0 -1];
-matrixbox( m );
-title( 'Flip y' );
-waitforbuttonpress;
-
-m = [ -1 0; 0 -1];
-matrixbox( m );
-title( 'Flip both' );
-waitforbuttonpress;
-
-m = [ 1 0; 1 1];
-matrixbox( m );
-title( 'Shear x' );
-waitforbuttonpress;
-
-m = [ 1 1; 0 1];
-matrixbox( m );
-title( 'Shear y' );
-waitforbuttonpress;
-
-phi = 0.1667*pi;
-m = [ cos(phi) -sin(phi); sin(phi) cos(phi)];
-matrixbox( m );
-title( 'Rotate 30' );
-waitforbuttonpress;
-
-phi = 0.333*pi;
-m = [ cos(phi) -sin(phi); sin(phi) cos(phi)];
-matrixbox( m );
-title( 'Rotate 60' );
-waitforbuttonpress;
-
-phi = 0.5*pi;
-m = [ cos(phi) -sin(phi); sin(phi) cos(phi)];
-matrixbox( m );
-title( 'Rotate 90' );
-waitforbuttonpress;
-
-phi = 0.75*pi;
-m = [ cos(phi) -sin(phi); sin(phi) cos(phi)];
-matrixbox( m );
-title( 'Rotate 135' );
-waitforbuttonpress;
-
-phi = 1.0*pi;
-m = [ cos(phi) -sin(phi); sin(phi) cos(phi)];
-matrixbox( m );
-title( 'Rotate 180' );
-waitforbuttonpress;
diff --git a/linearalgebra/simulations/matrixbox.m b/linearalgebra/simulations/matrixbox.m
deleted file mode 100644
index 5abbfa0..0000000
--- a/linearalgebra/simulations/matrixbox.m
+++ /dev/null
@@ -1,24 +0,0 @@
-function matrixbox( m )
-% visualizes the effect of a matrix m on a set of vectors forming a box
-% m: a 2x2 matrix
-
-    v = [ 0 1 1 0 0; 0 0 1 1 0];
-    w = m*v;
-    clf;
-    hold on;
-    %set(gca, 'Xtick', [], 'Ytick', [], 'box', 'off')
-    % axis:
-    plot( [-2 2], [0 0], 'k', 'LineWidth', 1 );
-    plot( [0 0], [-2 2], 'k', 'LineWidth', 1 );
-    % old box:
-    plot( v(1,:), v(2,:), 'k', 'LineWidth', 1.0 )
-    quiver( [v(1,1)], [v(2,1)], [v(1,3)], [v(2,3)], 1.0, 'k', 'LineWidth', 2.0 );
-    scatter( [v(1,2)], [v(2,2)], 60.0, 'filled', 'k' );
-    % transfomred box:
-    plot( w(1,:), w(2,:), 'b', 'LineWidth', 2.0 )
-    quiver( [w(1,1)], [w(2,1)], [w(1,3)], [w(2,3)], 1.0, 'b', 'LineWidth', 3.0 );
-    scatter( [w(1,2)], [w(2,2)], 100.0, 'filled', 'b' );
-    hold off;
-    xlim( [ -2 2 ] );
-    ylim( [ -2 2 ] );
-end
diff --git a/linearalgebra/simulations/spikesortingwave.m b/linearalgebra/simulations/spikesortingwave.m
deleted file mode 100644
index 369915e..0000000
--- a/linearalgebra/simulations/spikesortingwave.m
+++ /dev/null
@@ -1,127 +0,0 @@
-% generate spikes:
-n = 1000;
-misi = 0.01;
-isis = exprnd( misi, n, 1 );
-isis = isis + 0.01;
-spikes = cumsum( isis );
-p = rand( size( spikes ) );
-
-% spike waveforms:
-dt = 0.0001;
-x = -0.01:dt:0.01;
-y1 = 2.0*exp( -(x-0.0003).^2/2.0/0.0005^2 ) - 1.4*exp( -(x-0.0005).^2/2.0/0.0005^2 );
-y2 = exp( -x.^2/2.0/0.002^2 ).*cos(2.0*pi*x/0.005);
-p12 = 0.5;
-
-% voltage trace:
-noise = 0.2;
-t = 0:dt:spikes(end)+3.0*x(end);
-v = noise*randn( 1, length( t ) );
-for k = 1:length( spikes )
-    inx = ceil( spikes(k)/dt );
-    if p(k) < p12
-        v(inx:inx+length(x)-1) = v(inx:inx+length(x)-1) + y1; 
-    else
-        v(inx:inx+length(x)-1) = v(inx:inx+length(x)-1) + y2; 
-    end
-end
-
-figure( 1 );
-clf;
-plot( t(t<1.0), v(t<1.0) );
-hold on;
-
-% find peaks:
-thresh = 0.7;
-inx = find( v(2:end-1) > thresh & v(1:end-2)<v(2:end-1) & v(2:end-1) > v(3:end) ) + 1;
-spiket = t(inx);
-spikev = v(inx);
-tinx = inx;
-for k=1:2
-    inx = find( ( spiket(2:end-1)-spiket(1:end-2)>0.005 | spikev(2:end-1) > spikev(1:end-2) ) & ( spiket(3:end)-spiket(2:end-1)>0.005 | spikev(2:end-1) > spikev(3:end) ) )+1;
-    spiket = spiket(inx);
-    spikev = spikev(inx);
-    tinx = tinx(inx);
-end
-scatter( spiket(spiket<1.0), spikev(spiket<1.0), 100.0, 'r', 'filled' );
-%scatter( t(tinx), v(tinx), 100.0, 'r', 'filled' );
-hold off;
-
-% spike waveform snippets:
-w = ceil( 0.005/dt );
-vs = [];
-for k=1:length(tinx)
-    vs = [ vs; v(tinx(k)-w:tinx(k)+w-1) ];
-end
-ts = t(1:size(vs,2));
-ts = ts - ts(floor(length(ts)/2));
-figure( 2 );
-clf;
-hold on
-for k=1:size(vs,1)
-    plot( ts, vs(k,:), '-b' );
-end
-hold off
-
-% pca:
-cv = cov( vs );
-[ ev , ed ] = eig( cv );
-[d,dinx] = sort( diag(ed), 'descend' );
-% spectrum of eigenvalues:
-figure( 3 );
-clf;
-subplot( 4, 1, 1 );
-scatter( 1:length(d), d, 'b', 'filled' );
-% project onto eigenvectors:
-nx = vs * ev(:,dinx(1));
-ny = vs * ev(:,dinx(2));
-%scatter( nx, ny, 'b', 'filled', 'MarkerEdgeColor', 'white' );
-%hold on;
-
-% features:
-subplot( 4, 2, 5 );
-plot( 1000.0*ts, ev(:,dinx(1)), 'g', 'LineWidth', 2 );
-subplot( 4, 2, 6 );
-plot( 1000.0*ts, ev(:,dinx(2)), 'r', 'LineWidth', 2 );
-
-% clustering:
-%kx = kmeans( [ nx, ny ], 2 );
-% nx smaller or greater a threshold:
-kthresh = 1.6;
-kx = ones( size( vs, 1 ), 1 );
-kx(nx>kthresh) = 2;
-
-subplot( 4, 1, 2 );
-scatter( nx(kx==1), ny(kx==1), 'g', 'filled', 'MarkerEdgeColor', 'white' );
-hold on;
-scatter( nx(kx==2), ny(kx==2), 'r', 'filled', 'MarkerEdgeColor', 'white' );
-hold off;
-
-% show sorted spike waveforms:
-subplot( 4, 2, 7 );
-hold on
-kinx1 = find(kx==1);
-for k=1:length(kinx1)
-    plot( 1000.0*ts, vs(kinx1(k),:), '-g' );
-end
-plot( 1000.0*x, y1, '-k', 'LineWidth', 2 );
-xlim( [ 1000.0*ts(1) 1000.0*ts(end) ] )
-hold off
-subplot( 4, 2, 8 );
-hold on
-kinx2 = find(kx==2);
-for k=1:length(kinx2)
-    plot( 1000.0*ts, vs(kinx2(k),:), '-r' );
-end
-plot( 1000.0*x, y2, '-k', 'LineWidth', 2 );
-xlim( [ 1000.0*ts(1) 1000.0*ts(end) ] )
-hold off
-
-% spike trains:
-figure( 1 );
-hold on;
-six1 = find( spiket(kinx1)<1.0 );
-scatter( spiket(kinx1(six1)), spikev(kinx1(six1)), 100.0, 'g', 'filled' );
-six2 = find( spiket(kinx2)<1.0 );
-scatter( spiket(kinx2(six2)), spikev(kinx2(six2)), 100.0, 'r', 'filled' );
-hold off;
diff --git "a/moduledescription/Einf\303\274hrung_wissenschaftl_Datenverarbeitung.pdf" "b/moduledescription/Einf\303\274hrung_wissenschaftl_Datenverarbeitung.pdf"
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diff --git a/moduledescription/Hiwis.odt b/moduledescription/Hiwis.odt
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diff --git a/moduledescription/Hiwis.pdf b/moduledescription/Hiwis.pdf
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diff --git a/moduledescription/ModuleScientificComputing.doc b/moduledescription/ModuleScientificComputing.doc
new file mode 100644
index 0000000..5d85b1e
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diff --git a/moduledescription/ModuleScientificComputing.odt b/moduledescription/ModuleScientificComputing.odt
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diff --git a/pointprocesses/code/colorednoisepdf.m b/pointprocesses/code/colorednoisepdf.m
new file mode 100644
index 0000000..28f421b
--- /dev/null
+++ b/pointprocesses/code/colorednoisepdf.m
@@ -0,0 +1,10 @@
+function pcn = colorednoisepdf( x, misi, epsilon, tau )
+% returns the ISI pdf for PIF with colored noise drive
+% x: the input ISI
+% misis: the mean isi
+% epsilon: a parameter
+% tau: the correlation time of the noise
+    gamma1 = x/tau+exp(-x/tau)-1.0;
+    gamma2 = 1.0-exp(-x/tau);
+    pcn=exp(-(x-misi).^2./(4.0*epsilon*tau.^2.*gamma1)).*(((misi-x).*gamma2+2*gamma1*tau).^2./(2*gamma1*tau^2)-epsilon*(gamma2.^2-2*gamma1.*exp(-x/tau))) ./ (2*tau*sqrt(4*pi*epsilon*gamma1.^3));
+end
diff --git a/pointprocesses/code/colorednoisepdfisifit.m b/pointprocesses/code/colorednoisepdfisifit.m
new file mode 100644
index 0000000..1c653c0
--- /dev/null
+++ b/pointprocesses/code/colorednoisepdfisifit.m
@@ -0,0 +1,24 @@
+% misi = 0.02;
+% epsilon = 1.0;
+% tau = 0.1;
+x=0:0.002:0.1;
+% pcn = colorednoisepdf( x, misi, epsilon, tau )+10.0*randn( size( x ) );
+% plot( x, pcn );
+
+spikes = lifouspikes( 10, 15, 50.0, 1.0, 1.0 );
+isivec = isis( spikes );
+misi = mean( isivec );
+1.0/misi
+isibins = 0:0.0005:0.1;
+[ n, c ] = hist( isivec, isibins );
+n = n / sum(n)/(isibins(2)-isibins(1));
+bar( c, n );
+
+beta0 = [ 1.0, 0.01 ];
+b = nlinfit(c(1:end-2), n(1:end-2), @(b,x)(colorednoisepdf(x, misi, b(1), b(2))), beta0)
+
+pcn = colorednoisepdf( x, misi, b(1), b(2) );
+hold on
+plot( x, pcn, 'r', 'LineWidth', 3 );
+hold off
+
diff --git a/pointprocesses/code/counthist.m b/pointprocesses/code/counthist.m
new file mode 100644
index 0000000..d64fb23
--- /dev/null
+++ b/pointprocesses/code/counthist.m
@@ -0,0 +1,38 @@
+function [ counts, bins ] = counthist( spikes, w )
+% computes count histogram and compare them  with Poisson distribution
+% spikes: a cell array of vectors of spike times
+% w: observation window duration for computing the counts
+
+    % collect spike counts:
+    tmax = spikes{1}(end);
+    n = [];
+    r = [];
+    for k = 1:length(spikes)
+        for tk = 0:w:tmax-w
+            nn = sum( ( spikes{k} >= tk ) & ( spikes{k} < tk+w ) );
+            %nn = length( find( ( spikes{k} >= tk ) & ( spikes{k} < tk+w ) ) );
+            n = [ n nn ];
+        end
+        rate = (length(spikes{k})-1)/(spikes{k}(end) - spikes{k}(1));
+        r = [ r rate ];
+    end
+    % histogram of spike counts:
+    maxn = max( n );
+    [counts, bins ] = hist( n, 0:1:maxn+1 );
+    counts = counts / sum( counts );
+    if nargout == 0
+        bar( bins, counts );
+        hold on;
+        % Poisson distribution:
+        rate = mean( r );
+        x = 0:1:20;
+        l = rate*w;
+        y = l.^x.*exp(-l)./factorial(x);
+        plot( x, y, 'r', 'LineWidth', 3 );
+        xlim( [ 0 20 ] );
+        hold off;
+        xlabel( 'counts k' );
+        ylabel( 'P(k)' );
+    end
+end
+
diff --git a/pointprocesses/code/fano.m b/pointprocesses/code/fano.m
new file mode 100644
index 0000000..a73cada
--- /dev/null
+++ b/pointprocesses/code/fano.m
@@ -0,0 +1,39 @@
+function fano( spikes )
+% computes fano factor as a function of window size
+% spikes: a cell array of vectors of spike times
+
+    tmax = spikes{1}(end);
+    windows = 0.01:0.05:0.01*tmax;
+    mc = windows;
+    vc = windows;
+    ff = windows;
+    fs = windows;
+    for j = 1:length(windows)
+        w = windows( j );
+        % collect counts:
+        n = [];
+        for k = 1:length(spikes)
+            for tk = 0:w:tmax-w
+                nn = sum( ( spikes{k} >= tk ) & ( spikes{k} < tk+w ) );
+                %nn = length( find( ( spikes{k} >= tk ) & ( spikes{k} < tk+w ) ) );
+                n = [ n nn ];
+            end
+        end
+        % statistics for current window:
