added all my stuff
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0fdcf2f82e
commit
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announcements/correlationcartoon.jpeg
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announcements/correlationcartoon.jpeg
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announcements/evalutation.pdf
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announcements/evalutation.tex
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announcements/evalutation.tex
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\documentclass[pdftex]{beamer}
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\usepackage{beamerthemedefault}
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\usepackage{multimedia}
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\usepackage{wasysym}
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\useoutertheme[subsection=false]{smoothbars}
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\useinnertheme[shadow=true]{rounded}
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\setbeamercolor{block title}{fg=black,bg=gray}
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\setbeamercolor{block title alerted}{use=alerted text,fg=black,bg=alerted text.fg!75!bg}
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\setbeamercolor{block title example}{use=example text,fg=black,bg=example text.fg!75!bg}
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\setbeamercolor{block body}{parent=normal text,use=block title,bg=block title.bg!25!bg}
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\setbeamercolor{block body alerted}{parent=normal text,use=block title alerted,bg=block title alerted.bg!25!bg}
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\setbeamercolor{block body example}{parent=normal text,use=block title example,bg=block title example.bg!25!bg}
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\usepackage{hyperref}
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\usepackage[english]{babel} % language set to new-german
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\usepackage[utf8]{inputenc} % coding of german special characters
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\usepackage{graphicx} % \includegraphics[options]{file.eps}
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\usepackage{array}
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\usepackage{setspace}
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\usepackage{color}
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\usepackage{eqlist}
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\usepackage{wallpaper}
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\definecolor{tug}{HTML}{CC0000}
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\newcommand{\rot}[1]{{\color{tug} #1}}
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\definecolor{green}{HTML}{1C8C1C}
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\newcommand{\gruen}[1]{{\color{green} #1}}
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\definecolor{blue}{HTML}{2424AA}
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\newcommand{\blau}[1]{{\color{blue} #1}}
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\definecolor{gray}{rgb}{0.8,0.8,0.8}
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\definecolor{gray2}{rgb}{0.5,0.5,0.5}
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\definecolor{gray2}{rgb}{0.5,0.5,0.5}
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\newcommand{\grau}[1]{{\color{gray2} #1}}
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\setbeamercolor{footline}{fg=black,bg=gray}
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%\setbeamertemplate{headline}[text line]{
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% \begin{beamercolorbox}[wd=\paperwidth,ht=8ex,dp=4ex]{}%\vskip-4pt
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% \insertnavigation{0.85\paperwidth}
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% \hskip-1pt\rule{\paperwidth}{0.3pt}
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% \end{beamercolorbox}
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%}
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\setlength{\unitlength}{0.01\paperwidth}
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\setbeamercolor{background canvas}{bg=}
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\begin{document}
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\begin{frame}
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\frametitle{Evalutation}
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In Ilias:\\[2ex]
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Go to {\em Evaluation} and fill out the forms!\\[6ex]
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\includegraphics[width=0.8\textwidth]{correlationcartoon}
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\end{frame}
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\end{document}
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linearalgebra/code/Chorthippus_biguttulus.wav
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linearalgebra/code/Chorthippus_biguttulus.wav
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linearalgebra/code/PeterUndDerWolf.wav
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linearalgebra/code/Tielli-Kraulis.wav
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linearalgebra/code/coordinaterafo2.m
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linearalgebra/code/coordinaterafo2.m
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% some vectors:
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x = [ -1:0.02:1 ];
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y = x*0.5 + 0.1*randn( size(x) );
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plot( x, y, '.b' );
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hold on
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plot( x(10), y(10), '.r' );
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% new coordinate system:
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%e1 = [ 3/5 4/5 ];
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%e2 = [ -4/5 3/5 ];
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e1 = [ 3 4 ];
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e2 = [ -4 3 ];
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me1 = sqrt( e1*e1' );
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e1 = e1/me1;
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me2 = sqrt( e2*e2' );
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e2 = e2/me2;
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quiver( 0.0, 0.0 , e1(1), e1(2), 1.0, 'r' )
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quiver( 0.0, 0.0 , e2(1), e2(2), 1.0, 'g' )
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axis( 'equal' )
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% project [x y] onto e1 and e2:
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% % the long way:
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% nx = zeros( size( x ) ); % new x coordinate
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% ny = zeros( size( y ) ); % new y coordinates
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% for k=1:length(x)
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% xvec = [ x(k) y(k) ];
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% nx(k) = xvec * e1';
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% ny(k) = xvec * e2';
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% end
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% plot( nx, ny, '.g' );
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% the short way:
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%nx = [ x' y' ] * e1';
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%nx = [x; y]' * e1';
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% nx = e1 * [ x; y ];
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% ny = e2 * [ x; y ];
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% plot( nx, ny, '.g' );
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% even shorter:
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n = [e1; e2 ] * [ x; y ];
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plot( n(1,:), n(2,:), '.g' );
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hold off
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linearalgebra/code/coordinatetrafo.m
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linearalgebra/code/coordinatetrafo.m
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% some vectors:
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x=[-1:0.02:1]'; % column vector!
