diff --git a/pointprocesses/exercises/Makefile b/pointprocesses/exercises/Makefile
index 0d84b85..2bbd561 100644
--- a/pointprocesses/exercises/Makefile
+++ b/pointprocesses/exercises/Makefile
@@ -1,3 +1,3 @@
-TEXFILES=$(wildcard pointprocesses-?.tex)
+TEXFILES=$(wildcard pointprocesses-?.tex) psth.tex
 
 include ../../exercises.mk
diff --git a/pointprocesses/exercises/counthist.pdf b/pointprocesses/exercises/counthist.pdf
deleted file mode 100644
index 0cbcacf..0000000
Binary files a/pointprocesses/exercises/counthist.pdf and /dev/null differ
diff --git a/pointprocesses/exercises/hompoissonisih.m b/pointprocesses/exercises/hompoissonisih.m
deleted file mode 100644
index cd2bc06..0000000
--- a/pointprocesses/exercises/hompoissonisih.m
+++ /dev/null
@@ -1,18 +0,0 @@
-% generate spike times:
-rate = 20.0;
-spikes = hompoissonspikes( 10, rate, 50.0 );
-% isi histogram:
-isivec = isis( spikes );
-isihist( isivec );
-hold on
-% theoretical density:
-xmax = 5.0/rate;
-x = 0:0.0001:xmax;
-y = rate*exp(-rate*x);
-plot( 1000.0*x, y, 'r', 'LineWidth', 3 );
-% plot details:
-title( sprintf( 'Poisson spike trains, rate=%g Hz, nisi=%d', rate, length( isivec ) ) )
-xlim( [ 0.0 1000.0*xmax ] )
-ylim( [ 0.0 1.1*rate ] )
-legend( 'data', 'poisson' )
-hold off
diff --git a/pointprocesses/exercises/hompoissonisistats.m b/pointprocesses/exercises/hompoissonisistats.m
deleted file mode 100644
index 7ca3cf3..0000000
--- a/pointprocesses/exercises/hompoissonisistats.m
+++ /dev/null
@@ -1,46 +0,0 @@
-rates = 1:1:100;
-avisi = [];
-sdisi = [];
-cvisi = [];
-
-for rate = rates
-    spikes = hompoissonspikes( 10, rate, 100.0 );
-    isivec = isis( spikes );
-    av = mean( isivec );
-    sd = std( isivec );
-    cv = sd/av;
-    avisi = [ avisi av ];
-    sdisi = [ sdisi sd ];
-    cvisi = [ cvisi cv ];
-end
-
-f = figure;
-subplot( 1, 3, 1 );
-scatter( rates, 1000.0*avisi, 'b', 'filled' );
-hold on;
-plot( rates, 1000.0./rates, 'r' );
-hold off;
-xlabel( 'Rate \lambda [Hz]' );
-ylim( [ 0 1000 ] );
-title( 'Mean ISI [ms]' );
-legend( 'simulation', 'theory 1/\lambda' );
-
-subplot( 1, 3, 2 );
-scatter( rates, 1000.0*sdisi, 'b', 'filled' );
-hold on;
-plot( rates, 1000.0./rates, 'r' );
-hold off;
-xlabel( 'Rate \lambda [Hz]' );
-ylim( [ 0 1000 ] )
-title( 'Standard deviation ISI [ms]' );
-legend( 'simulation', 'theory 1/\lambda' );
-
-subplot( 1, 3, 3 );
-scatter( rates, cvisi, 'b', 'filled' );
-hold on;
-plot( rates, ones( size( rates ) ), 'r' );
-hold off;
-xlabel( 'Rate \lambda [Hz]' );
-ylim( [ 0 2 ] )
-title( 'CV' );
-legend( 'simulation', 'theory' );
diff --git a/pointprocesses/exercises/hompoissonspikes.m b/pointprocesses/exercises/hompoissonspikes.m
deleted file mode 100644
index 43b4e5c..0000000
--- a/pointprocesses/exercises/hompoissonspikes.m
+++ /dev/null
@@ -1,19 +0,0 @@
-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,:) < p ) * dt;
-    end
-end
diff --git a/pointprocesses/exercises/iafisistats-solutions.pdf b/pointprocesses/exercises/iafisistats-solutions.pdf
deleted file mode 100644
index d1b6c63..0000000
Binary files a/pointprocesses/exercises/iafisistats-solutions.pdf and /dev/null differ
diff --git a/pointprocesses/exercises/iafisistats.pdf b/pointprocesses/exercises/iafisistats.pdf
deleted file mode 100644