+        mc(j) = mean( n );
+        vc(j) = var( n );
+        ff(j) = vc( j )/mc( j );
+        fs(j) = sqrt(vc( j )/mc( j ));
+    end
+    
+    subplot( 1, 2, 1 );
+    scatter( mc, vc, 'filled' );
+    xlabel( 'Mean count' );
+    ylabel( 'Count variance' );
+
+    subplot( 1, 2, 2 );
+    scatter( 1000.0*windows, fs, 'filled' );
+    xlabel( 'Window W [ms]' );
+    ylabel( 'Fano factor' );
+end
+
diff --git a/pointprocesses/code/inversegauss.m b/pointprocesses/code/inversegauss.m
new file mode 100644
index 0000000..58a9512
--- /dev/null
+++ b/pointprocesses/code/inversegauss.m
@@ -0,0 +1,5 @@
+function y = inversegauss( x, m, d )
+% returns the inverse Gauss density with mean isi m and diffusion
+% coefficent d
+    y = exp(-(x-m).^2./(4.0*d.*x.*m.^2.0))./sqrt(4.0*pi*d.*x.^3.0);
+end
diff --git a/pointprocesses/code/inversegaussplot.m b/pointprocesses/code/inversegaussplot.m
new file mode 100644
index 0000000..c43e6d2
--- /dev/null
+++ b/pointprocesses/code/inversegaussplot.m
@@ -0,0 +1,28 @@
+f = figure;
+subplot( 1, 2, 1 );
+dx=0.0001;
+x = dx:dx:0.5;
+hold all
+m = 0.02;
+for d = [ 0.1 1 10 50 200 ]
+    plot( 1000.0*x, inversegauss( x, m, d ), 'LineWidth', 3, 'DisplayName', sprintf( 'D=%.1f', d ) );
+end
+title( sprintf( 'm=%g ms', 1000.0*m ) )
+xlim( [ 0 50 ] );
+xlabel( 'ISI [ms]' );
+ylabel( 'p(ISI)' );
+legend( '-DynamicLegend' );
+hold off;
+
+subplot( 1, 2, 2 );
+hold all;
+d = 5.0;
+for m = [ 0.005 0.01 0.02 0.05 ]
+    plot( 1000.0*x, inversegauss( x, m, d ), 'LineWidth', 3, 'DisplayName', sprintf( 'm=%g ms', 1000.0*m ) );
+end
+title( sprintf( 'D=%g Hz', d ) )
+xlim( [ 0 50 ] )
+xlabel( 'ISI [ms]' );
+ylabel( 'p(ISI)' );
+legend( '-DynamicLegend' )
+hold off
diff --git a/pointprocesses/code/isihist.m b/pointprocesses/code/isihist.m
new file mode 100644
index 0000000..a9722fd
--- /dev/null
+++ b/pointprocesses/code/isihist.m
@@ -0,0 +1,32 @@
+function isihist( isis, binwidth )
+% plot histogram of isis
+% isis: vector of interspike intervals
+% binwidth: optional width to be used for the isi bins
+
+    if nargin < 2
+        nperbin = 200;  % average number of data points per bin
+        bins = length( isis )/nperbin;  % number of bins
+        binwidth = max( isis )/bins;
+        if binwidth < 5e-4   % half a millisecond
+            binwidth = 5e-4;
+        end
+    end
+    bins = 0.5*binwidth:binwidth:max(isis);
+    % histogram:
+    [ nelements, centers ] = hist( isis, bins );
+    % normalization (integral = 1):
+    nelements = nelements / sum( nelements ) / binwidth;
+    % plot:
+    bar( 1000.0*centers, nelements );
+    xlabel( 'ISI [ms]' )
+    ylabel( 'p(ISI) [1/s]')
+    % annotation:
+    misi = mean( isis );
+    sdisi = std( isis );
+    disi = sdisi^2.0/2.0/misi^3;
+    text( 0.5, 0.6, sprintf( 'mean=%.1f ms', 1000.0*misi ), 'Units', 'normalized' )
+    text( 0.5, 0.5, sprintf( 'std=%.1f ms', 1000.0*sdisi ), 'Units', 'normalized' )
+    text( 0.5, 0.4, sprintf( 'CV=%.2f', sdisi/misi ), 'Units', 'normalized' )
+    %text( 0.5, 0.3, sprintf( 'D=%.1f Hz', disi ), 'Units', 'normalized' )
+end
+
diff --git a/pointprocesses/code/isireturnmap.m b/pointprocesses/code/isireturnmap.m
new file mode 100644
index 0000000..d790e13
--- /dev/null
+++ b/pointprocesses/code/isireturnmap.m
@@ -0,0 +1,24 @@
+function isireturnmap( isis, lag2 )
+% plot return maps for lag 1 and lag lag2
+
+    clf;
+    subplot( 1, 2, 1 );
+    lag = 1;
+    scatter( 1000.0*isis(1:end-lag)', 1000.0*isis(1+lag:end)', 'b', 'filled', 'MarkerEdgeColor', 'white' );
+    xlabel( 'ISI T_i [ms]' );
+    ylabel( 'ISI T_{i+1} [ms]' );
+    maxisi = max( isis );
+    maxy = ceil(maxisi/10)*10.0;
+    xlim( [0 1.5*maxy ])
+    ylim( [0 maxy ])
+
+    subplot( 1, 2, 2 );
+    lag = lag2;
+    scatter( 1000.0*isis(1:end-lag)', 1000.0*isis(1+lag:end)', 'b', 'filled', 'MarkerEdgeColor', 'white' );
+    xlabel( 'ISI T_i [ms]' );
+    ylabel( 'ISI T_{i+2} [ms]' );
+    xlim( [0 1.5*maxy ])
+    ylim( [0 maxy ])
+
+end
+
diff --git a/pointprocesses/code/isis.m b/pointprocesses/code/isis.m
new file mode 100644
index 0000000..b0b95ff
--- /dev/null
+++ b/pointprocesses/code/isis.m
@@ -0,0 +1,15 @@
+function isivec = isis( spikes )
+% returns a single list of isis computed from all trials in spikes
+% spikes: a cell array of vectors of spike times
+
+    isivec = [];
+    for k = 1:length(spikes)
+        difftimes = diff( spikes{k} );
+        if ( size( difftimes, 1 ) == 1 )
+            isivec = [ isivec difftimes ];
+        elseif ( size( difftimes, 2 ) == 1 )
+            isivec = [ isivec difftimes' ];
+        end
+    end
+end
+
diff --git a/pointprocesses/code/isiserialcorr.m b/pointprocesses/code/isiserialcorr.m
new file mode 100644
index 0000000..d5b44c1
--- /dev/null
+++ b/pointprocesses/code/isiserialcorr.m
@@ -0,0 +1,26 @@
+function isicorr = isiserialcorr( isis, maxlag )
+% serial correlation of isis
+% isis: vector of interspike intervals
+% maxlag: the maximum lag
+
+    lags = 0:maxlag;
+    isicorr = zeros( size( lags ) );
+    for k = 1:length(lags)
+        lag = lags(k);
+        if length( isis ) > lag+10
+            cc = corrcoef( [ isis(1:end-lag)', isis(1+lag:end)' ] );
+            isicorr(k) = cc( 1, 2 );
+        end
+    end
+    
+    if nargout == 0
+        % plot:
+        plot( lags, isicorr, '-b' );
+        hold on;
+        scatter( lags, isicorr, 100.0, 'b', 'filled' );
+        hold off;
+        xlabel( 'Lag k' )
+        ylabel( '\rho_k')
+    end
+end
+
diff --git a/pointprocesses/code/lifadaptspikes.m b/pointprocesses/code/lifadaptspikes.m
new file mode 100644
index 0000000..2ef1874
--- /dev/null
+++ b/pointprocesses/code/lifadaptspikes.m
@@ -0,0 +1,53 @@
+function spikes = lifadaptspikes( trials, input, tmaxdt, D, tauadapt, adaptincr )
+% Generate spike times of a leaky integrate-and-fire neuron
+% with an adaptation current
+% trials: the number of trials to be generated
+% input: the stimulus either as a single value or as a vector
+% tmaxdt: in case of a single value stimulus the duration of a trial
+%         in case of a vector as a stimulus the time step
+% D: the strength of additive white noise
+% tauadapt: adaptation time constant
+% adaptincr: adaptation strength
+    
+    tau = 0.01;
+    if nargin < 4
+        D = 1e0;
+    end
+    if nargin < 5
+        tauadapt = 0.1;
+    end
+    if nargin < 6
+        adaptincr = 1.0;
+    end
+    vreset = 0.0;
+    vthresh = 10.0;
+    dt = 1e-4;
+    
+    if max( size( input ) ) == 1
+        input = input * ones( ceil( tmaxdt/dt ), 1 );
+    else
+        dt = tmaxdt;
+    end
+    spikes = cell( trials, 1 );
+    for k=1:trials
+        times = [];
+        j = 1;
+        v = vreset;
+        a = 0.0;
+        noise = sqrt(2.0*D)*randn( length( input ), 1 )/sqrt(dt);
+        for i=1:length( noise )
+            v = v + ( - v - a + noise(i) + input(i))*dt/tau;
+            a = a + ( - a )*dt/tauadapt;
+            if v >= vthresh
+                v = vreset;
+                a = a + adaptincr/tauadapt;
+                spiketime = i*dt;
+                if spiketime > 4.0*tauadapt
+                    times(j) = spiketime - 4.0*tauadapt;
+                    j = j + 1;
+                end
+            end
+        end
+        spikes{k} = times;
+    end
+end
diff --git a/pointprocesses/code/lifboltzmanspikes.m b/pointprocesses/code/lifboltzmanspikes.m
new file mode 100644
index 0000000..14640e4
--- /dev/null
+++ b/pointprocesses/code/lifboltzmanspikes.m
@@ -0,0 +1,51 @@
+function spikes = lifboltzmanspikes( trials, input, tmaxdt, D, imax, ithresh, slope )
+% Generate spike times of a leaky integrate-and-fire neuron
+% trials: the number of trials to be generated
+% input: the stimulus either as a single value or as a vector
+% tmaxdt: in case of a single value stimulus the duration of a trial
+%         in case of a vector as a stimulus the time step
+% D: the strength of additive white noise
+% imax: maximum output of boltzman
+% ithresh: threshold of boltzman input
+% slope: slope factor of boltzman input
+    
+    tau = 0.01;
+    if nargin < 4
+        D = 1e0;
+    end
+    if nargin < 5
+        imax = 20;
+    end
+    if nargin < 6
+        ithresh = 10;
+    end
+    if nargin < 7
+        slope = 1;
+    end
+    vreset = 0.0;
+    vthresh = 10.0;
+    dt = 1e-4;
+    
+    if length( input ) == 1
+        input = input * ones( ceil( tmaxdt/dt ), 1 );
+    else
+        dt = tmaxdt;
+    end
+    inb = imax./(1.0+exp(-slope.*(input - ithresh)));
+    spikes = cell( trials, 1 );
+    for k=1:trials
+        times = [];
+        j = 1;
+        v = vreset;
+        noise = sqrt(2.0*D)*randn( length( input ), 1 )/sqrt(dt);
+        for i=1:length( noise )
+            v = v + ( - v + noise(i) + inb(i))*dt/tau;
+            if v >= vthresh
+                v = vreset;
+                times(j) = i*dt;
+                j = j + 1;
+            end
+        end
+        spikes{k} = times;
+    end
+end
diff --git a/pointprocesses/code/liffano.m b/pointprocesses/code/liffano.m
new file mode 100644
index 0000000..b90403f
--- /dev/null
+++ b/pointprocesses/code/liffano.m
@@ -0,0 +1,23 @@
+input = 65.0;   % lifadapt 100Hz
+%input = 8.0;   % lifadapt 10Hz
+%input = 15.7;  % lif 100Hz
+%input = 8.3;   % lif 10Hz
+trials = 10;
+tmax = 100.0;
+Dnoise = 0.1;
+Dounoise = 5e1;
+outau = 10.0;
+adapttau = 0.1;
+adaptincr = 5.0;
+
+%spikes = lifouadaptspikes( trials, input, 1.0, Dnoise, Dounoise, outau, adapttau, adaptincr );
+%spikeraster( spikes );
+%return;
+
+% generate spikes:
+%spikes = lifspikes( trials, input, tmax, noise );
+spikes = lifouspikes( trials, input, tmax, Dounoise, outau );
+%spikes = lifadaptspikes( trials, input, tmax, Dnoise, adapttau, adaptincr );
+%spikes = lifouadaptspikes( trials, input, tmax, Dnoise, Dounoise, outau, adapttau, adaptincr );
+
+fano( spikes );
diff --git a/pointprocesses/code/lifficurves.m b/pointprocesses/code/lifficurves.m
new file mode 100644
index 0000000..5106c12
--- /dev/null
+++ b/pointprocesses/code/lifficurves.m
@@ -0,0 +1,36 @@
+% lif:
+noises = [ 1e-5 1e-4 1e-3 1e-2 1e-1 ];
+inputs = 0:0.1:20;
+duration = 50.0;
+% pif:
