Improved page breaks

Makefile

@@ -10,6 +10,7 @@ chapters :
 
 $(BASENAME).pdf : $(BASENAME).tex header.tex $(SUBTEXS)
 	pdflatex -interaction=scrollmode $< | tee /dev/stderr | fgrep -q "Rerun to get cross-references right" && pdflatex -interaction=scrollmode $< || true
+	splitindex $(BASENAME).idx
 
 again :
 	pdflatex $(BASENAME).tex

bootstrap/lecture/bootstrap-chapter.tex

@@ -5,7 +5,7 @@
 \lstset{inputpath=../code}
 \graphicspath{{figures/}}
 
-\setcounter{page}{81}
+\setcounter{page}{69}
 \setcounter{chapter}{4}
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

bootstrap/lecture/bootstrap.tex

@@ -116,6 +116,7 @@ eine ganze Verteilung von Mittelwerten generieren
 (\figref{bootstrapsemfig}). Die Standardabweichung dieser Verteilung
 ist dann der gesuchte Standardfehler des Mittelwerts.
 
+\pagebreak[4]
 \begin{exercise}{bootstrapsem.m}{bootstrapsem.out}
   Erzeuge die Verteilung der Mittelwerte einer Stichprobe durch Bottstrapping,
   um daraus den Standardfehler des Mittelwerts zu bestimmen.

bootstrap/lecture/bootstrapsem.py

@@ -2,7 +2,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 plt.xkcd()
-fig = plt.figure( figsize=(6,4) )
+fig = plt.figure( figsize=(6,3.5) )
 rng = np.random.RandomState(637281)
 
 nsamples = 100

bootstrap/lecture/permutecorrelation.py

@@ -2,7 +2,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 plt.xkcd()
-fig = plt.figure( figsize=(6,4) )
+fig = plt.figure( figsize=(6,3.5) )
 rng = np.random.RandomState(637281)
 
 # generate correlated data:

designpattern/lecture/designpattern-chapter.tex

@@ -5,7 +5,7 @@
 \lstset{inputpath=../code}
 \graphicspath{{figures/}}
 
-\setcounter{page}{133}
+\setcounter{page}{121}
 \setcounter{chapter}{8}
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

designpattern/lecture/designpattern.tex

@@ -75,7 +75,6 @@ Zufallsgeneratoren geben oft nur Zufallszahlen mit festen Mittelwerten
 und Standardabweichungen (auch Skalierungen) zur\"uck. Multiplikation
 mit einem Faktor skaliert die Standardabweichung und Addition einer Zahl
 verschiebt den Mittelwert.
-
 \begin{lstlisting}[caption={Skalierung von Zufallszahlen}]
 % 100 random numbers draw from a Gaussian distribution with mean 0 and standard deviation 1.
 x = randn(100, 1);
@@ -85,7 +84,6 @@ mu = 4.8;
 sigma = 2.3;
 y = randn(100, 1)*sigma + mu;
 \end{lstlisting}
-
 Das gleiche Prinzip ist manchmal auch sinnvoll f\"ur \code{zeros()} oder \code{ones()}:
 \begin{lstlisting}[caption={Skalierung von \varcode{zeros()} und \varcode{ones()}}]
 x = -1:0.01:2;      % Vektor mit x-Werten
@@ -146,12 +144,10 @@ Die \code{histogram()} Funktion macht das mit den entsprechenden Parametern auto
 x = randn(100, 1);    % irgendwelche reellwertige Daten
 histogram(x, 'Normalization', 'pdf');
 \end{lstlisting}
-
 \begin{lstlisting}[caption={Probability mit der \varcode{histogram()}-Funktion}]
 x = randi(6, 100, 1);    % irgendwelche integer Daten
 histogram(x, 'Normalization', 'probability');
 \end{lstlisting}
-
 So geht es mit der \code{hist()}-Funktion:
 \begin{lstlisting}[caption={Probability-density-function mit der \varcode{hist()}- und \varcode{bar()}-Funktion}]
 x = randn(100, 1);         % irgendwelche reellwertige Daten
@@ -159,7 +155,6 @@ x = randn(100, 1);         % irgendwelche reellwertige Daten
 h = h/sum(h)/(b(2)-b(1));  % normieren zu einer Wahrscheinlichkeitsdichte
 bar(b, h);                 % und plotten.
 \end{lstlisting}
-
 \begin{lstlisting}[caption={Probability mit der \varcode{hist()}- und \varcode{bar()}-Funktion}]
 x = randi(6, 100, 1);    % irgendwelche integer Daten
 [h, b] = hist(x);        % Histogram berechnen