index c19617b..0000000
Binary files a/pointprocesses/exercises/iafisistats.pdf and /dev/null differ
diff --git a/pointprocesses/exercises/iafisistats.tex b/pointprocesses/exercises/iafisistats.tex
deleted file mode 100644
index 53ca479..0000000
--- a/pointprocesses/exercises/iafisistats.tex
+++ /dev/null
@@ -1,142 +0,0 @@
-\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}
diff --git a/pointprocesses/code/inversegauss.m b/pointprocesses/exercises/inversegauss.m
similarity index 100%
rename from pointprocesses/code/inversegauss.m
rename to pointprocesses/exercises/inversegauss.m
diff --git a/pointprocesses/code/inversegaussplot.m b/pointprocesses/exercises/inversegaussplot.m
similarity index 100%
rename from pointprocesses/code/inversegaussplot.m
rename to pointprocesses/exercises/inversegaussplot.m
diff --git a/pointprocesses/code/lifficurves.m b/pointprocesses/exercises/lifficurves.m
similarity index 100%
rename from pointprocesses/code/lifficurves.m
rename to pointprocesses/exercises/lifficurves.m
diff --git a/pointprocesses/code/lifisih.m b/pointprocesses/exercises/lifisih.m
similarity index 100%
rename from pointprocesses/code/lifisih.m
rename to pointprocesses/exercises/lifisih.m
diff --git a/pointprocesses/code/lifisistats.m b/pointprocesses/exercises/lifisistats.m
similarity index 100%
rename from pointprocesses/code/lifisistats.m
rename to pointprocesses/exercises/lifisistats.m
diff --git a/pointprocesses/exercises/pointprocesses-2.tex b/pointprocesses/exercises/pointprocesses-2.tex
index 8af40c9..87ffff3 100644
--- a/pointprocesses/exercises/pointprocesses-2.tex
+++ b/pointprocesses/exercises/pointprocesses-2.tex
@@ -1,12 +1,12 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\newcommand{\exercisetopic}{Point Processes}
-\newcommand{\exercisenum}{X2}
+\newcommand{\exercisetopic}{Integrate-and-fire models}
+\newcommand{\exercisenum}{13}
 \newcommand{\exercisedate}{January 19th, 2021}
 
 \input{../../exercisesheader}
 
-\firstpagefooter{Prof. Dr. Jan Benda}{}{jan.benda@uni-tuebingen.de}
+\firstpagefooter{Dr. Jan Benda}{}{jan.benda@uni-tuebingen.de}
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
@@ -14,113 +14,97 @@
 \input{../../exercisestitle}
 
 \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}
 
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\question \qt{Homogener Poisson Prozess}
-Wir wollen den homogenen Poisson Prozess benutzen um Spikes zu generieren,
-mit denen wir die Analysfunktionen des vorherigen \"Ubungszettel \"uberpr\"ufen k\"onnen.
-
-Ein homogener Poisson Prozess mit der Rate $\lambda$ (gemessen in Hertz) ist ein Punktprozess,
-bei dem die Wahrschienlichkeit eines Ereignisses unabh\"angig von der Zeit $t$ und
-unabh\"angig von vorherigen Ereignissen ist.
-Die Wahrscheinlichkeit $P$ eines Ereignisses innerhalb eines Bins der Breite $\Delta t$ ist
-\[ P = \lambda \cdot \Delta t \]
-f\"ur gen\"ugend kleine $\Delta t$.
-\begin{parts}
-  
-  \part Schreibe eine Funktion die $n$ homogene Poisson Spiketrains
-  einer gegebenen Dauer $T_{max}$ mit Rate $\lambda$ erzeugt.
-  \begin{solution}
-    \lstinputlisting{hompoissonspikes.m}
-  \end{solution}
-  
-  \part Benutze diese Funktion um einige Trials von Spikes zu erzeugen
-  und plotte diese als Spikeraster.