+% noises = [ 1e-1 1 1e1 1e2 1e3 ];
+% inputs = -5:0.1:10;
+% duration = 100.0;
+
+f = figure;
+hold all;
+for noise = noises
+    fprintf( 'noise=%.0e\n', noise );
+    rates = [];
+    for input = inputs
+        spikes = lifspikes( 10, input, duration, noise );
+        % spikes = pifspikes( 50, input, duration, noise );
+        nspikes = 0;
+        for k = 1:length( spikes )
+            nspikes = nspikes + length( spikes{k} );
+        end
+        rate = nspikes/duration/length( spikes );
+        %fprintf( 'I=%g  N=%d  rate=%g\n', input, length( spikes ), rate )
+        rates = [ rates rate ];
+    end
+    plot( inputs, rates, 'LineWidth', 2, 'DisplayName', sprintf( 'D=%.0e', noise ) );
+end
+xlabel( 'Input' );
+xlim( [ inputs(1) inputs(end) ] )
+ylabel( 'Firing rate [Hz]' );
+%title( 'Leaky integrate-and-fire' )
+title( 'Perfect integrate-and-fire' )
+legend( '-DynamicLegend', 'Location', 'NorthWest' )
+hold off
+
+
diff --git a/pointprocesses/code/lifinputdiscriminationslope.m b/pointprocesses/code/lifinputdiscriminationslope.m
new file mode 100644
index 0000000..3a52ef0
--- /dev/null
+++ b/pointprocesses/code/lifinputdiscriminationslope.m
@@ -0,0 +1,65 @@
+%input = 15.7;  % lif 100Hz
+%input = 8.3;   % lif 10Hz
+trials = 10;
+tmax = 50.0;
+Dnoise = 1.0;
+imax = 25.0;
+ithresh = 10.0;
+slope=0.2;
+
+% inputs = 0:2:30;
+% rates = zeros( size( inputs ) );
+% for j = 1:length( inputs )
+%     input = inputs(j);
+%     spikes = lifboltzmanspikes( trials, input, tmax, Dnoise, imax, ithresh, slope );
+%     nspikes = 0;
+%     for k = 1:length( spikes )
+%         nspikes = nspikes + length( spikes{k} );
+%     end
+%     rate = nspikes/tmax/length( spikes );
+%     rates(j) = rate;
+% end
+% plot( inputs, rates );
+% grid on;
+% return
+
+input = 10.0;  % 80 Hz
+
+window = 0.2;
+slopes = 0.1:0.1:2.0;
+pmax = zeros( size( slopes) );
+for j = 1:length( slopes )
+    slope = slopes( j );
+    spikes = lifboltzmanspikes( trials, input, tmax, Dnoise, imax, ithresh, slope );
+    [ n1, bins1 ] = counthist( spikes, w );
+
+    spikes = lifboltzmanspikes( trials, input+1.0, tmax, Dnoise, imax, ithresh, slope );
+    [ n2, bins2 ] = counthist( spikes, w );
+
+    subplot( 2, 1, 1 );
+    bar( bins1, n1, 'b' );
+    hold on;
+    bar( bins2, n2, 'r' );
+    hold off;
+
+    subplot( 2, 1, 2 );
+    bmax = max( [ length( bins1 ), length( bins2 ) ] );
+    decision1 = zeros( bmax, 1 );
+    decision2 = zeros( bmax, 1 );
+    cs1 = ones( bmax, 1 );
+    cs1(1:length(n1)) = cumsum( n1 );
+    cs2 = ones( bmax, 1 );
+    cs2(1:length(n2)) = cumsum( n2 );
+    cbins = 0:1:bmax-1;
+    plot( cbins, cs1, 'b' );
+    hold on;
+    plot( cbins, cs2, 'r' );
+    plot( cbins, cs1-cs2, 'g' );
+    hold off;
+    pause( 0.1 );
+    pmax(j) = max( cs1-cs2 );
+end
+
+clf;
+subplot( 1, 1, 1 );
+plot( slopes, pmax );
diff --git a/pointprocesses/code/lifinputdiscriminationtime.m b/pointprocesses/code/lifinputdiscriminationtime.m
new file mode 100644
index 0000000..9cd4603
--- /dev/null
+++ b/pointprocesses/code/lifinputdiscriminationtime.m
@@ -0,0 +1,51 @@
+input = 65.0;   % lifadapt 100Hz
+%input = 8.0;   % lifadapt 10Hz
+%input = 15.7;  % lif 100Hz
+%input = 8.3;   % lif 10Hz
+trials = 10;
+tmax = 50.0;
+Dnoise = 0.1;
+Dounoise = 5e1;
+outau = 10.0;
+adapttau = 0.2;
+adaptincr = 0.5;
+
+windows = 0.05:0.05:1.0;
+pmax = zeros( size( windows ) );
+for j = 1:length( windows )
+    w = windows( j );
+    spikes = lifadaptspikes( trials, input, tmax, Dnoise, adapttau, adaptincr );
+    %spikes = lifouspikes( trials, input, tmax, Dounoise, outau);
+    [ n1, bins1 ] = counthist( spikes, w );
+
+    spikes = lifadaptspikes( trials, input+10.0, tmax, Dnoise, adapttau, adaptincr );
+    %spikes = lifouspikes( trials, input+10.0, tmax, Dounoise, outau );
+    [ n2, bins2 ] = counthist( spikes, w );
+
+    subplot( 2, 1, 1 );
+    bar( bins1, n1, 'b' );
+    hold on;
+    bar( bins2, n2, 'r' );
+    hold off;
+
+    subplot( 2, 1, 2 );
+    bmax = max( [ length( bins1 ), length( bins2 ) ] );
+    decision1 = zeros( bmax, 1 );
+    decision2 = zeros( bmax, 1 );
+    cs1 = ones( bmax, 1 );
+    cs1(1:length(n1)) = cumsum( n1 );
+    cs2 = ones( bmax, 1 );
+    cs2(1:length(n2)) = cumsum( n2 );
+    cbins = 0:1:bmax-1;
+    plot( cbins, cs1, 'b' );
+    hold on;
+    plot( cbins, cs2, 'r' );
+    plot( cbins, cs1-cs2, 'g' );
+    hold off;
+    pause( 0.1 );
+    pmax(j) = max( cs1-cs2 );
+end
+
+clf;
+subplot( 1, 1, 1 );
+plot( windows, pmax );
diff --git a/pointprocesses/code/lifisih.m b/pointprocesses/code/lifisih.m
new file mode 100644
index 0000000..f4e7a8e
--- /dev/null
+++ b/pointprocesses/code/lifisih.m
@@ -0,0 +1,35 @@
+%input = 65.0;   % lifadapt 100Hz
+%input = 8.0;   % lifadapt 10Hz
+input = 15.7;  % lif 100Hz
+%input = 8.3;   % lif 10Hz
+trials = 10;
+tmax = 100.0;
+noise = 1e-1;
+adapttau = 0.1;
+adaptincr = 5.0;
+
+% generate spikes:
+spikes = lifspikes( trials, input, tmax, noise );
+%spikes = lifadaptspikes( trials, input, tmax, noise, adapttau, adaptincr );
+
+% interspike intervals:
+isivec = isis( spikes );
+% histogram
+f = figure( 1 );
+isihist( isivec, 10e-4 );
+hold on
+% theoretical density:
+misi = mean( isivec );
+disi = var( isivec )/2.0/misi^3;
+xmax = 3.0*misi;
+x = 0:0.0001:xmax;
+plot( 1000.0*x, inversegauss( x, misi, disi ), 'r', 'LineWidth', 3 );
+% plot details:
+title( sprintf( 'LIF, input=%g, nisi=%d', input, length( isivec ) ) )
+xlim( [ 0.0 1000.0*xmax ] )
+legend( 'data', 'inverse Gaussian' )
+hold off
+
+% serial correlations:
+f = figure( 2 );
+isiserialcorr( isivec, 10 );
diff --git a/pointprocesses/code/lifisistats.m b/pointprocesses/code/lifisistats.m
new file mode 100644
index 0000000..1dd1321
--- /dev/null
+++ b/pointprocesses/code/lifisistats.m
@@ -0,0 +1,63 @@
+inputs = 0:0.1:20;  % lif
+inputs = 0:0.1:10; % pif
+avisi = [];
+sdisi = [];
+cvisi = [];
+dcisi = [];
+
+for input = inputs
+    input
+    % spikes = lifspikes( 100, input, 100.0, 1e-2 );
+    spikes = pifspikes( 100, input, 100.0, 1e-1 );
+    isivec = isis( spikes );
+    if length( isivec ) <= 1
+        av = Inf;
+        sd = NaN;
+        cv = NaN;
+        dc = NaN;
+    else
+        av = mean( isivec );
+        sd = std( isivec );
+        if av > 0.0 
+            cv = sd/av;
+            dc = sd^2.0/2.0/av^3;
+        else
+            cv = NaN;
+            dc = NaN;
+        end
+    end
+    avisi = [ avisi av ];
+    sdisi = [ sdisi sd ];
+    cvisi = [ cvisi cv ];
+    dcisi = [ dcisi dc ];
+end
+
+f = figure;
+subplot( 2, 2, 1 );
+plot( inputs, 1.0./avisi, '-b', 'LineWidth', 3 );
+xlabel( 'Input' );
+xlim( [ inputs(1) inputs(end) ] )
+title( 'Mean rate [Hz]' );
+
+subplot( 2, 2, 2 );
+plot( inputs, 1000.0*avisi, '-b', 'LineWidth', 3 );
+hold on;
+plot( inputs, 1000.0*sdisi, '-c', 'LineWidth', 3 );
+hold off;
+xlabel( 'Input' );
+xlim( [ inputs(1) inputs(end) ] )
+ylim( [ 0 1000 ] )
+title( 'ISI [ms]' );
+legend( 's.d. ISI', 'mean ISI' );
+
+subplot( 2, 2, 3 );
+plot( inputs, cvisi, '-b', 'LineWidth', 3 );
+xlabel( 'Input' );
+xlim( [ inputs(1) inputs(end) ] )
+title( 'CV' );
+
+subplot( 2, 2, 4 );
+plot( inputs, dcisi, '-b', 'LineWidth', 3  );
+xlabel( 'Input' );
+xlim( [ inputs(1) inputs(end) ] )
+title( 'D [Hz]' );
diff --git a/pointprocesses/code/lifouadaptspikes.m b/pointprocesses/code/lifouadaptspikes.m
new file mode 100644
index 0000000..7dddb2d
--- /dev/null
+++ b/pointprocesses/code/lifouadaptspikes.m
@@ -0,0 +1,64 @@
+function spikes = lifouadaptspikes( trials, input, tmaxdt, D, Dou, outau, tauadapt, adaptincr )
+% Generate spike times of a leaky integrate-and-fire neuron
+% with colored noise and an adaptation current
+% trials: the number of trials to be generated
+% input: the stimulus either as a single value or as a vector
+% tmaxdt: in case of a single value stimulus the duration of a trial
+%         in case of a vector as a stimulus the time step
+% D: the strength of additive noise
+% Dou: the strength of additive colored noise
+% outau: time constant of the colored noise
+% tauadapt: adaptation time constant
+% adaptincr: adaptation strength
+    
+    tau = 0.01;
+    if nargin < 4
+        D = 1e0;
+    end
+    if nargin < 5
+        Dou = 1e0;
+    end
+    if nargin < 6
+        outau = 1.0;
+    end
+    if nargin < 7
+        tauadapt = 0.1;
+    end
+    if nargin < 8
+        adaptincr = 1.0;
+    end
+    vreset = 0.0;
+    vthresh = 10.0;
+    dt = 1e-4;
+    
+    if max( size( input ) ) == 1
+        input = input * ones( ceil( tmaxdt/dt ), 1 );
+    else
+        dt = tmaxdt;
+    end
+    spikes = cell( trials, 1 );
+    for k=1:trials
+        times = [];
+        j = 1;
+        v = vreset;
+        n = 0.0;
+        a = 0.0;
+        noise = sqrt(2.0*D)*randn( length( input ), 1 )/sqrt(dt);
+        noiseou = sqrt(2.0*Dou)*randn( length( input ), 1 )/sqrt(dt);
+        for i=1:length( noise )
+            n = n + ( - n + noiseou(i))*dt/outau;
+            v = v + ( - v - a + noise(i) + n + input(i))*dt/tau;
+            a = a + ( - a )*dt/tauadapt;
+            if v >= vthresh
+                v = vreset;
+                a = a + adaptincr;
+                spiketime = i*dt;
+                if spiketime > 4.0*tauadapt
+                    times(j) = spiketime - 4.0*tauadapt;
+                    j = j + 1;
+                end
+            end
+        end
+        spikes{k} = times;
+    end
+end
diff --git a/pointprocesses/code/lifouspikes.m b/pointprocesses/code/lifouspikes.m
new file mode 100644
index 0000000..fc1f5d5
--- /dev/null
+++ b/pointprocesses/code/lifouspikes.m
@@ -0,0 +1,44 @@
+function spikes = lifouspikes( trials, input, tmaxdt, D, outau )
+% Generate spike times of a leaky integrate-and-fire neuron
+% trials: the number of trials to be generated
+% input: the stimulus either as a single value or as a vector
+% tmaxdt: in case of a single value stimulus the duration of a trial
+%         in case of a vector as a stimulus the time step
+% D: the strength of additive white noise
+% outau: time constant of the colored noise
+    
+    tau = 0.01;
+    if nargin < 4
+        D = 1e0;
+    end
+    if nargin < 5