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y=4*x/3 + 0.1*randn( size( x ) );
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plot( x, y, '.b' );
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axis( 'equal' );
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hold on;
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% new coordinate system:
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% e1 = [ 3 4 ]
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% e2 = [ -4 3 ]
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% and normalized to unit length (factor 1/5):
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e = [ 3/5 -4/5; 4/5 3/5 ];
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quiver( [0 0], [0 0], e(1,:), e(2,:), 1.0, 'r' );
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% project [x y] onto e1 and e2:
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% % the long way:
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% nx = x;
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% ny = y;
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% for k=1:length(x)
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% nx(k) = [x(k) y(k)]*e(:,1);
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% ny(k) = [x(k) y(k)]*e(:,2);
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% end
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% plot( nx, ny, '.g' );
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% the short way
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% nx = [x y]*e(:,1);
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% ny = [x y]*e(:,2);
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% plot( nx, ny, '.g' );
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% even shorter:
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n = [x y]*e;
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plot( n(:,1), n(:,2), '.g' );
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xlabel( 'x' );
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ylabel( 'y' );
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hold off;
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linearalgebra/code/correlationcoefficient.m
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linearalgebra/code/correlationcoefficient.m
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set( 0, 'DefaultTextFontSize', 22.0 );
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set( 0, 'DefaultAxesFontSize', 22.0 );
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n = 10000;
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x = 1.0*randn( n, 1 );
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z = 1.0*randn( n, 1 );
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for r=-1:0.2:1
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y = r*x + sqrt(1.0-r^2.0)*z;
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cv = cov( x, y );
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cr = corrcoef( x, y );
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scatter( x, y, 'filled', 'MarkerEdgeColor', 'white' );
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xlabel( 'x' );
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ylabel( 'y' );
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text( 0.05, 0.9, sprintf( 'r = %.2f', r ), 'Units', 'normalized' )
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text( 0.05, 0.8, sprintf( 'cor = %.2f %.2f', cr(1,1), cr(1,2) ), 'Units', 'normalized' )
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text( 0.05, 0.7, sprintf( 'cov = %.2f %.2f', cv(1,1), cv(1,2) ), 'Units', 'normalized' )
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waitforbuttonpress;
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end
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y = x.^2.0 + 0.5*z;
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cv = cov( x, y );
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cr = corrcoef( x, y );
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scatter( x, y, 'filled', 'MarkerEdgeColor', 'white' );
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xlabel( 'x' );
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ylabel( 'y' );
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text( 0.05, 0.9, sprintf( 'x^2', r ), 'Units', 'normalized' )
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text( 0.05, 0.8, sprintf( 'cor = %.2f %.2f', cr(1,1), cr(1,2) ), 'Units', 'normalized' )
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text( 0.05, 0.7, sprintf( 'cov = %.2f %.2f', cv(1,1), cv(1,2) ), 'Units', 'normalized' )
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@ -44,11 +44,16 @@ function covareigen( x, y, pm, w, h )
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% plot eigenvectors:
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% plot eigenvectors:
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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 )
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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 )
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xlabel( 'x' );
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ylabel( 'y' );
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axis( 'equal' );
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axis( 'equal' );
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hold off;
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hold off;
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% histogram of x values:
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subplot( 2, 2, 3 );
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subplot( 2, 2, 3 );
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hist( x, 50, 'b' );
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hist( x, 50, 'b' );
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xlabel( 'x' );
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ylabel( 'n_x' );
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% sort the eigenvalues:
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% sort the eigenvalues:
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[d,inx] = sort( diag(d), 'descend' );