-  \begin{solution}
-    \begin{lstlisting}
-      spikes = hompoissonspikes( 10, 100.0, 0.5 );
-      spikeraster( spikes )
-    \end{lstlisting}
-    \mbox{}\\[-3ex]
-    \colorbox{white}{\includegraphics[width=0.7\textwidth]{poissonraster100hz}}
-  \end{solution}
-  
-  \part Berechne Histogramme aus den Interspikeintervallen von $n$
-  Poisson Spiketrains mit der Rate $\lambda=100$\,Hz. Ver\"andere
-  \"uber die Dauer $T_{max}$ der Spiketrains und die Anzahl $n$ der
-  Trials die Anzahl der Intervalle und ver\"andere auch die Binbreite
-  des Histograms (fang mit 1\,ms an). Wieviele Interspikeintervalle
-  werden ben\"otigt, um ein ``sch\"ones'' Histogramm zu erhalten? Wie
-  lange m\"usste man also von dem Neuron ableiten?
-  \begin{solution}
-    About 5000 intervals for 25 bins. This corresponds to a $5000 /
-    100\,\hertz = 50\,\second$ recording of a neuron firing with
-    100\,\hertz.
-  \end{solution}
-  
-  \part Vergleiche Interspike-Intervall Histogramme von Poisson-Spikes
-  verschiedener Raten $\lambda$ mit der theoretisch zu erwartenden Verteilung
-  der Intervalle $T$ des Poisson Prozesses
-  \[ p(T) = \lambda e^{-\lambda T} \; .\]
-  \begin{solution}
-    \lstinputlisting{hompoissonisih.m}
-    \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissonisih100hz}}
-    \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissonisih20hz}}
-  \end{solution}
-  
-  \part \extra Was pasiert mit den Histogrammen, wenn die Binbreite
-  der Histogramme kleiner als das bei der Erzeugung der Poisson
-  Spiketrains verwendete $\Delta t$ ist?
-  \begin{solution}
-    Die Bins zwischen der durch $\Delta t$ vorgegebenen
-    Diskretisierung haben den Wert 0. Dadurch werden aber die anderen
-    durch die Normierung h\"oher als sie sein sollten.
-  \end{solution}
-  
-  \part Plotte den Mittelwert der Interspikeintervalle, die
-  dazugeh\"orige Standardabweichung und den Variationskoeffizienten
-  als Funktion der Rate $\lambda$ des Poisson Prozesses. Vergleiche
-  die Ergebnisse mit den theoretischen Erwartungen (siehe Vorlesungsskript).
-  \begin{solution}
-    \lstinputlisting{hompoissonisistats.m}
-    \colorbox{white}{\includegraphics[width=0.98\textwidth]{poissonisistats}}
-  \end{solution}
-  
-  \part Plotte die seriellen Korrelationen von Poisson-Spiketrains und
-  erkl\"are kurz das Ergebniss.
-  \begin{solution}
-    \mbox{}\\[-2ex]\hspace*{2cm}
-    \colorbox{white}{\includegraphics[width=0.8\textwidth]{poissonserial100hz}}\\
-    Alle Korrelationen zwischen Interspikeintervallen sind Null, da
-    beim Poisson Prozess das Auftreten jedes Spikes unabh\"angig von
-    den vorherigen Spikes ist.
-  \end{solution}
-  
-  \part Vergleiche Histogramme von Spikecounts gez\"ahlt in Fenstern
-  der Breite $W$ mit der Poisson Verteilung
-  \[ P(k) = \frac{(\lambda W)^ke^{\lambda W}}{k!} \; , \] 
-  wobei die Rate $\lambda$ aus den Daten bestimmt werden
-  soll. Hinweis: es gibt eine \code{matlab} Funktion, die die
-  Fakult\"at $n!$ berechnet.
-  \begin{solution}
-    %\lstinputlisting{counthist.m}
-    \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz10ms}}
-    \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissoncounthistdist100hz100ms}}
-  \end{solution}
-  
-  \part Schreibe eine Funktion, die die mittlere Anzahl, die Varianz
-  und den Fano-Faktor der Anzahl der Spikes in einem Fenster der
-  Breite $W$ bestimmt. Benutze die Funktion, um diese Parameter f\"ur
-  verschiedene Fensterbreiten $W$ zu bestimmen. Zwei Plots sollen aus
-  den Ergebnissen angefertigt werden: (i) Varianz gegen Mittelwert der counts.
-  (ii) Fano Faktor als Funktion der Fensterbreite.