+        outau = 1.0;
+    end
+    vreset = 0.0;
+    vthresh = 10.0;
+    dt = 1e-4;
+    
+    if length( input ) == 1
+        input = input * ones( ceil( tmaxdt/dt ), 1 );
+    else
+        dt = tmaxdt;
+    end
+    spikes = cell( trials, 1 );
+    for k=1:trials
+        times = [];
+        j = 1;
+        n = 0.0;
+        v = vreset;
+        noise = sqrt(2.0*D)*randn( length( input ), 1 )/sqrt(dt);
+        for i=1:length( noise )
+            n = n + ( - n + noise(i))*dt/outau;
+            v = v + ( - v + n + input(i))*dt/tau;
+            if v >= vthresh
+                v = vreset;
+                times(j) = i*dt;
+                j = j + 1;
+            end
+        end
+        spikes{k} = times;
+    end
+end
diff --git a/pointprocesses/code/lifrateisicorr.m b/pointprocesses/code/lifrateisicorr.m
new file mode 100644
index 0000000..6276e79
--- /dev/null
+++ b/pointprocesses/code/lifrateisicorr.m
@@ -0,0 +1,34 @@
+% relation between firing rate and serieller correlation
+
+input = 65.0;   % lifadapt 100Hz
+%input = 8.0;   % lifadapt 10Hz
+trials = 10;
+tmax = 50.0;
+noise = 1e-5;
+adapttau = 0.1;
+adaptincr = 0.5;
+
+clf;
+for adapttau = 0.01:0.02:0.2
+    inputs = 1:5:120;
+    iscs = zeros( size( inputs ) );
+    rates = zeros( size( inputs ) );
+    for k = 1:length(inputs)
+        input = inputs(k);
+        % generate spikes:
+        spikes = lifadaptspikes( trials, input, tmax, noise, adapttau, adaptincr );
+        isivec = isis( spikes );
+        isc = isiserialcorr( isivec, 10 );
+        iscs(k) = isc(2);
+        rates(k) = 1.0/mean( isivec );
+    end
+
+    subplot( 2, 1, 1 );
+    hold on;
+    plot( inputs, rates );
+    hold off;
+    subplot( 2, 1, 2 );
+    hold on;
+    plot( rates, iscs );
+    hold off;
+end
diff --git a/pointprocesses/code/lifspikes.m b/pointprocesses/code/lifspikes.m
new file mode 100644
index 0000000..cfa0f55
--- /dev/null
+++ b/pointprocesses/code/lifspikes.m
@@ -0,0 +1,38 @@
+function spikes = lifspikes( trials, input, tmaxdt, D )
+% Generate spike times of a leaky integrate-and-fire neuron
+% trials: the number of trials to be generated
+% input: the stimulus either as a single value or as a vector
+% tmaxdt: in case of a single value stimulus the duration of a trial
+%         in case of a vector as a stimulus the time step
+% D: the strength of additive white noise
+    
+    tau = 0.01;
+    if nargin < 4
+        D = 1e0;
+    end
+    vreset = 0.0;
+    vthresh = 10.0;
+    dt = 1e-4;
+    
+    if length( input ) == 1
+        input = input * ones( ceil( tmaxdt/dt ), 1 );
+    else
+        dt = tmaxdt;
+    end
+    spikes = cell( trials, 1 );
+    for k=1:trials
+        times = [];
+        j = 1;
+        v = vreset;
+        noise = sqrt(2.0*D)*randn( length( input ), 1 )/sqrt(dt);
+        for i=1:length( noise )
+            v = v + ( - v + noise(i) + input(i))*dt/tau;
+            if v >= vthresh
+                v = vreset;
+                times(j) = i*dt;
+                j = j + 1;
+            end
+        end
+        spikes{k} = times;
+    end
+end
diff --git a/pointprocesses/code/pifouspikes.m b/pointprocesses/code/pifouspikes.m
new file mode 100644
index 0000000..b6516cc
--- /dev/null
+++ b/pointprocesses/code/pifouspikes.m
@@ -0,0 +1,44 @@
+function spikes = pifouspikes( trials, input, tmaxdt, D, outau )
+% Generate spike times of a perfect integrate-and-fire neuron
+% trials: the number of trials to be generated
+% input: the stimulus either as a single value or as a vector
+% tmaxdt: in case of a single value stimulus the duration of a trial
+%         in case of a vector as a stimulus the time step
+% D: the strength of additive white noise
+% outau: time constant of the colored noise
+    
+    tau = 0.01;
+    if nargin < 4
+        D = 1e0;
+    end
+    if nargin < 5
+        outau = 1.0;
+    end
+    vreset = 0.0;
+    vthresh = 10.0;
+    dt = 1e-4;
+    
+    if length( input ) == 1
+        input = input * ones( ceil( tmaxdt/dt ), 1 );
+    else
+        dt = tmaxdt;
+    end
+    spikes = cell( trials, 1 );
+    for k=1:trials
+        times = [];
+        j = 1;
+        n = 0.0;
+        v = vreset;
+        noise = sqrt(2.0*D)*randn( length( input ), 1 )/sqrt(dt);
+        for i=1:length( noise )
+            n = n + ( - n + noise(i))*dt/outau;
+            v = v + ( n + input(i))*dt/tau;
+            if v >= vthresh
+                v = vreset;
+                times(j) = i*dt;
+                j = j + 1;
+            end
+        end
+        spikes{k} = times;
+    end
+end
diff --git a/pointprocesses/code/pifspikes.m b/pointprocesses/code/pifspikes.m
new file mode 100644
index 0000000..397658c
--- /dev/null
+++ b/pointprocesses/code/pifspikes.m
@@ -0,0 +1,38 @@
+function spikes = pifspikes( trials, input, tmaxdt, D )
+% Generate spike times of a perfect integrate-and-fire neuron
+% trials: the number of trials to be generated
+% input: the stimulus either as a single value or as a vector
+% tmaxdt: in case of a single value stimulus the duration of a trial
+%         in case of a vector as a stimulus the time step
+% D: the strength of additive white noise
+    
+    tau = 0.01;
+    if nargin < 4
+        D = 1e-1;
+    end
+    vreset = 0.0;
+    vthresh = 10.0;
+    dt = 1e-4;
+    
+    if length( input ) == 1
+        input = input * ones( ceil( tmaxdt/dt ), 1 );
+    else
+        dt = tmaxdt;
+    end
+    spikes = cell( trials, 1 );
+    for k=1:trials
+        times = [];
+        j = 1;
+        v = vreset;
+        noise = sqrt(2.0*D)*randn( length( input ), 1 )/sqrt(dt);
+        for i=1:length( noise )
+            v = v + ( noise(i) + input(i))*dt/tau;
+            if v >= vthresh
+                v = vreset;
+                times(j) = i*dt;
+                j = j + 1;
+            end
+        end
+        spikes{k} = times;
+    end
+end
diff --git a/pointprocesses/code/poissonisih.m b/pointprocesses/code/poissonisih.m
new file mode 100644
index 0000000..f006e2b
--- /dev/null
+++ b/pointprocesses/code/poissonisih.m
@@ -0,0 +1,27 @@
+rate = 100.0;
+trials = 50;
+tmax = 100.0;
+
+% generate spikes:
+spikes = poissonspikes( trials, rate, tmax );
+% interspike intervals:
+isivec = isis( spikes );
+% histogram
+f = figure( 1 );
+isihist( isivec );
+hold on
+% theoretical density:
+xmax = 5.0/rate;
+x = 0:0.0001:xmax;
+y = rate*exp(-rate*x);
+plot( 1000.0*x, y, 'r', 'LineWidth', 3 );
+% plot details:
+title( sprintf( 'Poisson spike trains, rate=%g Hz, nisi=%d', rate, length( isivec ) ) )
+xlim( [ 0.0 1000.0*xmax ] )
+ylim( [ 0.0 1.1*rate ] )
+legend( 'data', 'poisson' )
+hold off
+
+% serial correlations:
+f = figure( 2 );
+isiserialcorr( isivec, 10 );
diff --git a/pointprocesses/code/poissonisistats.m b/pointprocesses/code/poissonisistats.m
new file mode 100644
index 0000000..0c97e3d
--- /dev/null
+++ b/pointprocesses/code/poissonisistats.m
@@ -0,0 +1,46 @@
+rates = 1:1:100;
+avisi = [];
+sdisi = [];
+cvisi = [];
+
+for rate = rates
+    spikes = poissonspikes( 10, rate, 100.0 );
+    isivec = isis( spikes );
+    av = mean( isivec );
+    sd = std( isivec );
+    cv = sd/av;
+    avisi = [ avisi av ];
+    sdisi = [ sdisi sd ];
+    cvisi = [ cvisi cv ];
+end
+
+f = figure;
+subplot( 1, 3, 1 );
+scatter( rates, 1000.0*avisi, 'b', 'filled' );
+hold on;
+plot( rates, 1000.0./rates, 'r' );
+hold off;
+xlabel( 'Rate \lambda [Hz]' );
+ylim( [ 0 1000 ] );
+title( 'Mean ISI [ms]' );
+legend( 'simulation', 'theory 1/\lambda' );
+
+subplot( 1, 3, 2 );
+scatter( rates, 1000.0*sdisi, 'b', 'filled' );
+hold on;
+plot( rates, 1000.0./rates, 'r' );
+hold off;
+xlabel( 'Rate \lambda [Hz]' );
+ylim( [ 0 1000 ] )
+title( 'Standard deviation ISI [ms]' );
+legend( 'simulation', 'theory 1/\lambda' );
+
+subplot( 1, 3, 3 );
+scatter( rates, cvisi, 'b', 'filled' );
+hold on;
+plot( rates, ones( size( rates ) ), 'r' );
+hold off;
+xlabel( 'Rate \lambda [Hz]' );
+ylim( [ 0 2 ] )
+title( 'CV' );
+legend( 'simulation', 'theory' );
diff --git a/pointprocesses/code/poissonspikes.m b/pointprocesses/code/poissonspikes.m
new file mode 100644
index 0000000..f00291c
--- /dev/null
+++ b/pointprocesses/code/poissonspikes.m
@@ -0,0 +1,20 @@
+function spikes = poissonspikes( trials, rate, tmax )
+% Generate spike times of a homogeneous poisson process
+% trials: number of trials that should be generated
+% rate: the rate of the Poisson process in Hertz
+% tmax: the duration of each trial in seconds
+% returns a cell array of vectors of spike times
+
+    dt = 3.33e-5;
+    p = rate*dt; % probability of event per bin of width dt
+    % make sure p is small enough:
+    if p > 0.1
+        p = 0.1
+        dt = p/rate;
+    end
+    spikes = cell( trials, 1 );
+    for k=1:trials
+        x = rand( 1, round(tmax/dt) ); % uniform random numbers for each bin
+        spikes{k} = find( x < p ) * dt;
+    end
+end
diff --git a/pointprocesses/code/raster.pdf b/pointprocesses/code/raster.pdf
new file mode 100644
index 0000000..a20fa0e
Binary files /dev/null and b/pointprocesses/code/raster.pdf differ
diff --git a/pointprocesses/code/savefigpdf.m b/pointprocesses/code/savefigpdf.m
new file mode 100644
index 0000000..fbe2dc2
--- /dev/null
+++ b/pointprocesses/code/savefigpdf.m
@@ -0,0 +1,28 @@
+function savefigpdf( fig, name, width, height )
+% Saves figure fig in pdf file name.pdf with appropriately set page size
+% and fonts
+
+% default width:
+if nargin < 3
+    width = 11.7;
+end
+% default height:
+if nargin < 4
+    height = 9.0;
+end
+
+% paper:
+set( fig, 'PaperUnits', 'centimeters' );
+set( fig, 'PaperSize', [width height] );
+set( fig, 'PaperPosition', [0.0 0.0 width height] );
+set( fig, 'Color', 'white')
+
+% font:
+set( findall( fig, 'type', 'axes' ), 'FontSize', 12 )
+set( findall( fig, 'type', 'text' ), 'FontSize', 12 )
+
+% save:
+saveas( fig, name, 'pdf' )
+
+end
+
diff --git a/pointprocesses/code/spikeraster.m b/pointprocesses/code/spikeraster.m
new file mode 100644
index 0000000..f7ff5d4
--- /dev/null
+++ b/pointprocesses/code/spikeraster.m
@@ -0,0 +1,17 @@
+function spikeraster( spikes )
+% Display a spike raster of the spike times given in spikes.