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[d,inx] = sort( diag(d), 'descend' );
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@ -59,24 +64,28 @@ function covareigen( x, y, pm, w, h )
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x = x - mean( x );
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x = x - mean( x );
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y = y - mean( y );
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y = y - mean( y );
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% project onto eigenvectors:
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% project onto eigenvectors:
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nx = [ x y ] * v(:,inx(1));
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nc = [ x y ] * v(:,inx);
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ny = [ x y ] * v(:,inx(2));
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cv = cov( nc )
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cv = cov( [nx, ny] )
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[ v , d] = eig( cv )
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[ v , d] = eig( cv )
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if (pm > 0) & (nargin == 5)
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if (pm > 0) & (nargin == 5)
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if pm == 1
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if pm == 1
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[n,c] = hist3([nx ny], { xp, yp } );
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[n,c] = hist3( nc, { xp, yp } );
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contourf( c{1}, c{2}, n' );
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contourf( c{1}, c{2}, n' );
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else
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else
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gauss = reshape( exp(-0.5*diag(xy*inv(cv)*xy'))/sqrt((2.0*pi)^2.0*det(cv)), size( xg ) );
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gauss = reshape( exp(-0.5*diag(xy*inv(cv)*xy'))/sqrt((2.0*pi)^2.0*det(cv)), size( xg ) );
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contourf( xp, yp, gauss )
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contourf( xp, yp, gauss )
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end
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end
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else
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else
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scatter( nx, ny, 'b', 'filled', 'MarkerEdgeColor', 'white' );
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scatter( nc(:,1), nc(:,2), 'b', 'filled', 'MarkerEdgeColor', 'white' );
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end
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end
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xlabel( 'x' );
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ylabel( 'y' );
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axis( 'equal' );
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axis( 'equal' );
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hold off;
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hold off;
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% histogram of new x values:
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subplot( 2, 2, 4 );
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subplot( 2, 2, 4 );
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hist( nx, 50, 'b' );
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hist( nc(:,1), 50, 'b' );
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xlabel( 'new x' );
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ylabel( 'n_newx' );
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end
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end
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@ -27,18 +27,33 @@ function covareigen3( x, y, z )
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%axis( 'equal' );
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%axis( 'equal' );
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hold off;
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hold off;
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% 2D scatter plots:
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subplot( 2, 6, 4 );
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scatter( x, y, 'b', 'filled', 'MarkerEdgeColor', 'white' );
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xlabel( 'x' )
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ylabel( 'y' )
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subplot( 2, 6, 5 );
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scatter( x, z, 'b', 'filled', 'MarkerEdgeColor', 'white' );
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xlabel( 'x' )
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ylabel( 'z' )
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subplot( 2, 6, 6 );
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scatter( y, z, 'b', 'filled', 'MarkerEdgeColor', 'white' );
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xlabel( 'y' )
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ylabel( 'z' )
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% sort the eigenvalues:
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% sort the eigenvalues:
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[d,inx] = sort( diag(d), 'descend' );
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[d,inx] = sort( diag(d), 'descend' );
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subplot( 2, 2, 2 );
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subplot( 2, 2, 4 );
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hold on;
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hold on;
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% subtract means:
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% subtract means:
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x = x - mean( x );
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x = x - mean( x );