likelihood/lecture/likelihood-chapter.tex

@@ -5,7 +5,7 @@
 \lstset{inputpath=../code}
 \graphicspath{{figures/}}
 
-\setcounter{page}{101}
+\setcounter{page}{89}
 \setcounter{chapter}{6}
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

likelihood/lecture/likelihood.tex

@@ -118,10 +118,11 @@ diesem Mittelwert gezogen worden sind (\figref{mlemeanfig}).
   Wahrscheinlichkeiten) f\"ur den Mittelwert als Parameter. Vergleiche
   die Position der Maxima mit dem aus den Daten berechneten
   Mittelwert.
-  \newpage
+  \pagebreak[4]
 \end{exercise}
 
 
+\pagebreak[4]
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Kurvenfit als Maximum-Likelihood Sch\"atzung}
 Beim \determ{Kurvenfit} soll eine Funktion $f(x;\theta)$ mit den Parametern
@@ -251,7 +252,7 @@ z.B. dem Gradientenabstieg, gel\"ost wird \matlabfun{mle()}.
 \begin{exercise}{mlegammafit.m}{mlegammafit.out}
   Erzeuge Gammaverteilte Zufallszahlen und benutze Maximum-Likelihood,
   um die Parameter der Gammafunktion aus den Daten zu bestimmen.
-  \newpage
+  \pagebreak
 \end{exercise}
 
 

likelihood/lecture/mlepropline.py

@@ -2,7 +2,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 plt.xkcd()
-fig = plt.figure( figsize=(6,4) )
+fig = plt.figure( figsize=(6,3.5) )
 
 # the line:
 slope = 2.0

pointprocesses/code/binnedRate.m

@@ -1,19 +1,24 @@
-function [time, rate] = binned_rate(spike_times, bin_width, dt, t_max)
-% Calculates the firing rate with the binning method. The hist funciton is
-% used to count the number of spikes in each bin. 
+function [time, rate] = binned_rate(spikes, bin_width, dt, t_max)
+% PSTH computed with binning method. 
+% The hist funciton is used to count the number of spikes in each bin. 
+%
+% [time, rate] = binned_rate(spikes, bin_width, dt, t_max)
+%
 % Arguments: 
-%           spike_times, vector containing the times of the spikes.
-%           bin_width, the width of the bins in seconds.
-%           dt, the temporal resolution.
-%           t_max, the tiral duration.
+%   spikes   : vector containing the times of the spikes.
+%   bin_width: the width of the bins in seconds.
+%   dt       : the temporal resolution.
+%   t_max    : the tiral duration.
 %
-% Returns two vectors containing the time and the rate.
+% Returns:
+%   two vectors containing the time and the rate.
 
-time = 0:dt:t_max-dt;
-bins = 0:bin_width:t_max;
-rate = zeros(size(time));
-
-h = hist(spike_times, bins) ./ bin_width;
-for i = 2:length(bins)
-    rate(round(bins(i - 1) / dt) + 1:round(bins(i) / dt)) = h(i);
+    time = 0:dt:t_max-dt;
+    bins = 0:bin_width:t_max;
+    rate = zeros(size(time));
+    h = hist(spikes, bins) ./ bin_width;
+    for i = 2:length(bins)
+        rate(round(bins(i - 1) / dt) + 1:round(bins(i) / dt)) = h(i);
+    end
 end
+

pointprocesses/code/convolutionRate.m

@@ -1,16 +1,20 @@
-function [time, rate] = convolution_rate(spike_times, sigma, dt, t_max)
-% Calculates the firing rate with the convolution method. 
+function [time, rate] = convolution_rate(spikes, sigma, dt, t_max)
+% PSTH computed with convolution method. 
+%
+% [time, rate] = convolution_rate(spikes, sigma, dt, t_max)
+%
 % Arguments: 
-%           spike_times, a vector containing the spike times.
-%           sigma, the standard deviation of the Gaussian kernel in seconds.
-%           dt, the temporal resolution in seconds.
-%           t_max, the trial duration in seconds.
+%   spikes: a vector containing the spike times.
+%   sigma : the standard deviation of the Gaussian kernel in seconds.
+%   dt    : the temporal resolution in seconds.
+%   t_max : the trial duration in seconds.
 %
-% Returns two vectors containing the time and the rate.
+% Returns:
+    two vectors containing the time and the rate.
 