-  \begin{solution}
-    \lstinputlisting{fanoplot.m}
-    \colorbox{white}{\includegraphics[width=0.98\textwidth]{poissonfano100hz}}
-  \end{solution}
-  
-\end{parts}
 
+    \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{inversegauss.m}
+      \lstinputlisting{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{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{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{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}
diff --git a/pointprocesses/exercises/pointprocesses-3.tex b/pointprocesses/exercises/pointprocesses-3.tex
deleted file mode 100644
index 1af6dc7..0000000
--- a/pointprocesses/exercises/pointprocesses-3.tex
+++ /dev/null
@@ -1,163 +0,0 @@
-\documentclass[12pt,a4paper,pdftex]{exam}
-
-\newcommand{\exercisetopic}{Point Processes}
-\newcommand{\exercisenum}{X3}
-\newcommand{\exercisedate}{January 19th, 2021}
-
-\input{../../exercisesheader}
-
-\firstpagefooter{Prof. Dr. Jan Benda}{}{jan.benda@uni-tuebingen.de}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\begin{document}
-
-\input{../../exercisestitle}
-
-\begin{questions}
-  
-  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
-  \question \qt{Statistik von Spiketrains 2}
-  In Ilias findet ihr die Dateien \code{poisson.mat},
-  \code{pifou.mat}, und \code{lifadapt.mat}.  Jede dieser Dateien
-  enth\"alt mehrere Trials von Spiketrains von einer bestimmten Art
-  von Neuron. Die Spikezeiten sind in Sekunden gemessen.
-
-  Mit den folgenden Aufgaben wollen wir die Statistik der Spiketrains
-  der drei Neurone miteinander vergleichen.
-
-  Bereits im letzten \"Ubungszettel erstellte Funktionen d\"urfen (sollen!)
-  wiederverwendet werden.
-  \begin{parts}
-    \part Lade die Spiketrains aus den drei Dateien. Stelle sie in Rasterplots dar.
-    
-    \part Plotte die Interspike-Intervall Verteilungen.
-
-    Annotiere die Plots mit dem Mittelwert, der
-    Standardabweichung, und dem Variationskoeffizienten der
-    Interspikeintervalle, sowie der mittleren Feuerrate.
-
-    \part Vergleiche die ISI-Histogramme mit der ISI Verteilung eines Poisson Prozesses
-    der Rate $\lambda$:
-    \[ p(T) = \lambda e^{-\lambda T} \; .\]
-
-    \part Erstelle Return-Maps f\"ur die drei Spiketrains, also jedes
-    Interspike-Intervall $T_{i+1}$ gegen das vorherige Intervall $T_i$
-    geplottet.
-  
-    \part Schreibe eine Funktion, die die seriellen Korrelationen der
-    Interspikeintervalle f\"ur Lags bis zu \code{maxlag} berechnet und
-    plottet.  Die Seriellen Korrelationen $\rho_k$ f\"ur Lag $k$ der
-    Interspikeintervalle $T_i$ sind die Korrelationskoeffizienten
-    zwischen den Interspikeintervallen $T_i$ und den um das Lag $k$
-    verschobenen Intervallen $T_{i+k}$:
-    \[ \rho_k = \frac{\langle (T_{i+k} - \langle T \rangle)(T_i -
-      \langle T \rangle) \rangle}{\langle (T_i - \langle T
-      \rangle)^2\rangle} = \frac{{\rm cov}(T_{i+k}, T_i)}{{\rm
-        var}(T_i)} = {\rm corr}(T_{i+k}, T_i) \] 
-
-    Benutze diese Funktion, um die Interspikeintervall-Korrelationen
-    der drei Neurone zu vergleichen.
-    \begin{solution}
-      \lstinputlisting{../code/isiserialcorr.m}
-      \lstinputlisting{../code/plotserialcorr.m}
-      \colorbox{white}{\includegraphics[width=1\textwidth]{serialcorr}}
-    \end{solution}
-    
-  \end{parts}
-
-  \continue
-  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
-  \question \qt{Homogener Poisson Prozess}
-  Wir wollen den homogenen Poisson Prozess benutzen um Spikes zu
-  generieren, mit denen wir die Analysfunktionen des vorherigen
-  Aufgaben \"uberpr\"ufen k\"onnen.