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y = y - mean( y );
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y = y - mean( y );
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% project onto eigenvectors:
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% project onto eigenvectors:
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nx = [ x y z ] * v(:,inx(1));
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nx = [ x y z ] * v(:,inx);
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ny = [ x y z ] * v(:,inx(2));
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scatter( nx(:,1), nx(:,2), 'b', 'filled', 'MarkerEdgeColor', 'white' );
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scatter( nx, ny, 'b', 'filled', 'MarkerEdgeColor', 'white' );
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xlabel( 'ex' )
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ylabel( 'ey' )
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axis( 'equal' );
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axis( 'equal' );
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hold off;
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hold off;
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19
linearalgebra/code/covareigen3examples.m
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linearalgebra/code/covareigen3examples.m
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scrsz = get( 0, 'ScreenSize' );
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set( 0, 'DefaultFigurePosition', [ scrsz(3)/2 scrsz(4)/2 scrsz(3)/2 scrsz(4)/2 ] );
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n = 10000;
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% three distributions:
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x = randn( n, 1 );
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y = randn( n, 1 );
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z = randn( n, 1 );
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dx = [ 0 8 0 0 ];
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dy = [ 0 0 8 0 ];
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dz = [ 0 0 0 8 ];
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for k = 1:4
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x((k-1)*n/4+1:k*n/4) = x((k-1)*n/4+1:k*n/4) + dx(k);
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y((k-1)*n/4+1:k*n/4) = y((k-1)*n/4+1:k*n/4) + dy(k);
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z((k-1)*n/4+1:k*n/4) = z((k-1)*n/4+1:k*n/4) + dz(k);
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end
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f = figure( 1 );
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covareigen3( x, y, z );
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@ -5,11 +5,11 @@ set( 0, 'DefaultFigurePosition', [ scrsz(3)/2 scrsz(4)/2 scrsz(3)/2 scrsz(4)/2 ]
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n = 10000;
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n = 10000;
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x = randn( n, 1 );
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x = randn( n, 1 );
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f = figure( 1 );
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f = figure( 1 );
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for r = 0.01:0.199:1
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for r = 0.01:0.19:1
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fprintf( 'Correlation = %g\n', r );
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fprintf( 'Correlation = %g\n', r );
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clf( f );
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clf( f );
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y = r*x + sqrt(1-r^2)*randn( n, 1 );
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y = r*x + sqrt(1-r^2)*randn( n, 1 );
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covareigen( x, y, 2, 5.0, 3.0 );
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covareigen( x, y, 0, 5.0, 3.0 );
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key = waitforbuttonpress;
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key = waitforbuttonpress;
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end
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end
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return
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return
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x = [ x1; x2 ];
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x = [ x1; x2 ];
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y = [ y1+d2; y2-d2 ];
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y = [ y1+d2; y2-d2 ];
|
||||||
scrsz = get(0,'ScreenSize');
|
scrsz = get(0,'ScreenSize');
|
||||||
covareigen( x, y, 1, 10.0, 7.0 );
|
covareigen( x, y, 0, 10.0, 7.0 );
|
||||||
%key = waitforbuttonpress;
|
%key = waitforbuttonpress;
|
||||||
pause( 1.0 );
|
pause( 1.0 );
|
||||||
end
|
end
|
||||||
BIN
linearalgebra/code/extdata.mat
Normal file
BIN
linearalgebra/code/extdata.mat
Normal file
Binary file not shown.
54
linearalgebra/code/extracellularrecording.m
Normal file
54
linearalgebra/code/extracellularrecording.m
Normal file
@ -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' );
|
||||||
25
linearalgebra/code/gaussian.m
Normal file
25
linearalgebra/code/gaussian.m
Normal file
@ -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' );
|
||||||
21
linearalgebra/code/matrix2dtrafos.m
Normal file
21
linearalgebra/code/matrix2dtrafos.m
Normal file
@ -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
|
||||||
45
linearalgebra/code/matrixbox.m
Normal file
45
linearalgebra/code/matrixbox.m
Normal file
@ -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
|
||||||
85
linearalgebra/code/matrixmultiplicationlatex.m
Normal file
85
linearalgebra/code/matrixmultiplicationlatex.m
Normal file
@ -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 );
|
||||||
BIN
linearalgebra/code/p-unit_spike_times.mat
Normal file
BIN
linearalgebra/code/p-unit_spike_times.mat
Normal file
Binary file not shown.
BIN
linearalgebra/code/p-unit_stimulus.mat
Normal file
BIN
linearalgebra/code/p-unit_stimulus.mat
Normal file
Binary file not shown.