 time = 0:dt:t_max - dt;
 rate = zeros(size(time));
-spike_indices = round(spike_times / dt);
+spike_indices = round(spikes / dt);
 rate(spike_indices) = 1;
 kernel = gauss_kernel(sigma, dt);
 

pointprocesses/code/instantaneousRate.m

@@ -1,22 +1,24 @@
-function [time, rate] = instantaneous_rate(spike_times, dt, t_max)
-% Function calculates the firing rate as the inverse of the interspike
-% interval.
+function [time, rate] = instantaneous_rate(spikes, dt, t_max)
+% Firing rate as the inverse of the interspike interval.
+%
+% [time, rate] = instantaneous_rate(spikes, dt, t_max)
+%
 % Arguments:
-%            spike_times, vector containing the times of the spikes.
-%            dt, the temporal resolutions of the recording.
-%            t_max, the duration of the trial.
+%   spikes: vector containing the times of the spikes.
+%   dt    : the temporal resolutions of the recording.
+%   t_max : the duration of the trial.
 %
-% Returns the vector representing time and a vector containing the rate.
+% Returns:
+%   the vector representing time and a vector containing the rate.
 
-time = 0:dt:t_max-dt;
-rate = zeros(size(time));
+    time = 0:dt:t_max-dt;
+    rate = zeros(size(time));
 
-isis = diff([0 spike_times]);
-inst_rate = 1 ./ isis;
-spike_indices = [1 round(spike_times ./ dt)];
+    isis = diff([0 spikes]);
+    inst_rate = 1 ./ isis;
+    spike_indices = [1 round(spikes ./ dt)];
 
-for i = 2:length(spike_indices)
-    rate(spike_indices(i - 1):spike_indices(i)) = inst_rate(i - 1);
+    for i = 2:length(spike_indices)
+        rate(spike_indices(i - 1):spike_indices(i)) = inst_rate(i - 1);
+    end
 end
-    
-

pointprocesses/code/isiHist.m

@@ -1,7 +1,7 @@
-function [pdf, centers] = isi_hist(isis, binwidth)
+function [pdf, centers] = isiHist(isis, binwidth)
 % Compute normalized histogram of interspike intervals.
 %
-% [pdf, centers] = isi_hist(isis, binwidth)
+% [pdf, centers] = isiHist(isis, binwidth)
 %
 % Arguments:
 %   isis: vector of interspike intervals in seconds

pointprocesses/code/isis.m

@@ -2,7 +2,12 @@ function isivec = isis( spikes )
 % returns a single list of isis computed from all trials in spikes
 %
 % isivec = isis( spikes )
+%
+% Arguments:
 %   spikes: a cell array of vectors of spike times in seconds
+%   isivec: a column vector with all the interspike intervalls
+%
+% Returns:
 %   isivec: a column vector with all the interspike intervalls
 
     isivec = [];
@@ -13,4 +18,3 @@ function isivec = isis( spikes )
         isivec = [ isivec; difftimes(:) ];
     end
 end
-

pointprocesses/code/plotISIHist.m

@@ -1,7 +1,7 @@
-function plot_isi_hist(isis, binwidth)
+function plotISIHist(isis, binwidth)
 % Plot and annotate histogram of interspike intervals.
 %
-% isihist(isis, binwidth)
+% plotISIHist(isis, binwidth)
 %
 % Arguments:
 %   isis: vector of interspike intervals in seconds
@@ -9,9 +9,9 @@ function plot_isi_hist(isis, binwidth)
 
     % compute normalized histogram:
     if nargin < 2
-        [pdf, centers] = isi_hist(isis);
+        [pdf, centers] = isiHist(isis);
     else
-        [pdf, centers] = isi_hist(isis, binwidth);
+        [pdf, centers] = isiHist(isis, binwidth);
     end
 