-
-  Ein homogener Poisson Prozess mit der Rate $\lambda$ (gemessen in
-  Hertz) ist ein Punktprozess, bei dem die Wahrscheinlichkeit eines
-  Ereignisses unabh\"angig von der Zeit $t$ und unabh\"angig von
-  vorherigen Ereignissen ist.  Wenn wir die Zeitachse in kleine Bins
-  der Breite $\Delta t$ einteilen, dann ist
-  \[ P = \lambda \cdot \Delta t \]
-  die Wahrscheinlichkeit innerhalb eines Bins ein Ereignis (``spike'')
-  zu erhalten. $\Delta t$ muss daf\"ur klein genug sein, so dass $P<0.1$.
-  \begin{parts}
-    
-    \part Schreibe eine Funktion die $n$ homogene Poisson Spiketrains
-    einer gegebenen Dauer $T_{max}$ mit Rate $\lambda$ erzeugt.
-
-    Falls das nicht gelingt, benutze f\"ur die folgenden Aufgaben
-    soweit m\"oglich spikes aus der Datei \code{poisson.mat}.
-    \begin{solution}
-      \lstinputlisting{hompoissonspikes.m}
-    \end{solution}
-    
-    \part Benutze diese Funktion um einige Trials von Spikes zu erzeugen
-    und plotte diese als Spikeraster.
-    \begin{solution}
-      \begin{lstlisting}
-        spikes = hompoissonspikes( 10, 100.0, 0.5 );
-        spikeraster( spikes )
-      \end{lstlisting}
-      \mbox{}\\[-3ex]
-      \colorbox{white}{\includegraphics[width=0.7\textwidth]{poissonraster100hz}}
-    \end{solution}
-    
-    \part Berechne Histogramme aus den Interspikeintervallen von $n$
-    Poisson Spiketrains mit der Rate $\lambda=100$\,Hz. Wie viele bins
-    werden f\"ur ein ``sch\"ones'' ISI-Histogramm ungef\"ahr ben\"otigt?
-    Ver\"andere \"uber die Dauer $T_{max}$ der Spiketrains und die
-    Anzahl $n$ der Trials die Anzahl der Intervalle. Wieviele
-    Interspikeintervalle werden ben\"otigt, um ein ``sch\"ones''
-    Histogramm zu erhalten? Wie lange m\"usste man also von dem Neuron
-    ableiten?
-    \begin{solution}
-      About 5000 intervals for 25 bins. This corresponds to a $5000 /
-      100\,\hertz = 50\,\second$ recording of a neuron firing with
-      100\,\hertz.
-    \end{solution}
-    
-    \part Vergleiche Interspike-Intervall Histogramme von Poisson-Spikes
-    verschiedener Raten $\lambda$ mit der theoretisch zu erwartenden Verteilung
-    der Intervalle $T$ des Poisson Prozesses
-    \[ p(T) = \lambda e^{-\lambda T} \; .\]
-    Achte darauf, dass die Bins des Histograms nicht kleiner als $\Delta t$ sind!
-    \begin{solution}
-      \lstinputlisting{hompoissonisih.m}
-      \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissonisih100hz}}
-      \colorbox{white}{\includegraphics[width=0.48\textwidth]{poissonisih20hz}}
-    \end{solution}
-    
-    \part \extra Was passiert mit den Histogrammen, wenn die Binbreite
-    der Histogramme kleiner als das bei der Erzeugung der Poisson
-    Spiketrains verwendete $\Delta t$ ist?
-    \begin{solution}
-      Die Bins zwischen der durch $\Delta t$ vorgegebenen
-      Diskretisierung haben den Wert 0. Dadurch werden aber die anderen
-      durch die Normierung h\"oher als sie sein sollten.
-    \end{solution}
-    
-    \part Plotte den Mittelwert der Interspikeintervalle, die
-    dazugeh\"orige Standardabweichung und den Variationskoeffizienten
-    als Funktion der Rate $\lambda$ des Poisson Prozesses. Vergleiche
-    die Ergebnisse mit den theoretischen Erwartungen (siehe Vorlesungsskript).
-    \begin{solution}
-      \lstinputlisting{hompoissonisistats.m}
-      \colorbox{white}{\includegraphics[width=0.98\textwidth]{poissonisistats}}
-    \end{solution}
-    
-    \part Plotte die seriellen Korrelationen von Poisson-Spiketrains und
-    erkl\"are kurz das Ergebniss.