95
linearalgebra/code/pca2d.m
Normal file
95
linearalgebra/code/pca2d.m
Normal file
@ -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
|
||||||
33
linearalgebra/code/pca2dexamples.m
Normal file
33
linearalgebra/code/pca2dexamples.m
Normal file
@ -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
|
||||||
60
linearalgebra/code/pca3d.m
Normal file
60
linearalgebra/code/pca3d.m
Normal file
@ -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
|
||||||
19
linearalgebra/code/pca3dexamples.m
Normal file
19
linearalgebra/code/pca3dexamples.m
Normal file
@ -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 );
|
||||||
21
linearalgebra/code/simplematrix2dtrafos.m
Normal file
21
linearalgebra/code/simplematrix2dtrafos.m
Normal file
@ -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
|
||||||
21
linearalgebra/code/simplematrixbox.m
Normal file
21
linearalgebra/code/simplematrixbox.m
Normal file
@ -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
|
||||||
72
linearalgebra/code/soundmixture.m
Normal file
72
linearalgebra/code/soundmixture.m
Normal file
@ -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 );
|
||||||
|
|
||||||
118
linearalgebra/code/spikesortingwave.m
Normal file
118
linearalgebra/code/spikesortingwave.m
Normal file
@ -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;
|
||||||
227
linearalgebra/code/sta.m
Normal file
227
linearalgebra/code/sta.m
Normal file
@ -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
|
||||||
31
linearalgebra/code/stastcexample.m
Normal file
31
linearalgebra/code/stastcexample.m
Normal file
@ -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 );
|
||||||
66
linearalgebra/code/stc.m
Normal file
66
linearalgebra/code/stc.m
Normal file
@ -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
|
||||||
BIN
linearalgebra/exercises/correlations.pdf
Normal file
BIN
linearalgebra/exercises/correlations.pdf
Normal file
Binary file not shown.
95
linearalgebra/exercises/correlations.tex
Normal file
95
linearalgebra/exercises/correlations.tex
Normal file
@ -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}
|
||||||
BIN
linearalgebra/exercises/matrices.pdf
Normal file
BIN
linearalgebra/exercises/matrices.pdf
Normal file
Binary file not shown.
205
linearalgebra/exercises/matrices.tex
Normal file
205
linearalgebra/exercises/matrices.tex
Normal file
@ -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}
|
||||||
BIN
linearalgebra/resources/Pillow2007-STC-LNPmodel.pdf
Normal file
BIN
linearalgebra/resources/Pillow2007-STC-LNPmodel.pdf
Normal file
Binary file not shown.
@ -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 );
|
|
||||||
@ -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;
|
|
||||||
@ -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
|
|
||||||
@ -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;
|
|
||||||
BIN
moduledescription/Einführung_wissenschaftl_Datenverarbeitung.pdf
Normal file
BIN
moduledescription/Einführung_wissenschaftl_Datenverarbeitung.pdf
Normal file
Binary file not shown.
Binary file not shown.
BIN
moduledescription/Hiwis.odt
Normal file
BIN
moduledescription/Hiwis.odt
Normal file
Binary file not shown.
BIN
moduledescription/Hiwis.pdf
Normal file
BIN
moduledescription/Hiwis.pdf
Normal file
Binary file not shown.
BIN
moduledescription/ModuleScientificComputing.doc
Normal file
BIN
moduledescription/ModuleScientificComputing.doc
Normal file
Binary file not shown.
BIN
moduledescription/ModuleScientificComputing.odt
Normal file
BIN
moduledescription/ModuleScientificComputing.odt
Normal file
Binary file not shown.
10
pointprocesses/code/colorednoisepdf.m
Normal file
10
pointprocesses/code/colorednoisepdf.m
Normal file
@ -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
|
||||||
24
pointprocesses/code/colorednoisepdfisifit.m
Normal file
24
pointprocesses/code/colorednoisepdfisifit.m
Normal file
@ -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
|
||||||
|
|
||||||
38
pointprocesses/code/counthist.m
Normal file
38
pointprocesses/code/counthist.m
Normal file
@ -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
|
||||||
|
|
||||||
39
pointprocesses/code/fano.m
Normal file
39
pointprocesses/code/fano.m
Normal file
@ -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
|
||||||
|
|
||||||
5
pointprocesses/code/inversegauss.m
Normal file
5
pointprocesses/code/inversegauss.m
Normal file
@ -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
|
||||||
28
pointprocesses/code/inversegaussplot.m
Normal file
28
pointprocesses/code/inversegaussplot.m
Normal file
@ -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
|
||||||
32
pointprocesses/code/isihist.m
Normal file
32
pointprocesses/code/isihist.m
Normal file
@ -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
|
||||||
|
|
||||||
24
pointprocesses/code/isireturnmap.m
Normal file
24
pointprocesses/code/isireturnmap.m