     % plot:

pointprocesses/code/reconstructStimulus.m

@@ -1,19 +1,19 @@
-function s_est = reconstructStimulus(spike_times, sta, stim_duration, dt)
-% Function estimates the stimulus from the Spike-Triggered-Average
-% (sta).
+function s_est = reconstructStimulus(spikes, sta, duration, deltat)
+% Estimate the stimulus from the spike-triggered-average (STA).
+%
+% s_est = reconstructStimulus(spikes, sta, duration, deltat)
+%
 % Arguments:
-%         spike_times, a vector containing the spike times in seconds.
-%         sta, a vector containing the spike-triggered-average.
-%         stim_duration, the total duration of the stimulus.
-%         dt, the sampling interval given in seconds.
+%     spikes  : a vector containing the spike times in seconds.
+%     sta     : a vector containing the spike-triggered-average.
+%     duration: the total duration of the stimulus.
+%     deltat  : the time step of the stimulus in seconds.
 %
 % Returns:
-%         the estimated stimulus.
-
-s_est = zeros(round(stim_duration / dt), 1);
-
-binary_spikes = zeros(size(s_est));
-binary_spikes(round(spike_times ./ dt)) = 1;
-
-s_est = conv(binary_spikes, sta, 'same');
+%     s_est: vector with the estimated stimulus.
 
+    s_est = zeros(round(duration / deltat), 1);
+    binary_spikes = zeros(size(s_est));
+    binary_spikes(round(spikes ./ deltat)) = 1;
+    s_est = conv(binary_spikes, sta, 'same');
+end

pointprocesses/code/spikeTriggeredAverage.m

@@ -1,32 +1,31 @@
-function [sta, std_sta, valid_spikes] = spikeTriggeredAverage(stimulus, spike_times, count, sampling_rate)
-% Function estimates the Spike-Triggered-Average (sta).
+function [sta, std_sta, n_spikes] = spikeTriggeredAverage(stimulus, spikes, count, deltat)
+% Estimate the spike-triggered-average (STA).
+%
+% [sta, std_sta, n_spikes] = spikeTriggeredAverage(stimulus, spikes, count, deltat)
 %
 % Arguments:
-%           stimulus, a vector containing stimulus intensities
-%           as a function of time.
-%           spike_times, a vector containing the spike times 
-%           in seconds.
-%           count, the number of datapoints that are taken around
-%           the spike times.
-%           sampling_rate, the sampling rate of the stimulus.
+%     stimulus: vector of stimulus intensities as a function of time.
+%     spikes  : vector with spike times in seconds.
+%     count   : number of datapoints that are taken around the spike times.
+%     deltat  : the time step of the stimulus in seconds.
 %
 % Returns: 
-%           the sta, a vector containing the staandard deviation and 
-%           the number of spikes taken into account.
+%     sta     : vector with the STA.
+%     std_sta : standard deviation of the STA.
+%     n_spikes: number of spikes contained in STA.
 
-snippets = zeros(numel(spike_times), 2*count);
-valid_spikes = 1;
-for i = 1:numel(spike_times)
-    t = spike_times(i);
-    index = round(t*sampling_rate);
-    if index <= count || (index + count) > length(stimulus)
-        continue
+    snippets = zeros(numel(spikes), 2*count);
+    n_spikes = 0;
+    for i = 1:numel(spikes)
+        t = spikes(i);
+        index = round(t/deltat);
+        if index <= count || (index + count) > length(stimulus)
+            continue
+        end
+        snippets(n_spikes,:) = stimulus(index-count:index+count-1);
+        n_spikes = n_spikes + 1;
     end
-    snippets(valid_spikes,:) = stimulus(index-count:index+count-1);
-    valid_spikes = valid_spikes + 1;
+    snippets(n_spikes+1:end,:) = [];
+    sta = mean(snippets, 1);
+    std_sta = std(snippets,[],1);
 end
-
-snippets(valid_spikes:end,:) = [];
-
-sta = mean(snippets, 1);
-std_sta = std(snippets,[],1);

pointprocesses/lecture/pointprocesses.tex

@@ -86,7 +86,7 @@ heisen die Intervalle auch \determ{Interspikeintervalle}
 \"ublichen Gr\"o{\ss}en beschrieben werden.
 
 \begin{figure}[t]
-  \includegraphics[width=1\textwidth]{isihexamples}\hfill
+  \includegraphics[width=0.96\textwidth]{isihexamples}\vspace{-2ex}
   \titlecaption{\label{isihexamplesfig}Interspikeintervall Histogramme}{der in
     \figref{rasterexamplesfig} gezeigten Spikes.}
 \end{figure}
@@ -113,15 +113,15 @@ heisen die Intervalle auch \determ{Interspikeintervalle}
   \frac{\sigma_{ISI}^2}{2\mu_{ISI}^3}$.
 \end{itemize}
 