-    \begin{solution}
-      \mbox{}\\[-2ex]\hspace*{2cm}
-      \colorbox{white}{\includegraphics[width=0.8\textwidth]{poissonserial100hz}}\\
-      Alle Korrelationen zwischen Interspikeintervallen sind Null, da
-      beim Poisson Prozess das Auftreten jedes Spikes unabh\"angig von
-      den vorherigen Spikes ist.
-    \end{solution}
-    
-  \end{parts}
-
-
-\end{questions}
-
-\end{document}
diff --git a/pointprocesses/exercises/pointprocesses01-de.tex b/pointprocesses/exercises/pointprocesses01-de.tex
deleted file mode 100644
index ea8674d..0000000
--- a/pointprocesses/exercises/pointprocesses01-de.tex
+++ /dev/null
@@ -1,198 +0,0 @@
-\documentclass[12pt,a4paper,pdftex]{exam}
-
-\usepackage[german]{babel}
-\usepackage{pslatex}
-\usepackage[mediumspace,mediumqspace,Gray]{SIunits}      % \ohm, \micro
-\usepackage{xcolor}
-\usepackage{graphicx}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
-
-%%%%% layout %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot}
-\ifprintanswers
-\newcommand{\stitle}{L\"osungen}
-\else
-\newcommand{\stitle}{\"Ubung}
-\fi
-\header{{\bfseries\large \stitle}}{{\bfseries\large Punktprozesse}}{{\bfseries\large 27. Oktober, 2015}}
-\firstpagefooter{Prof. Dr. Jan Benda}{Phone: 29 74573}{Email:
-jan.benda@uni-tuebingen.de}
-\runningfooter{}{\thepage}{}
-
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
-
-%%%%% listings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{listings}
-\lstset{
-  language=Matlab,
-  basicstyle=\ttfamily\footnotesize,
-  numbers=left,
-  numberstyle=\tiny,
-  title=\lstname,
-  showstringspaces=false,
-  commentstyle=\itshape\color{darkgray},
-  breaklines=true,
-  breakautoindent=true,
-  columns=flexible,
-  frame=single,
-  xleftmargin=1em,
-  xrightmargin=1em,
-  aboveskip=10pt
-}
-
-%%%%% math stuff: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage{amsmath}
-\usepackage{amssymb}
-\usepackage{bm} 
-\usepackage{dsfont}
-\newcommand{\naZ}{\mathds{N}}
-\newcommand{\gaZ}{\mathds{Z}}
-\newcommand{\raZ}{\mathds{Q}}
-\newcommand{\reZ}{\mathds{R}}
-\newcommand{\reZp}{\mathds{R^+}}
-\newcommand{\reZpN}{\mathds{R^+_0}}
-\newcommand{\koZ}{\mathds{C}}
-
-%%%%% page breaks %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\continue}{\ifprintanswers%
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\continuepage}{\ifprintanswers%
-\newpage
-\else
-\vfill\hspace*{\fill}$\rightarrow$\newpage%
-\fi}
-\newcommand{\newsolutionpage}{\ifprintanswers%
-\newpage%
-\else
-\fi}
-
-%%%%% new commands %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\newcommand{\qt}[1]{\textbf{#1}\\}
-\newcommand{\pref}[1]{(\ref{#1})}
-\newcommand{\extra}{--- Zusatzaufgabe ---\ \mbox{}}
-\newcommand{\code}[1]{\texttt{#1}}
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\begin{document}
-
-\input{instructions}
-
-
-\begin{questions}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-  \question \qt{Statistik von Spiketrains}
-  In Ilias findet ihr die Dateien \code{poisson.mat},
-  \code{pifou.mat}, und \code{lifadapt.mat}.  Jede dieser Dateien
-  enth\"alt mehrere Trials von Spiketrains von einer bestimmten Art
-  von Neuron. Die Spikezeiten sind in Sekunden gemessen.
-
-  Mit den folgenden Aufgaben wollen wir die Statistik der Spiketrains
-  der drei Neurone miteinander vergleichen.
-  \begin{parts}
-    \part Lade die Spiketrains aus den drei Dateien. Achte darauf,
-    dass sie verschiedene Variablen\-namen bekommen.