+% spikes: a cell array of vectors of spike times
+
+ntrials = length(spikes);
+for k = 1:ntrials
+    times = 1000.0*spikes{k};  % conversion to ms
+    for i = 1:length( times )
+      line([times(i) times(i)],[k-0.4 k+0.4], 'Color', 'k' ); 
+    end
+end
+xlabel( 'Time [ms]' );
+ylabel( 'Trials');
+ylim( [ 0.3 ntrials+0.7 ] )
+
+end
+
diff --git a/pointprocesses/exercises/hompoissonisih.m b/pointprocesses/exercises/hompoissonisih.m
new file mode 100644
index 0000000..cd2bc06
--- /dev/null
+++ b/pointprocesses/exercises/hompoissonisih.m
@@ -0,0 +1,18 @@
+% generate spike times:
+rate = 20.0;
+spikes = hompoissonspikes( 10, rate, 50.0 );
+% isi histogram:
+isivec = isis( spikes );
+isihist( isivec );
+hold on
+% theoretical density:
+xmax = 5.0/rate;
+x = 0:0.0001:xmax;
+y = rate*exp(-rate*x);
+plot( 1000.0*x, y, 'r', 'LineWidth', 3 );
+% plot details:
+title( sprintf( 'Poisson spike trains, rate=%g Hz, nisi=%d', rate, length( isivec ) ) )
+xlim( [ 0.0 1000.0*xmax ] )
+ylim( [ 0.0 1.1*rate ] )
+legend( 'data', 'poisson' )
+hold off
diff --git a/pointprocesses/exercises/hompoissonisistats.m b/pointprocesses/exercises/hompoissonisistats.m
new file mode 100644
index 0000000..7ca3cf3
--- /dev/null
+++ b/pointprocesses/exercises/hompoissonisistats.m
@@ -0,0 +1,46 @@
+rates = 1:1:100;
+avisi = [];
+sdisi = [];
+cvisi = [];
+
+for rate = rates
+    spikes = hompoissonspikes( 10, rate, 100.0 );
+    isivec = isis( spikes );
+    av = mean( isivec );
+    sd = std( isivec );
+    cv = sd/av;
+    avisi = [ avisi av ];
+    sdisi = [ sdisi sd ];
+    cvisi = [ cvisi cv ];
+end
+
+f = figure;
+subplot( 1, 3, 1 );
+scatter( rates, 1000.0*avisi, 'b', 'filled' );
+hold on;
+plot( rates, 1000.0./rates, 'r' );
+hold off;
+xlabel( 'Rate \lambda [Hz]' );
+ylim( [ 0 1000 ] );
+title( 'Mean ISI [ms]' );
+legend( 'simulation', 'theory 1/\lambda' );
+
+subplot( 1, 3, 2 );
+scatter( rates, 1000.0*sdisi, 'b', 'filled' );
+hold on;
+plot( rates, 1000.0./rates, 'r' );
+hold off;
+xlabel( 'Rate \lambda [Hz]' );
+ylim( [ 0 1000 ] )
+title( 'Standard deviation ISI [ms]' );
+legend( 'simulation', 'theory 1/\lambda' );
+
+subplot( 1, 3, 3 );
+scatter( rates, cvisi, 'b', 'filled' );
+hold on;
+plot( rates, ones( size( rates ) ), 'r' );
+hold off;
+xlabel( 'Rate \lambda [Hz]' );
+ylim( [ 0 2 ] )
+title( 'CV' );
+legend( 'simulation', 'theory' );
diff --git a/pointprocesses/exercises/hompoissonspikes.m b/pointprocesses/exercises/hompoissonspikes.m
new file mode 100644
index 0000000..12af153
--- /dev/null
+++ b/pointprocesses/exercises/hompoissonspikes.m
@@ -0,0 +1,19 @@
+function spikes = hompoissonspikes( trials, rate, tmax )
+% Generate spike times of a homogeneous poisson process
+% trials: number of trials that should be generated
+% rate: the rate of the Poisson process in Hertz
+% tmax: the duration of each trial in seconds
+% returns a cell array of vectors of spike times
+
+    dt = 3.33e-5;
+    p = rate*dt;
+    if p > 0.2
+        p = 0.2
+        dt = p/rate;
+    end
+    x = rand( trials, ceil(tmax/dt) );
+    spikes = cell( trials, 1 );
+    for k=1:trials
+        spikes{k} = find( x(k,:) >= 1.0-p ) * dt;
+    end
+end
diff --git a/pointprocesses/exercises/iafisistats-solutions.pdf b/pointprocesses/exercises/iafisistats-solutions.pdf
new file mode 100644
index 0000000..d1b6c63
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diff --git a/pointprocesses/exercises/iafisistats.pdf b/pointprocesses/exercises/iafisistats.pdf
new file mode 100644
index 0000000..c19617b
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diff --git a/pointprocesses/exercises/iafisistats.tex b/pointprocesses/exercises/iafisistats.tex
new file mode 100644
index 0000000..53ca479
--- /dev/null
+++ b/pointprocesses/exercises/iafisistats.tex
@@ -0,0 +1,142 @@
+\documentclass[addpoints,10pt]{exam}
+\usepackage{url}
+\usepackage{color}
+\usepackage{hyperref}
+\usepackage{graphicx}
+
+\pagestyle{headandfoot}
+\runningheadrule
+\firstpageheadrule
+
+\firstpageheader{Scientific Computing}{Integrate-and-fire models}{Oct 28, 2014}
+%\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
+\firstpagefooter{}{}{}
+\runningfooter{}{}{}
+\pointsinmargin
+\bracketedpoints
+
+%\printanswers
+\shadedsolutions
+
+\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
+
+%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{listings}
+\lstset{
+ basicstyle=\ttfamily,
+ numbers=left,
+ showstringspaces=false,
+ language=Matlab,
+ breaklines=true,
+ breakautoindent=true,
+ columns=flexible,
+ frame=single,
+ captionpos=t,
+ xleftmargin=2em,
+ xrightmargin=1em,
+ aboveskip=10pt,
+ %title=\lstname,
+ title={\protect\filename@parse{\lstname}\protect\filename@base.\protect\filename@ext}
+ }
+
+
+\begin{document}
+
+\sffamily
+%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
+
+\begin{questions}
+  \question \textbf{Statistics of integrate-and-fire neurons}
+  For the following use different variants of the leaky integrate-and-fire models provided in \texttt{lifspikes.m}, 
+  \texttt{lifouspikes.m}, and \texttt{lifadaptspikes.m} do generate some spike train data.
+  Use the functions you wrote for the Poisson process to analyze the statistics of the spike trains.
+  \begin{parts}
+    \part Generate a few trials of the two models for two different inputs
+    that result in qualitatively different spike trains and display
+    them in a raster plot. Decide for a noise strength (good values to try are 0.001, 0.01, 0.1, 1).
+    \begin{solution}
+      \begin{lstlisting}
+spikes = pifspikes( 10, 1.0, 0.5, 0.01 );
+%spikes = pifspikes( 10, 10.0, 0.5, 0.01 );
+%spikes = lifspikes( 10, 11.0, 0.5, 0.001 );
+%spikes = lifspikes( 10, 15.0, 0.5, 0.001 );
+spikeraster( spikes )
+      \end{lstlisting}
+      \mbox{}\\[-3ex]
+      \colorbox{white}{\includegraphics[width=0.48\textwidth]{pifraster02}}
+      \colorbox{white}{\includegraphics[width=0.48\textwidth]{pifraster10}}\\
+      \colorbox{white}{\includegraphics[width=0.48\textwidth]{lifraster10}}
+      \colorbox{white}{\includegraphics[width=0.48\textwidth]{lifraster15}}
+    \end{solution}
+
+
+    \part The inverse Gaussian describes the interspike interval distribution of a PIF driven with white noise:
+    \[ p(T) = \frac{1}{\sqrt{4\pi D T^3}}\exp\left[-\frac{(T-\langle T \rangle)^2}{4DT\langle T \rangle^2}\right] \]
+    where $\langle T \rangle$ is the mean interspike interval and 
+    \[ D = \frac{\langle(T - \langle T \rangle)^2\rangle}{2 \langle T \rangle^3} \]
+    is the diffusion coefficient (variance of the interspike intervals
+    $T$ divided by two times the mean cubed). Show in two plots how
+    this distribution depends on $\langle T \rangle$ and $D$.
+    \begin{solution}
+      \lstinputlisting{simulations/inversegauss.m}
+      \lstinputlisting{simulations/inversegaussplot.m}
+      \colorbox{white}{\includegraphics[width=0.98\textwidth]{inversegauss}}
+    \end{solution}
+
+    \part Extent your function plotting an interspike interval histogram
+    to also report the diffusion coefficient $D$.
+    \begin{solution}
+      \begin{lstlisting}
+...
+% annotation:
+misi = mean( isis );
+sdisi = std( isis );
+disi = sdisi^2.0/2.0/misi^3;
+text( 0.6, 0.7, sprintf( 'mean=%.1f ms', 1000.0*misi ), 'Units', 'normalized' )
+text( 0.6, 0.6, sprintf( 'std=%.1f ms', 1000.0*sdisi ), 'Units', 'normalized' )
+text( 0.6, 0.5, sprintf( 'CV=%.2f', sdisi/misi ), 'Units', 'normalized' )
+text( 0.6, 0.4, sprintf( 'D=%.1f Hz', disi ), 'Units', 'normalized' )
+...
+      \end{lstlisting}
+    \end{solution}
+
+    \part Compare intersike interval histograms obtained from the LIF and PIF models with the inverse Gaussian.
+    \begin{solution}
+      \lstinputlisting{simulations/lifisih.m}
+      \colorbox{white}{\includegraphics[width=0.48\textwidth]{pifisih01}}
+      \colorbox{white}{\includegraphics[width=0.48\textwidth]{pifisih10}}\\
+      \colorbox{white}{\includegraphics[width=0.48\textwidth]{lifisih08}}
+      \colorbox{white}{\includegraphics[width=0.48\textwidth]{lifisih16}}
+    \end{solution}
+
+    \part Plot the firing rate (inverse mean interspike interval),
+    mean interspike interval, the corresponding standard deviation,
+    CV, and diffusion coefficient as a function of the input to the LIF
+    and the PIF with noise strength set to 0.01.
+    \begin{solution}
+      \lstinputlisting{simulations/lifisistats.m}
+      Leaky integrate-and-fire:\\
+      \colorbox{white}{\includegraphics[width=0.8\textwidth]{lifisistats}}\\
+      Perfect integrate-and-fire:\\
+      \colorbox{white}{\includegraphics[width=0.8\textwidth]{pifisistats}}
+    \end{solution}
+
+    \part Plot the firing rate as a function of input of the LIF and the PIF for various values
+    of the noise strength.
+    \begin{solution}
+      \lstinputlisting{simulations/lifficurves.m}
+      Leaky integrate-and-fire:\\
+      \colorbox{white}{\includegraphics[width=0.7\textwidth]{lifficurves}}\\
+      Perfect integrate-and-fire:\\
+      \colorbox{white}{\includegraphics[width=0.7\textwidth]{pifficurves}}
+    \end{solution}
+
+    \part Use the functions for computing serial correlations, count statistics and fano factors
+    to further explore the statistics of the integrate-and-fire models!
+
+  \end{parts}
+  
+\end{questions}
+
+
+\end{document}
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+\documentclass[addpoints,10pt]{exam}
+\usepackage{url}
+\usepackage{color}
+\usepackage{hyperref}
+\usepackage{graphicx}
+
+\pagestyle{headandfoot}
+\runningheadrule
+\firstpageheadrule
+
+\firstpageheader{Scientific Computing}{Homogeneous Poisson process}{Oct 27, 2014}
+%\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
+\firstpagefooter{}{}{}
+\runningfooter{}{}{}
+\pointsinmargin
+\bracketedpoints
+
+%\printanswers
+\shadedsolutions
+
+\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
+
+%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{listings}
+\lstset{
+ basicstyle=\ttfamily,
+ numbers=left,
+ showstringspaces=false,
+ language=Matlab,
+ breaklines=true,
+ breakautoindent=true,
+ columns=flexible,
+ frame=single,
+ captionpos=t,
+ xleftmargin=2em,
+ xrightmargin=1em,
+ aboveskip=10pt,
+ %title=\lstname,
+ title={\protect\filename@parse{\lstname}\protect\filename@base.\protect\filename@ext}
+ }
+
+
+\begin{document}
+
+\sffamily
+%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
+
+\begin{questions}
+  \question \textbf{Homogeneous Poisson process}
+  We use the Poisson process to generate spike trains on which we can test and imrpove some 
+  standard analysis functions.
+
+  A homogeneous Poisson process of rate $\lambda$ (measured in Hertz) is a point process
+  where the probability of an event is independent of time $t$ and independent of previous events.
+  The probability $P$ of an event within a bin of width $\Delta t$ is 
+  \[ P = \lambda \cdot \Delta t \]
+  for sufficiently small $\Delta t$.
+  \begin{parts}
+
+    \part Write a function that generates $n$ homogeneous Poisson spike trains of a given duration $T_{max}$
+    with rate $\lambda$.
+    \begin{solution}
+      \lstinputlisting{hompoissonspikes.m}
+    \end{solution}
+
+    \part Using this function, generate a few trials and display them in a raster plot.
+    \begin{solution}
+      \lstinputlisting{simulations/spikeraster.m}
+      \begin{lstlisting}
+spikes = hompoissonspikes( 10, 100.0, 0.5 );
+spikeraster( spikes )
+      \end{lstlisting}
+      \mbox{}\\[-3ex]
+      \colorbox{white}{\includegraphics[width=0.7\textwidth]{poissonraster100hz}}
+    \end{solution}
+
+    \part Write a function that extracts a single vector of interspike intervals
+    from the spike times returned by the first function.
+    \begin{solution}
+      \lstinputlisting{simulations/isis.m}
+    \end{solution}
+
+    \part Write a function that plots the interspike-interval histogram
+    from a vector of interspike intervals. The function should also
+    compute the mean, the standard deviation, and the CV of the intervals
+    and display the values in the plot.
+    \begin{solution}
+      \lstinputlisting{simulations/isihist.m}
+    \end{solution}
+
+    \part Compute histograms for Poisson spike trains with rate
+    $\lambda=100$\,Hz. Play around with $T_{max}$ and $n$ and the bin width
+    (start with 1\,ms) of the histogram.
+    How many
+    interspike intervals do you approximately need to get a ``nice''
+    histogram? How long do you need to record from the neuron?
+    \begin{solution}
+      About 5000 intervals for 25 bins. This corresponds to a $5000 / 100\,\hertz = 50\,\second$ recording
+      of a neuron firing with 100\,\hertz.
+    \end{solution}
+
+    \part Compare the histogram with the true distribution of intervals $T$ of the Poisson process
+    \[ p(T) = \lambda e^{-\lambda T} \]
+    for various rates $\lambda$.
+    \begin{solution}
+      \lstinputlisting{hompoissonisih.m}
+      \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissonisih100hz}}
+      \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissonisih20hz}}
+    \end{solution}
+
+    \part What happens if you make the bin width of the histogram smaller than $\Delta t$ 
+    used for generating the Poisson spikes?
+    \begin{solution}
+      The bins between the discretization have zero entries. Therefore
+      the other ones become higher than they should be.
+    \end{solution}
+
+    \part Plot the mean interspike interval, the corresponding standard deviation, and the CV
+    as a function of the rate $\lambda$ of the Poisson process.