Normal file
@ -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
|
||||||
|
|
||||||
15
pointprocesses/code/isis.m
Normal file
15
pointprocesses/code/isis.m
Normal file
@ -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
|
||||||
|
|
||||||
26
pointprocesses/code/isiserialcorr.m
Normal file
26
pointprocesses/code/isiserialcorr.m
Normal file
@ -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
|
||||||
|
|
||||||
53
pointprocesses/code/lifadaptspikes.m
Normal file
53
pointprocesses/code/lifadaptspikes.m
Normal file
@ -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
|
||||||
51
pointprocesses/code/lifboltzmanspikes.m
Normal file
51
pointprocesses/code/lifboltzmanspikes.m
Normal file
@ -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
|
||||||
23
pointprocesses/code/liffano.m
Normal file
23
pointprocesses/code/liffano.m
Normal file
@ -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 );
|
||||||
36
pointprocesses/code/lifficurves.m
Normal file
36
pointprocesses/code/lifficurves.m
Normal file
@ -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
|
||||||
|
|
||||||
|
|
||||||
65
pointprocesses/code/lifinputdiscriminationslope.m
Normal file
65
pointprocesses/code/lifinputdiscriminationslope.m
Normal file
@ -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 );
|
||||||
51
pointprocesses/code/lifinputdiscriminationtime.m
Normal file
51
pointprocesses/code/lifinputdiscriminationtime.m
Normal file
@ -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 );
|
||||||
35
pointprocesses/code/lifisih.m
Normal file
35
pointprocesses/code/lifisih.m
Normal file
@ -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 );
|
||||||
63
pointprocesses/code/lifisistats.m
Normal file
63
pointprocesses/code/lifisistats.m
Normal file
@ -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]' );
|
||||||
64
pointprocesses/code/lifouadaptspikes.m
Normal file
64
pointprocesses/code/lifouadaptspikes.m
Normal file
@ -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
|
||||||
44
pointprocesses/code/lifouspikes.m
Normal file
44
pointprocesses/code/lifouspikes.m
Normal file
@ -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
|
||||||
34
pointprocesses/code/lifrateisicorr.m
Normal file
34
pointprocesses/code/lifrateisicorr.m
Normal file
@ -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
|
||||||
38
pointprocesses/code/lifspikes.m
Normal file
38
pointprocesses/code/lifspikes.m
Normal file
@ -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
|
||||||
44
pointprocesses/code/pifouspikes.m
Normal file
44
pointprocesses/code/pifouspikes.m
Normal file
@ -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
|
||||||
38
pointprocesses/code/pifspikes.m
Normal file
38
pointprocesses/code/pifspikes.m
Normal file
@ -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
|
||||||
27
pointprocesses/code/poissonisih.m
Normal file
27
pointprocesses/code/poissonisih.m
Normal file
@ -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 );
|
||||||
46
pointprocesses/code/poissonisistats.m
Normal file
46
pointprocesses/code/poissonisistats.m
Normal file
@ -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' );
|
||||||
20
pointprocesses/code/poissonspikes.m
Normal file
20
pointprocesses/code/poissonspikes.m
Normal file
@ -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
|
||||||
BIN
pointprocesses/code/raster.pdf
Normal file
BIN
pointprocesses/code/raster.pdf
Normal file
Binary file not shown.
28
pointprocesses/code/savefigpdf.m
Normal file
28
pointprocesses/code/savefigpdf.m
Normal file
@ -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
|
||||||
|
|
||||||
17
pointprocesses/code/spikeraster.m
Normal file
17
pointprocesses/code/spikeraster.m
Normal file
@ -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
|
||||||
|
|
||||||
18
pointprocesses/exercises/hompoissonisih.m
Normal file
18
pointprocesses/exercises/hompoissonisih.m
Normal file
@ -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
|
||||||
46
pointprocesses/exercises/hompoissonisistats.m
Normal file
46
pointprocesses/exercises/hompoissonisistats.m
Normal file
@ -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' );
|
||||||
19
pointprocesses/exercises/hompoissonspikes.m
Normal file
19
pointprocesses/exercises/hompoissonspikes.m
Normal file
@ -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
|
||||||
BIN
pointprocesses/exercises/iafisistats-solutions.pdf
Normal file
BIN
pointprocesses/exercises/iafisistats-solutions.pdf
Normal file
Binary file not shown.
BIN
pointprocesses/exercises/iafisistats.pdf
Normal file
BIN
pointprocesses/exercises/iafisistats.pdf
Normal file
Binary file not shown.
142
pointprocesses/exercises/iafisistats.tex
Normal file
142
pointprocesses/exercises/iafisistats.tex
Normal file
@ -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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