-    \begin{solution}
-      \begin{lstlisting}
-        clear all
-        % not so good:
-        load poisson.mat
-        whos
-        poissonspikes = spikes;
-        load pifou.mat;
-        pifouspikes = spikes;
-        load lifadapt.mat;
-        lifadaptspikes = spikes;
-        clear spikes;
-        % better:
-        clear all
-        x = load( 'poisson.mat' );
-        poissonspikes = x.spikes;
-        x = load( pifou.mat' );
-        pifouspikes = x.spikes;
-        x = load( 'lifadapt.mat' );
-        lifadaptspikes = x.spikes;
-      \end{lstlisting}
-    \end{solution}
-    
-    \part Schreibe eine Funktion, die die Spikezeiten der ersten
-    $t_{max}$ Sekunden in einem Rasterplot visualisiert.  In jeder
-    Zeile des Rasterplots wird ein Spiketrain dargestellt. Jeder
-    einzelne Spike wird als senkrechte Linie zu der Zeit des
-    Auftretens des Spikes geplottet. Benutze die Funktion, um die
-    Spikeraster der ersten 1\,s der drei Neurone nebeneinander zu plotten.
-    \begin{solution}
-      \lstinputlisting{../code/spikeraster.m}
-      \lstinputlisting{../code/plotspikeraster.m}
-      \mbox{}\\[-3ex]
-      \colorbox{white}{\includegraphics[width=1\textwidth]{spikeraster}}
-    \end{solution}
-  
-    \part Schreibe eine Funktion, die einen einzigen Vektor mit den
-    Interspikeintervallen aller Trials von Spikezeiten zur\"uckgibt.
-    \begin{solution}
-      \lstinputlisting{../code/isis.m}
-    \end{solution}
-    
-    \part Schreibe eine Funktion, die ein normiertes Histogramm aus
-    einem Vektor von Interspikeintervallen, gegeben in Sekunden,
-    berechnet und dieses mit richtiger Achsenbeschriftung plottet.
-    Die Interspikeintervalle sollen dabei in Millisekunden angegeben
-    werden. Die Funktion soll zus\"atzlich den Mittelwert, die
-    Standardabweichung, und den Variationskoeffizienten der
-    Interspikeintervalle berechnen und diese im Plot mit angeben.
-    
-    Benutze die vorherige und diese Funktion, um die
-    Interspikeintervall Verteilung der drei Neurone zu vergleichen.
-    \begin{solution}
-      \lstinputlisting{../code/isihist.m}
-      \lstinputlisting{../code/plotisih.m}
-      \mbox{}\\[-3ex]
-      \colorbox{white}{\includegraphics[width=1\textwidth]{isihist}}
-    \end{solution}
-  
-    \part Schreibe eine Funktion, die die seriellen Korrelationen der
-    Interspikeintervalle f\"ur Lags bis zu \code{maxlag} berechnet und
-    plottet.  Die Seriellen Korrelationen $\rho_k$ f\"ur Lag $k$ der
-    Interspikeintervalle $T_i$ sind die Korrelationskoeffizienten
-    zwischen den Interspikeintervallen $T_i$ und den um das Lag $k$
-    verschobenen Intervallen $T_{i+k}$:
-    \[ \rho_k = \frac{\langle (T_{i+k} - \langle T \rangle)(T_i -
-      \langle T \rangle) \rangle}{\langle (T_i - \langle T
-      \rangle)^2\rangle} = \frac{{\rm cov}(T_{i+k}, T_i)}{{\rm
-        var}(T_i)} = {\rm corr}(T_{i+k}, T_i) \] 
-
-    Benutze diese Funktion, um die Interspikeintervall-Korrelationen
-    der drei Neurone zu vergleichen.
-    \begin{solution}
-      \lstinputlisting{../code/isiserialcorr.m}
-      \lstinputlisting{../code/plotserialcorr.m}
-      \colorbox{white}{\includegraphics[width=1\textwidth]{serialcorr}}
-    \end{solution}
-  
-    \part Schreibe eine Funktion, die aus Spikezeiten
-    Histogramme aus der Anzahl von Spikes, die in Fenstern gegebener L\"ange $W$
-    gez\"ahlt werden, erzeugt und plottet.
-
-    Wende diese Funktion auf die drei
-    Datens\"atze an. Probiere verschiedene Fenstergr\"o{\ss}en $W$ aus.