+    Compare the simulations with the theoretical expectations for the dependence on $\lambda$.
+    \begin{solution}
+      \lstinputlisting{hompoissonisistats.m}
+      \colorbox{white}{\includegraphics[width=0.98\textwidth]{poissonisistats}}
+    \end{solution}
+
+    \part Write a function that computes serial correlations for the interspike intervals
+    for a range of lags.
+    The serial correlations $\rho_k$ at lag $k$ are defined as
+    \[ \rho_k = \frac{\langle (T_{i+k} - \langle T \rangle)(T_i - \langle T \rangle) \rangle}{\langle (T_i - \langle T \rangle)^2\rangle} = \frac{{\rm cov}(T_{i+k}, T_i)}{{\rm var}(T_i)} \]
+    Use this function to show that interspike intervals of Poisson spikes are independent.
+    \begin{solution}
+      \lstinputlisting{simulations/isiserialcorr.m}
+      \colorbox{white}{\includegraphics[width=0.98\textwidth]{poissonserial100hz}}
+    \end{solution}
+
+    \part Write a function that generates from spike times 
+    a histogram of spike counts in a count window of given duration $W$.
+    The function should also plot the Poisson distribution
+    \[ P(k) = \frac{(\lambda W)^ke^{\lambda W}}{k!} \]
+    for the rate $\lambda$ determined from the spike trains.
+    \begin{solution}
+      \lstinputlisting{simulations/counthist.m}
+      \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz10ms}}
+      \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz100ms}}
+    \end{solution}
+
+    \part Write a function that computes mean count, variance of count and the corresponding Fano factor
+    for a range of count window durations. The function should generate tow plots: one plotting
+    the count variance against the mean, the other one the Fano factor as a function of the window duration.
+    \begin{solution}
+      \lstinputlisting{simulations/fano.m}
+      \colorbox{white}{\includegraphics[width=0.98\textwidth]{poissonfano100hz}}
+    \end{solution}
+
+  \end{parts}
+  
+\end{questions}
+
+
+\end{document}
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diff --git a/pointprocesses/lecture/Makefile b/pointprocesses/lecture/Makefile
new file mode 100644
index 0000000..7a243a5
--- /dev/null
+++ b/pointprocesses/lecture/Makefile
@@ -0,0 +1,142 @@
+BASENAME=pointprocesses
+
+TEXFILE=$(BASENAME).tex
+DVIFILE=$(BASENAME).dvi
+PSFILE=$(BASENAME).ps
+PDFFILE=$(BASENAME).pdf
+
+FOILSFILE=foils.pdf
+THUMBNAILSFILE=thumbnails.pdf
+
+HTMLBASENAME=$(BASENAME)h
+HTMLTEXFILE=$(BASENAME)h.tex
+HTMLDIR=$(BASENAME)h
+
+GPTFILES=$(wildcard *.gpt)
+GPTTEXFILES=$(GPTFILES:.gpt=.tex)
+
+
+all: ps pdf talk again watchps watchpdf foils thumbs html html1 epsfigs clean cleanup cleanplots help
+.PHONY: epsfigs
+
+
+# thumbnails:
+thumbs: $(THUMBNAILSFILE)
+$(THUMBNAILSFILE): $(TEXFILE) $(GPTTEXFILES)
+	sed -e 's/setboolean{presentation}{true}/setboolean{presentation}{false}/; s/usepackage{crop}/usepackage[frame]{crop}/' $< > thumbsfoils.tex
+	pdflatex thumbsfoils | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex thumbsfoils || true
+	pdfnup --nup 2x4 --no-landscape --paper a4paper --trim "-1cm -1cm -1cm -1cm" --outfile $@ thumbsfoils.pdf '1-19'
+	rm thumbsfoils.*
+
+# transparencies:
+foils: $(FOILSFILE)
+$(FOILSFILE): $(TEXFILE) $(GPTTEXFILES)
+	sed -e 's/setboolean{presentation}{true}/setboolean{presentation}{false}/' $< > tfoils.tex
+	pdflatex tfoils | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex tfoils || true
+	pdfnup --nup 1x2 --orient portrait --trim "-1mm -1mm -1mm -1mm" --frame true --delta "1cm 1cm" --paper a4paper --outfile tfoils2.pdf tfoils.pdf
+	pdfnup --nup 1x1 --orient portrait --trim "-2cm -2cm -2cm -2cm" --paper a4paper --outfile $@ tfoils2.pdf 
+	rm tfoils.* tfoils2.pdf
+
+# talk:
+talk: $(PDFFILE)
+pdf: $(PDFFILE)
+$(PDFFILE): $(TEXFILE) $(GPTTEXFILES)
+	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
+# batchmode (no output, no stop on error)
+# nonstopmode / scrollmode (no stop on error)
+# errorstopmode (stop on error)
+
+
+again :
+	pdflatex $(TEXFILE)
+
+watchpdf :
+	while true; do ! make -q pdf && make pdf; sleep 0.5; done
+
+# html
+html : $(HTMLTEXFILE) $(GPTTEXFILES)
+	rm -f $(HTMLDIR)/*
+	htlatex $<
+	mkdir -p $(HTMLDIR)
+	mv $(HTMLBASENAME).html $(HTMLDIR)
+	mv $(HTMLBASENAME)*.* $(HTMLDIR)
+	mv z*.gif $(HTMLDIR)
+	cd $(HTMLDIR); for i in *.gif; do convert -page +0+0 $$i tmp.gif; mv tmp.gif $$i; done; rmtex $(HTMLBASENAME)
+
+#$(HTMLTEXFILE) : $(TEXFILE) Makefile
+#	sed 's/setboolean{html}{false}/setboolean{html}{true}/; s/\\colorbox{white}{\(.*\)}/\1/g' $< > $@
+
+html1 : $(HTMLTEXFILE) $(GPTTEXFILES)
+	latex2html -dir $(HTMLDIR) -mkdir -subdir -nonavigation -noinfo -image_type png -notransparent -white -split 0 $<
+	sed 's-<I>Date:</I>--' $(HTMLDIR)/$(HTMLDIR).html > tmp.html
+	cp tmp.html $(HTMLDIR)/index.html
+	mv tmp.html $(HTMLDIR)/$(HTMLDIR).html
+
+$(HTMLTEXFILE) : $(TEXFILE)
+	sed '/^%nohtml/,/^%endnohtml/d; s/\\colorbox{white}{\(.*\)}/\1/g' $< > $@
+
+
+# eps of all figures:
+epsfigs:
+	mkdir -p epsfigs; \
+	for i in $(GPTFILES); do \
+	  { sed -n -e '1,/\\begin{document}/p' $(TEXFILE); echo "\texpicture{$${i%%.*}}"; echo "\end{document}"; } > tmp.tex; \
+	  latex tmp.tex; \
+	  dvips tmp.dvi; \
+	  ps2eps tmp.ps; \
+	  mv tmp.eps epsfigs/$${i%%.*}.eps; \
+	  rm tmp.*; \
+	done
+
+
+# plots:
+%.tex: %.gpt whitestyles.gp
+	gnuplot whitestyles.gp $<
+	epstopdf $*.eps
+
+
+clean :
+	rm -f *~
+	rmtex $(BASENAME)
+	rm -f $(GPTTEXFILES)
+
+cleanup :
+	rm -f *~
+	rmtex $(BASENAME)
+	rm -f $(PSFILE) $(PDFFILE) $(FOILSFILE) $(THUMBNAILSFILE)
+	rm -f $(GPTTEXFILES)
+	rm -f -r $(HTMLDIR)
+
+
+cleanplots :
+	sed -n -e '/\\begin{document}/,/\\end{document}/p' $(TEXFILE) | fgrep '\input{' | grep -v '^%' | sed 's/.*input{\(.*\).tex}.*/\1.gpt/' > plot.fls
+	mkdir -p unusedplots
+	for i in *.gp*; do \
+	  grep -q $$i plot.fls || { grep -q $$i $$(<plot.fls) && echo $$i || mv $$i unusedplots; }; \
+	done >> plot.fls
+	for i in $$(<plot.fls); do \
+	  sed "s/\([^'\" ]*\.dat\)/\n\1\n/g;" $$i | fgrep .dat; \
+	done | sort | uniq > dat.fls
+	mkdir -p unuseddata 
+	for i in *.dat; do \
+	  grep -q $$i dat.fls || mv $$i unuseddata; \
+	done
+	rm dat.fls plot.fls
+
+
+help : 
+	@echo -e \
+	"make pdf:    make the pdf file of the talk.\n"\
+	"make foils:  make black&white postscript foils of the talk.\n"\
+	"make thumbs: make color thumbnails of the talk.\n"\
+	"make again: run latex and make the pdf file of the talk,\n"\
+        "            no matter whether you changed the .tex file or not.\n\n"\
+	"make watchpdf:   make the pdf file of the talk\n"\
+        "                 whenever the tex file is modified.\n"\
+	"make html:       make a html version of the paper (in $(HTMLDIR)).\n\n"\
+	"make clean:      remove all intermediate files,\n"\
+        "                 just leave the source files and the final .ps and .pdf files.\n"\
+	"make cleanup:    remove all intermediate files as well as\n"\
+        "                 the final .ps and .pdf files.\n"\
+	"make cleanplots: move all unused .gpt and .dat files\n"\
+        "                 into unusedplots/ and unuseddata/, respectively."
diff --git a/pointprocesses/lecture/UT_WBMW_Rot_RGB.pdf b/pointprocesses/lecture/UT_WBMW_Rot_RGB.pdf
new file mode 100644
index 0000000..f54eedc
--- /dev/null
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+\documentclass{beamer}
+
+%%%%% title %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\title[]{Scientific Computing --- Point Processes}
+\author[]{Jan Benda}
+\institute[]{Neuroethology}
+\date[]{WS 14/15}
+\titlegraphic{\includegraphics[width=0.3\textwidth]{UT_WBMW_Rot_RGB}}
+
+%%%%% beamer %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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+%%%%% packages %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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+%%%% graphics %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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+
+%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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+ 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{document} 
+
+\begin{frame}[plain]
+  \frametitle{}
+  \vspace{-1cm}
+  \titlepage % erzeugt Titelseite
+\end{frame}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{frame}
+  \frametitle{Content}
+  \tableofcontents
+\end{frame}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Point processes}
+
+\begin{frame}
+  \frametitle{Point process}
+  \vspace{-3ex}
+  \texpicture{pointprocessscetchA}
+
+  A point process is a stochastic (or random) process that generates a sequence of events
+  at times $\{t_i\}$, $t_i \in \reZ$.
+
+  For each point process there is an underlying continuous-valued
+  process evolving in time. The associated point process occurs when
+  the underlying continuous process crosses a threshold.
+  Examples:
+  \begin{itemize}
+  \item Spikes/heartbeat: generated by the dynamics of the membrane potential of neurons/heart cells.
+  \item Earth quakes: generated by the pressure dynamics between the tectonic plates on either side of a geological fault line.
+  \item Onset of cricket/frogs/birds/... songs: generated by the dynamics of the state of a nervous system.
+  \end{itemize}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Point process}
+  \texpicture{pointprocessscetchB}
+\end{frame}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Homogeneous Poisson process}
+
+\begin{frame}
+  \frametitle{Homogeneous Poisson process}
+  The probability $p(t)\delta t$ of an event occuring at time $t$
+  is independent of $t$ and independent of any previous event
+  (independent of event history).
+
+  The probability $P$ for an event occuring within a time bin of width $\Delta t$
+  is
+  \[ P=\lambda \cdot \Delta t \]
+  for a Poisson process with rate $\lambda$.
+  \includegraphics[width=1\textwidth]{poissonraster100hz}
+\end{frame}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Interval statistics}
+
+\begin{frame}
+  \frametitle{Rate}
+  Rate of events $r$ (``spikes per time'') measured in Hertz.
+  \begin{itemize}
+  \item Number of events $N$ per observation time $W$: $r = \frac{N}{W}$
+  \item Without boundary effects: $r = \frac{N-1}{t_N-t_1}$
+  \item Inverse interval: $r = \frac{1}{\mu_{ISI}}$
+  \end{itemize}
+\end{frame}
+
+\begin{frame}
+  \frametitle{(Interspike) interval statistics}
+  \begin{itemize}
+  \item Histogram $p(T)$ of intervals $T$. Normalized to $\int_0^{\infty} p(T) \; dT = 1$
+  \item Mean interval $\mu_{ISI} = \langle T \rangle = \frac{1}{n}\sum\limits_{i=1}^n T_i$
+  \item Variance of intervals $\sigma_{ISI}^2 = \langle (T - \langle T \rangle)^2 \rangle$\vspace{1ex}
+  \item Coefficient of variation $CV_{ISI} = \frac{\sigma_{ISI}}{\mu_{ISI}}$
+  \item Diffusion coefficient $D_{ISI} = \frac{\sigma_{ISI}^2}{2\mu_{ISI}^3}$
+  \vfill
+  \end{itemize}
+  \includegraphics[width=0.45\textwidth]{poissonisih100hz}\hfill
+  \includegraphics[width=0.45\textwidth]{lifisih16}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Interval statistics of homogeneous Poisson process}
+  \begin{itemize}
+  \item Exponential distribution of intervals $T$: $p(T) = \lambda e^{-\lambda T}$
+  \item Mean interval $\mu_{ISI} = \frac{1}{\lambda}$
+  \item Variance of intervals $\sigma_{ISI}^2 = \frac{1}{\lambda^2}$
+  \item Coefficient of variation $CV_{ISI} = 1$
+  \end{itemize}
+  \vfill
+  \includegraphics[width=0.45\textwidth]{poissonisihexp20hz}\hfill
+  \includegraphics[width=0.45\textwidth]{poissonisihexp100hz}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Interval return maps}
+  Scatter plot between succeeding intervals separated by lag $k$.
+  \vfill
+  Poisson process $\lambda=100$\,Hz:
+  \includegraphics[width=1\textwidth]{poissonreturnmap100hz}\hfill
+\end{frame}
+
+\begin{frame}
+  \frametitle{Serial interval correlations}
+  Correlation coefficients between succeeding intervals separated by lag $k$:
+  \[ \rho_k = \frac{\langle (T_{i+k} - \langle T \rangle)(T_i - \langle T \rangle) \rangle}{\langle (T_i - \langle T \rangle)^2\rangle} = \frac{{\rm cov}(T_{i+k}, T_i)}{{\rm var}(T_i)} \]
+  \begin{itemize}
+  \item $\rho_0=1$ (correlation of each interval with itself).
+  \item Poisson process: $\rho_k =0$ for $k>0$ (renewal process!) 
+  \end{itemize}
+  \vfill
+  \includegraphics[width=0.7\textwidth]{poissonserial100hz}
+\end{frame}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Count statistics}
+
+\begin{frame}
+  \frametitle{Count statistics}
+  Histogram of number of events $N$ (counts) within observation window of duration $W$.
+
+  \vfill
+  \includegraphics[width=0.48\textwidth]{poissoncounthist100hz10ms}\hfill
+  \includegraphics[width=0.48\textwidth]{poissoncounthist100hz100ms}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Count statistics of Poisson process}
+  Poisson distribution:
+  \[ P(k) = \frac{(\lambda W)^ke^{\lambda W}}{k!} \]
+
+  \vfill
+  \includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz10ms}\hfill
+  \includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz100ms}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Count statistics --- Fano factor}
+  Statistics of number of events $N$ within observation window of duration $W$.
+  \begin{itemize}
+  \item Mean count: $\mu_N = \langle N \rangle$
+  \item Count variance: $\sigma_N^2 = \langle (N - \langle N \rangle)^2 \rangle$
+  \item Fano factor (variance divided by mean): $F = \frac{\sigma_N^2}{\mu_N}$
+  \item Poisson process: $F=1$
+  \end{itemize}
+  \vfill
+  Poisson process $\lambda=100$\,Hz:
+  \includegraphics[width=1\textwidth]{poissonfano100hz}
+\end{frame}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Integrate-and-fire models}
+
+\begin{frame}
+  \frametitle{Integrate-and-fire models}
+  Leaky integrate-and-fire model (LIF):
+  \[ \tau \frac{dV}{dt} = -V + RI + D\xi \]
+  Whenever membrane potential $V(t)$ crosses the firing threshold $\theta$, a spike is emitted and
+  $V(t)$ is reset to $V_{reset}$.
+  \begin{itemize}
+  \item $\tau$: membrane time constant (typically 10\,ms)
+  \item $R$: input resistance (here 1\,mV (!))
+  \item $D\xi$: additive Gaussian white noise of strength $D$
+  \item $\theta$: firing threshold (here 10\,mV)
+  \item $V_{reset}$: reset potential (here 0\,mV)
+  \end{itemize}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Integrate-and-fire models}
+  Discretization with time step $\Delta t$: $V(t) \rightarrow V_i,\;t_i = i \Delta t$.\\
+  Euler integration:
+  \begin{eqnarray*}
+    \frac{dV}{dt} & \approx & \frac{V_{i+1} - V_i}{\Delta t} \\
+    \Rightarrow \quad V_{i+1} & = & V_i + \Delta t \frac{-V_i+RI_i+\sqrt{2D\Delta t}N_i}{\tau}
+  \end{eqnarray*}
+  $N_i$ are normally distributed random numbers (Gaussian with zero mean and unit variance)
+  --- the $\sqrt{\Delta t}$ is for white noise.
+
+  \includegraphics[width=0.82\textwidth]{lifraster16}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Interval statistics of LIF}
+  Interval distribution approaches Inverse Gaussian for large $I$:
+  \[ p(T) = \frac{1}{\sqrt{4\pi D T^3}}\exp\left[-\frac{(T-\langle T \rangle)^2}{4DT\langle T \rangle^2}\right] \]
+  where $\langle T \rangle$ is the mean interspike interval and $D$
+  is the diffusion coefficient.
+  \vfill
+  \includegraphics[width=0.45\textwidth]{lifisihdistr08}\hfill
+  \includegraphics[width=0.45\textwidth]{lifisihdistr16}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Interval statistics of PIF}
+  For the perfect integrate-and-fire (PIF)
+  \[ \tau \frac{dV}{dt} =  RI + D\xi \]
+  (the canonical model or supra-threshold firing on a limit cycle)\\
+  the Inverse Gaussian describes exactly the interspike interval distribution.
+  \vfill
+  \includegraphics[width=0.45\textwidth]{pifisihdistr01}\hfill
+  \includegraphics[width=0.45\textwidth]{pifisihdistr10}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Interval return map of LIF}
+  LIF $I=15.7$:
+  \includegraphics[width=1\textwidth]{lifreturnmap16}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Serial correlations of LIF}
+  LIF $I=15.7$:
+  \includegraphics[width=1\textwidth]{lifserial16}\\
+  Integrate-and-fire driven with white noise are still renewal processes!
+\end{frame}
+
+\begin{frame}
+  \frametitle{Count statistics of LIF}
+  LIF $I=15.7$:
+  \includegraphics[width=1\textwidth]{liffano16}\\
+  Fano factor is not one!
+\end{frame}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{frame}
+  \frametitle{Interval statistics of LIF with OU noise}
+  \begin{eqnarray*}
+    \tau \frac{dV}{dt} & = & -V + RI + U \\
+    \tau_{OU} \frac{dU}{dt} & = & - U + D\xi
+  \end{eqnarray*}
+  Ohrnstein-Uhlenbeck noise is lowpass filtered white noise.
+  \includegraphics[width=0.45\textwidth]{lifouisihdistr08-100ms}\hfill
+  \includegraphics[width=0.45\textwidth]{lifouisihdistr16-100ms}\\
+  More peaky than the inverse Gaussian!
+\end{frame}
+
+\begin{frame}
+  \frametitle{Interval return map of LIF with OU noise}
+  LIF $I=15.7$, $\tau_{OU}=100$\,ms:
+  \includegraphics[width=1\textwidth]{lifoureturnmap16-100ms}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Serial correlations of LIF with OU noise}
+  LIF $I=15.7$, $\tau_{OU}=100$\,ms:
+  \includegraphics[width=1\textwidth]{lifouserial16-100ms}\\
+  OU-noise introduces positive interval correlations!
+\end{frame}
+
+\begin{frame}
+  \frametitle{Count statistics of LIF with OU noise}
+  LIF $I=15.7$, $\tau_{OU}=100$\,ms:
+  \includegraphics[width=1\textwidth]{lifoufano16-100ms}\\
+  Fano factor increases with count window duration.
+\end{frame}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\begin{frame}
+  \frametitle{Interval statistics of LIF with adaptation}
+  \begin{eqnarray*}
+    \tau \frac{dV}{dt} & = & -V - A + RI + D\xi \\
+    \tau_{adapt} \frac{dA}{dt} & = & - A
+  \end{eqnarray*}
+  Adaptation $A$ with time constant $\tau_{adapt}$ and increment $\Delta A$ at spike.
+  \includegraphics[width=0.45\textwidth]{lifadaptisihdistr08-100ms}\hfill
+  \includegraphics[width=0.45\textwidth]{lifadaptisihdistr65-100ms}\\
+  Similar to LIF with white noise.
+\end{frame}
+
+\begin{frame}
+  \frametitle{Interval return map of LIF with adaptation}
+  LIF $I=10$, $\tau_{adapt}=100$\,ms:
+  \includegraphics[width=1\textwidth]{lifadaptreturnmap10-100ms}\\
+  Negative correlation at lag one.
+\end{frame}
+
+\begin{frame}
+  \frametitle{Serial correlations of LIF with adaptation}
+  LIF $I=10$, $\tau_{adapt}=100$\,ms:
+  \includegraphics[width=1\textwidth]{lifadaptserial10-100ms}\\
+  Adaptation with white noise introduces negative interval correlations!
+\end{frame}
+
+\begin{frame}
+  \frametitle{Count statistics of LIF with adaptation}
+  LIF $I=10$, $\tau_{adapt}=100$\,ms:
+  \includegraphics[width=1\textwidth]{lifadaptfano10-100ms}\\
+  Fano factor decreases with count window duration.
+\end{frame}
+
+
+\end{document}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Non stationary}
+\subsection{Inhomogeneous Poisson process}
+\subsection{Firing rate}
+\subsection{Instantaneous rate}
+\subsection{Autocorrelation}
+\subsection{Crosscorrelation}
+\subsection{Joint PSTH}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Renewal process}
+\subsection{Superthreshold firing}
+\subsection{Subthreshold firing}
+\section{Non-renewal processes}
+\subsection{Bursting}
+\subsection{Resonator}
+
+
+\subsection{Standard distributions}
+\subsubsection{Gamma}
+\subsubsection{How to read ISI histograms}
+refractoriness, poisson tail, sub-, supra-threshold, missed spikes
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Correlation with stimulus}
+\subsection{Tuning curve}
+\subsection{Linear filter}
+\subsection{Spatiotemporal receptive field}
+\subsection{Generalized linear model}
+
+\begin{frame}
+\end{frame}
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diff --git a/pointprocesses/lecture/pointprocessscetchA.pdf b/pointprocesses/lecture/pointprocessscetchA.pdf
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diff --git a/pointprocesses/lecture/pointprocessscetchA.tex b/pointprocesses/lecture/pointprocessscetchA.tex
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+  /Title (pointprocessscetchB.tex)
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diff --git a/pointprocesses/lecture/pointprocessscetchB.gpt b/pointprocesses/lecture/pointprocessscetchB.gpt
new file mode 100644
index 0000000..e1cda52
--- /dev/null
+++ b/pointprocesses/lecture/pointprocessscetchB.gpt
@@ -0,0 +1,204 @@
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+set origin 0, 0.4
+set label 1 '\normalsize Intervals $\{T_i\}, \; T_i = t_{i+1} - t_i$'
+set label 3 "$T_{1}$" at 1.20025, -0.5 center
+set arrow 3 from 0.724649, 0.2 to 1.67586, 0.2 heads
+set label 4 "$T_{2}$" at 2.34987, -0.5 center
+set arrow 4 from 1.67586, 0.2 to 3.02389, 0.2 heads
+set label 5 "$T_{3}$" at 3.29927, -0.5 center
+set arrow 5 from 3.02389, 0.2 to 3.57466, 0.2 heads
+set label 6 "$T_{4}$" at 3.86293, -0.5 center
+set arrow 6 from 3.57466, 0.2 to 4.15121, 0.2 heads
+set label 7 "$T_{5}$" at 4.57767, -0.5 center
+set arrow 7 from 4.15121, 0.2 to 5.00412, 0.2 heads
+set label 8 "$T_{6}$" at 5.82481, -0.5 center
+set arrow 8 from 5.00412, 0.2 to 6.64549, 0.2 heads
+set label 9 "$T_{7}$" at 7.23103, -0.5 center
+set arrow 9 from 6.64549, 0.2 to 7.81657, 0.2 heads
+set label 10 "$T_{8}$" at 8.79811, -0.5 center
+set arrow 10 from 7.81657, 0.2 to 9.77964, 0.2 heads
+plot '-' w l lt 1 lc rgb 'black' lw 10
+0.724649 0
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+
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+e
+unset label 3
+unset label 4
+unset label 5
+unset label 6
+unset label 7
+unset label 8
+unset label 9
+unset label 10
+unset arrow 3
+unset arrow 4
+unset arrow 5
+unset arrow 6
+unset arrow 7
+unset arrow 8
+unset arrow 9
+unset arrow 10
+
+set origin 0, 0
+set size 1, 0.4
+set border 2
+set yrange [-0.5:10.5]
+set ytics 2 nomirror out
+set arrow 1 from 0, 0.0 to 11, 0.0 head filled
+set label 2 'Time' at 11, -2.2 center
+set label 1 '\normalsize Event counts $\{ n_i \}$' at graph -0.07, graph 1.2
+plot '-' w l lt 1 lc rgb 'black' lw 3, \
+     '-' w p lt 1 lc rgb 'black' pt 7 ps 1.5 lw 2, \
+     '-' w p lt 1 lc rgb 'white' pt 7 ps 1.0 lw 2, \
+     '-' w p lt 1 lc rgb 'black' pt 7 ps 1.5 lw 2
+0 0
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+e
+
+unset multiplot
diff --git a/pointprocesses/lecture/pointprocessscetchB.pdf b/pointprocesses/lecture/pointprocessscetchB.pdf
new file mode 100644
index 0000000..a4c7e8c
Binary files /dev/null and b/pointprocesses/lecture/pointprocessscetchB.pdf differ
diff --git a/pointprocesses/lecture/pointprocessscetchB.tex b/pointprocesses/lecture/pointprocessscetchB.tex
new file mode 100644
index 0000000..14803c3
--- /dev/null
+++ b/pointprocesses/lecture/pointprocessscetchB.tex
@@ -0,0 +1,130 @@
+% GNUPLOT: LaTeX picture with Postscript
+\begingroup
+  \makeatletter
+  \providecommand\color[2][]{%
+    \GenericError{(gnuplot) \space\space\space\@spaces}{%
+      Package color not loaded in conjunction with
+      terminal option `colourtext'%
+    }{See the gnuplot documentation for explanation.%
+    }{Either use 'blacktext' in gnuplot or load the package
+      color.sty in LaTeX.}%
+    \renewcommand\color[2][]{}%
+  }%
+  \providecommand\includegraphics[2][]{%
+    \GenericError{(gnuplot) \space\space\space\@spaces}{%
+      Package graphicx or graphics not loaded%
+    }{See the gnuplot documentation for explanation.%
+    }{The gnuplot epslatex terminal needs graphicx.sty or graphics.sty.}%
+    \renewcommand\includegraphics[2][]{}%
+  }%
+  \providecommand\rotatebox[2]{#2}%
+  \@ifundefined{ifGPcolor}{%
+    \newif\ifGPcolor
+    \GPcolortrue
+  }{}%
+  \@ifundefined{ifGPblacktext}{%
+    \newif\ifGPblacktext
+    \GPblacktexttrue
+  }{}%
+  % define a \g@addto@macro without @ in the name:
+  \let\gplgaddtomacro\g@addto@macro
+  % define empty templates for all commands taking text:
+  \gdef\gplbacktext{}%
+  \gdef\gplfronttext{}%
+  \makeatother
+  \ifGPblacktext
+    % no textcolor at all
+    \def\colorrgb#1{}%
+    \def\colorgray#1{}%
+  \else
+    % gray or color?