-    \begin{solution}
-      \lstinputlisting{../code/counthist.m}
-      \lstinputlisting{../code/plotcounthist.m}
-      \colorbox{white}{\includegraphics[width=1\textwidth]{counthist}}
-    \end{solution}
-
-    
-  \end{parts}
-  
-\end{questions}
-
-\end{document}
\ No newline at end of file
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diff --git a/pointprocesses/exercises/psth.tex b/pointprocesses/exercises/psth.tex
index a662af7..a9ff599 100644
--- a/pointprocesses/exercises/psth.tex
+++ b/pointprocesses/exercises/psth.tex
@@ -1,42 +1,20 @@
 \documentclass[12pt,a4paper,pdftex]{exam}
 
-\usepackage[german]{babel}
-\usepackage{natbib}
-\usepackage{graphicx}
-\usepackage[small]{caption}
-\usepackage{sidecap}
-\usepackage{pslatex}
-\usepackage{amsmath}
-\usepackage{amssymb}
-\setlength{\marginparwidth}{2cm}
-\usepackage[breaklinks=true,bookmarks=true,bookmarksopen=true,pdfpagemode=UseNone,pdfstartview=FitH,colorlinks=true,citecolor=blue]{hyperref}
+\newcommand{\exercisetopic}{Time-dependent firing rates}
+\newcommand{\exercisenum}{12}
+\newcommand{\exercisedate}{January 19th, 2021}
 
-%%%%% text size %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\usepackage[left=20mm,right=20mm,top=25mm,bottom=25mm]{geometry}
-\pagestyle{headandfoot} \header{{\bfseries\large Exercise
-    }}{{\bfseries\large Time-dependent firing rate}}{{\bfseries\large January, 14, 2020}}
-\firstpagefooter{Dr. Jan Grewe}{Phone: 29 74588}{Email:
-  jan.grewe@uni-tuebingen.de} \runningfooter{}{\thepage}{}
+\input{../../exercisesheader}
 
-\setlength{\baselineskip}{15pt}
-\setlength{\parindent}{0.0cm}
-\setlength{\parskip}{0.3cm}
-\renewcommand{\baselinestretch}{1.15}
+\firstpagefooter{Dr. Jan Grewe}{}{jan.grewe@uni-tuebingen.de}
 
-\newcommand{\code}[1]{\texttt{#1}}
-\renewcommand{\solutiontitle}{\noindent\textbf{Solution}\par\noindent}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \begin{document}
 
-\vspace*{-6.5ex}
-\begin{center}
-  \textbf{\Large Introduction to scientific computing.}\\[1ex]
-  {\large Jan Grewe, Jan Benda}\\[-3ex]
-  Abteilung Neuroethologie \hfill --- \hfill Institut f\"ur Neurobiologie \hfill --- \hfill \includegraphics[width=0.28\textwidth]{UT_WBMW_Black_RGB} \\
-\end{center}
+\input{../../exercisestitle}
 
 \begin{questions}
+  
   \question Plot the time-dependent firing rate of a neuron. Calculate
   the firing rate from the \emph{instantaneous firing rate} (based on the
   interspike interval). Use the \code{lifoustim.mat}. The dataset
@@ -45,14 +23,14 @@
   duration of each trial is 30 seconds.
 
   \begin{parts}
-    \part{} Write a function that takes three arguments: the spike
+    \part Write a function that takes three arguments: the spike
     times of a single trial, the trial duration and the temporal
     resolution. The function should return two variables: the time axis and the
     time-dependent firing rate.
-    \part{} Write a script that applies the above function to estimate
+    \part Write a script that applies the above function to estimate
     the time-dependent firing rate of each trial. Plot the firing rates of the individual responses
     and the average response as a function of time into the same graph.
-    \part{} Extend your program that it saves the figure with the width and height of 8.5\,cm using a fontsize of 10\,pt for labels.
+    \part Extend your program that it saves the figure with the width and height of 8.5\,cm using a fontsize of 10\,pt for labels.
     See Chapter 3 in the script, or browse the Matlab help for more
     information. Store the figure in pdf format.
   \end{parts}
@@ -65,10 +43,10 @@
 
   \question{} Some trials are different than the others.
   \begin{parts}
-    \part{} Use a rasterplot to identify them. In which sense
+    \part Use a rasterplot to identify them. In which sense
     are they different? Save the rasterplot in pdf
     format. Use the same figure specifications as above and make sure it is properly labeled.
-    \part{} Identify those trials in which the spike count
+    \part Identify those trials in which the spike count
     deviates more than $2\sigma$ (twice the standard deviation) from the average.
   \end{parts}
 \end{questions}