+    \ifGPcolor
+      \def\colorrgb#1{\color[rgb]{#1}}%
+      \def\colorgray#1{\color[gray]{#1}}%
+      \expandafter\def\csname LTw\endcsname{\color{white}}%
+      \expandafter\def\csname LTb\endcsname{\color{black}}%
+      \expandafter\def\csname LTa\endcsname{\color{black}}%
+      \expandafter\def\csname LT0\endcsname{\color[rgb]{1,0,0}}%
+      \expandafter\def\csname LT1\endcsname{\color[rgb]{0,1,0}}%
+      \expandafter\def\csname LT2\endcsname{\color[rgb]{0,0,1}}%
+      \expandafter\def\csname LT3\endcsname{\color[rgb]{1,0,1}}%
+      \expandafter\def\csname LT4\endcsname{\color[rgb]{0,1,1}}%
+      \expandafter\def\csname LT5\endcsname{\color[rgb]{1,1,0}}%
+      \expandafter\def\csname LT6\endcsname{\color[rgb]{0,0,0}}%
+      \expandafter\def\csname LT7\endcsname{\color[rgb]{1,0.3,0}}%
+      \expandafter\def\csname LT8\endcsname{\color[rgb]{0.5,0.5,0.5}}%
+    \else
+      % gray
+      \def\colorrgb#1{\color{black}}%
+      \def\colorgray#1{\color[gray]{#1}}%
+      \expandafter\def\csname LTw\endcsname{\color{white}}%
+      \expandafter\def\csname LTb\endcsname{\color{black}}%
+      \expandafter\def\csname LTa\endcsname{\color{black}}%
+      \expandafter\def\csname LT0\endcsname{\color{black}}%
+      \expandafter\def\csname LT1\endcsname{\color{black}}%
+      \expandafter\def\csname LT2\endcsname{\color{black}}%
+      \expandafter\def\csname LT3\endcsname{\color{black}}%
+      \expandafter\def\csname LT4\endcsname{\color{black}}%
+      \expandafter\def\csname LT5\endcsname{\color{black}}%
+      \expandafter\def\csname LT6\endcsname{\color{black}}%
+      \expandafter\def\csname LT7\endcsname{\color{black}}%
+      \expandafter\def\csname LT8\endcsname{\color{black}}%
+    \fi
+  \fi
+  \setlength{\unitlength}{0.0500bp}%
+  \begin{picture}(6462.00,3740.00)%
+    \gplgaddtomacro\gplbacktext{%
+      \csname LTb\endcsname%
+      \put(122,3587){\makebox(0,0)[l]{\strut{}\normalsize Event times $\{t_i\}$}}%
+      \put(6329,2947){\makebox(0,0){\strut{}Time}}%
+      \put(910,2865){\makebox(0,0){\strut{}$t_{1}$}}%
+      \put(1412,2865){\makebox(0,0){\strut{}$t_{2}$}}%
+      \put(2123,2865){\makebox(0,0){\strut{}$t_{3}$}}%
+      \put(2413,2865){\makebox(0,0){\strut{}$t_{4}$}}%
+      \put(2717,2865){\makebox(0,0){\strut{}$t_{5}$}}%
+      \put(3167,2865){\makebox(0,0){\strut{}$t_{6}$}}%
+      \put(4033,2865){\makebox(0,0){\strut{}$t_{7}$}}%
+      \put(4650,2865){\makebox(0,0){\strut{}$t_{8}$}}%
+      \put(5685,2865){\makebox(0,0){\strut{}$t_{9}$}}%
+    }%
+    \gplgaddtomacro\gplfronttext{%
+    }%
+    \gplgaddtomacro\gplbacktext{%
+      \csname LTb\endcsname%
+      \put(122,2465){\makebox(0,0)[l]{\strut{}\normalsize Intervals $\{T_i\}, \; T_i = t_{i+1} - t_i$}}%
+      \put(6329,1825){\makebox(0,0){\strut{}Time}}%
+      \put(1161,1770){\makebox(0,0){\strut{}$T_{1}$}}%
+      \put(1767,1770){\makebox(0,0){\strut{}$T_{2}$}}%
+      \put(2268,1770){\makebox(0,0){\strut{}$T_{3}$}}%
+      \put(2565,1770){\makebox(0,0){\strut{}$T_{4}$}}%
+      \put(2942,1770){\makebox(0,0){\strut{}$T_{5}$}}%
+      \put(3600,1770){\makebox(0,0){\strut{}$T_{6}$}}%
+      \put(4341,1770){\makebox(0,0){\strut{}$T_{7}$}}%
+      \put(5168,1770){\makebox(0,0){\strut{}$T_{8}$}}%
+    }%
+    \gplgaddtomacro\gplfronttext{%
+    }%
+    \gplgaddtomacro\gplbacktext{%
+      \colorrgb{0.00,0.00,0.00}%
+      \put(333,268){\makebox(0,0)[r]{\strut{}$0$}}%
+      \colorrgb{0.00,0.00,0.00}%
+      \put(333,460){\makebox(0,0)[r]{\strut{}$2$}}%
+      \colorrgb{0.00,0.00,0.00}%
+      \put(333,652){\makebox(0,0)[r]{\strut{}$4$}}%
+      \colorrgb{0.00,0.00,0.00}%
+      \put(333,844){\makebox(0,0)[r]{\strut{}$6$}}%
+      \colorrgb{0.00,0.00,0.00}%
+      \put(333,1036){\makebox(0,0)[r]{\strut{}$8$}}%
+      \colorrgb{0.00,0.00,0.00}%
+      \put(333,1228){\makebox(0,0)[r]{\strut{}$10$}}%
+      \csname LTb\endcsname%
+      \put(122,1487){\makebox(0,0)[l]{\strut{}\normalsize Event counts $\{ n_i \}$}}%
+      \put(6329,57){\makebox(0,0){\strut{}Time}}%
+    }%
+    \gplgaddtomacro\gplfronttext{%
+    }%
+    \gplbacktext
+    \put(0,0){\includegraphics{pointprocessscetchB}}%
+    \gplfronttext
+  \end{picture}%
+\endgroup
diff --git a/pointprocesses/lecture/poissoncounthist100hz100ms.pdf b/pointprocesses/lecture/poissoncounthist100hz100ms.pdf
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index 0000000..746f30b
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diff --git a/pointprocesses/lecture/poissonfano100hz.pdf b/pointprocesses/lecture/poissonfano100hz.pdf
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index 0000000..c25dd3c
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diff --git a/pointprocesses/lecture/poissonisih100hz.pdf b/pointprocesses/lecture/poissonisih100hz.pdf
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diff --git a/pointprocesses/lecture/poissonraster100hz.pdf b/pointprocesses/lecture/poissonraster100hz.pdf
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index 0000000..24a9b7e
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diff --git a/pointprocesses/lecture/poissonreturnmap100hz.pdf b/pointprocesses/lecture/poissonreturnmap100hz.pdf
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index 0000000..30991e9
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diff --git a/pointprocesses/lecture/poissonserial100hz.pdf b/pointprocesses/lecture/poissonserial100hz.pdf
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index 0000000..ff667d9
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diff --git a/pointprocesses/lecture/raster.pdf b/pointprocesses/lecture/raster.pdf
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index 0000000..a20fa0e
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diff --git a/pointprocesses/lecture/thumbs.pdf b/pointprocesses/lecture/thumbs.pdf
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diff --git a/pointprocesses/lecture/whitestyles.gp b/pointprocesses/lecture/whitestyles.gp
new file mode 100644
index 0000000..1eeb255
--- /dev/null
+++ b/pointprocesses/lecture/whitestyles.gp
@@ -0,0 +1,67 @@
+set tics scale 0.8
+set tics out
+set format '$%g$'
+unset grid
+unset key
+
+# on white background:
+set style line  1 lt -1 lc rgb "black" lw 2              # border
+set border back ls 1
+set style line  2 lt 1 lc rgb "black" lw 4 pt 7 ps 2     # thick line
+set style line  3 lt 1 lc rgb "black" lw 2               # thin line
+set style line  4 lt 3 lc rgb "black" lw 1               # grid line
+
+set style line 10 lt 1 lc rgb "#00DD00" lw 6          # stimulus thick, AM
+set style line 11 lt 1 lc rgb "#00DD00" lw 3          # stimulus thin, AM
+set style line 15 lt 1 lc rgb "black" lw 15              # stimulus bar
+
+set style line 20 lt 1 lc rgb "yellow" lw 4              # thin spikes cell 1
+set style line 21 lt 1 lc rgb "orange" lw 4              # thin spikes cell 2
+set style line 22 lt 1 lc rgb "yellow" lw 6              # thick spikes cell 1
+set style line 23 lt 1 lc rgb "orange" lw 6              # thick spikes cell 2
+set style line 24 lt 1 lc rgb "blue" lw 22               # big sync spikes
+set style line 25 lt 1 lc rgb "blue" lw 6                # thick sync spikes
+set style line 26 lt 1 lc rgb "orange" lw 4              # all spikes
+
+set style line 30 lt 1 lc rgb "dark-red" lw 6 pt 7 ps 2   # firing rate 1 thick
+set style line 31 lt 1 lc rgb "red" lw 6 pt 7 ps 2        # firing rate 2 thick
+set style line 32 lt 1 lc rgb "orange" lw 6 pt 7 ps 2     # firing rate 3 thick
+set style line 33 lt 1 lc rgb "blue" lw 6 pt 7 ps 2       # firing rate 4 thick
+set style line 34 lt 1 lc rgb "gray" lw 6                 # firing rate gray thick
+set style line 35 lt 1 lc rgb "dark-red" lw 3 pt 7 ps 0.7 # firing rate 1 thin
+set style line 36 lt 1 lc rgb "red" lw 3                  # firing rate 2 thin
+set style line 37 lt 1 lc rgb "orange" lw 3               # firing rate 3 thin
+set style line 38 lt 1 lc rgb "blue" lw 3                 # firing rate 4 thin
+set style line 39 lt 1 lc rgb "gray" lw 3                 # firing rate gray thin
+
+set style line 40 lt 1 lc rgb "green" lw 6 pt 5 ps 2     # onset f-I curve thick
+set style line 41 lt 1 lc rgb "green" lw 3 pt 5 ps 2     # onset f-I curve thin
+set style line 42 lt 1 lc rgb "red" lw 6 pt 7 ps 2            # steady-state f-I curve thick
+set style line 43 lt 1 lc rgb "red" lw 3 pt 7 ps 2            # steady-state f-I curve thin
+set style line 44 lt 1 lc rgb "blue" lw 6 pt 9 ps 2           # adapted f-I curve thick
+set style line 45 lt 1 lc rgb "blue" lw 3 pt 9 ps 2           # adapted f-I curve thin
+set style line 47 lt 1 lc rgb "cyan" lw 6                     # adaptation level thick
+set style line 48 lt 1 lc rgb "cyan" lw 3                     # adaptation level thin
+set style line 49 lt 1 lc rgb "yellow" lw 3 pt 7 ps 4         # current adaptation point
+
+set style line 50 lt 1 lc rgb "dark-red" lw 4         # membrane voltage 1
+set style line 51 lt 1 lc rgb "red" lw 4              # membrane voltage 2
+set style line 52 lt 1 lc rgb "orange" lw 4           # membrane voltage 3
+set style line 55 lt 1 lc rgb "orange" lw 4           # nerve potential
+
+set style line 60 lt 1 lc rgb "blue" lw 4             # gating variable 
+set style line 61 lt 1 lc rgb "cyan" lw 4             # gating variable average
+
+set style line 70 lt 1 lc rgb "red" lw 4              # ionic current
+set style line 71 lt 1 lc rgb "orange" lw 4           # ionic current average
+
+set style line 80 lt 1 lc rgb "#77FFFF" lw 4 pt 13 ps 2.4# baseline
+set style line 81 lt 1 lc rgb "#FF5533" lw 4 pt 9 ps 2.8 # beat
+set style line 82 lt 1 lc rgb "yellow" lw 4 pt 7 ps 2.4  # chirp
+set style line 83 lt 1 lc rgb "#FF7700" lw 4 pt 11 ps 2.8# beat shuffled
+set style line 84 lt 1 lc rgb "#FFAA00" lw 4 pt 5 ps 2.2 # chirp shuffled
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+set style line 91 lt 1 lc rgb "#77FFFF" lw 4             # EOD thick
+
+set term epslatex input color dashed rounded size 11.4cm, 6.6cm
diff --git a/pointprocesses/resources/Daley2003-IntroductionToTheTheoryOfPointProcesses.pdf b/pointprocesses/resources/Daley2003-IntroductionToTheTheoryOfPointProcesses.pdf
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index 0000000..5a193da
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diff --git a/resources/Mallot2014-DatAnal.pdf b/resources/Mallot2014-DatAnal.pdf
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