-\begin{exercise}{isi_hist.m}{}
-  Schreibe eine Funktion \code{isi\_hist()}, die einen Vektor mit Interspikeintervallen
+\begin{exercise}{isiHist.m}{}
+  Schreibe eine Funktion \code{isiHist()}, die einen Vektor mit Interspikeintervallen
   entgegennimmt und daraus ein normiertes Histogramm der Interspikeintervalle
   berechnet.
 \end{exercise}
 
-\begin{exercise}{plot_isi_hist.m}{}
+\begin{exercise}{plotISIHist.m}{}
   Schreibe eine Funktion, die die Histogrammdaten der Funktion
-  \code{isi\_hist()} entgegennimmt, um das Histogramm zu plotten.  Im
+  \code{isiHist()} entgegennimmt, um das Histogramm zu plotten.  Im
   Plot sollen die Interspikeintervalle in Millisekunden aufgetragen
   werden.  Das Histogramm soll zus\"atzlich mit Mittelwert,
   Standardabweichung und Variationskoeffizient der
@@ -155,6 +155,7 @@ Intervalls mit sich selber).
 \begin{exercise}{isiserialcorr.m}{}
   Schreibe eine Funktion \code{isiserialcorr()}, die einen Vektor mit Interspikeintervallen
   entgegennimmt und daraus die seriellen Korrelationen berechnet und plottet.
+  \pagebreak[4]
 \end{exercise}
 
 
@@ -395,6 +396,7 @@ vorkommen k\"onnen nicht aufgl\"ost werden. Mit der Wahl der Binweite
 wird somit eine Annahme \"uber die relevante Zeitskala des Spiketrains
 gemacht.
 
+\pagebreak[4]
 \begin{exercise}{binnedRate.m}{}
   Implementiere die Absch\"atzung der Feuerrate mit der ``binning''
   Methode. Plotte das PSTH.
@@ -435,6 +437,7 @@ Binweite, die zeitliche Aufl\"osung von $r(t)$. Die Breite des Kerns
 macht also auch wieder eine Annahme \"uber die relevante Zeitskala des
 Spiketrains.
 
+\pagebreak[4]
 \begin{exercise}{convolutionRate.m}{}
   Verwende die Faltungsmethode um die Feuerrate zu bestimmen. Plotte
   das Ergebnis.
@@ -490,10 +493,13 @@ die Zellantwort mit dem STA verfaltet.
   Implementiere eine Funktion, die den STA ermittelt. Verwende dazu
   den Datensatz \file{sta\_data.mat}. Die Funktion sollte folgende
   R\"uckgabewerte haben:
+  \vspace{-1ex}
   \begin{itemize}
+    \setlength{\itemsep}{0ex}
   \item den Spike-Triggered-Average.
   \item die Standardabweichung der individuellen STAs.
-  \item die Anzahl Aktionspotentiale, die dem STA zugrunde liegen.
+  \item die Anzahl Aktionspotentiale, die zur Berechnung des STA verwendet wurden.
+  \vspace{-2ex}
   \end{itemize}
 \end{exercise}
 

regression/lecture/regression-chapter.tex

@@ -5,7 +5,7 @@
 \lstset{inputpath=../code}
 \graphicspath{{figures/}}
 
-\setcounter{page}{89}
+\setcounter{page}{77}
 \setcounter{chapter}{5}
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

regression/lecture/regression.tex

@@ -84,7 +84,8 @@ zus\"atzlich gro{\ss}e Abst\"ande st\"arker gewichtet.
   Schreibe eine Funktion \code{meanSquareError()}, die die mittlere
   quadratische Abweichung zwischen einem Vektor mit den beobachteten
   Werten $y$ und einem Vektor mit den entsprechenden Vorhersagen
-  $y^{est}$ berechnet.\newpage
+  $y^{est}$ berechnet.
+  \pagebreak[4]
 \end{exercise}
 
 
@@ -315,6 +316,7 @@ partielle Ableitung nach $m$ durch
   Parametersatz $(m, b)$ der Geradengleichung als 2-elementigen Vektor
   sowie die $x$- und $y$-Werte der Messdaten als Argumente
   entgegennimmt und den Gradienten an dieser Stelle zur\"uckgibt.
+  \pagebreak[4]
 \end{exercise}
 
 \begin{exercise}{errorGradient.m}{}
@@ -376,6 +378,7 @@ Punkte in Abbildung \ref{gradientdescentfig} gro{\ss}.
     Funktion der Optimierungsschritte zeigt.
   \item Erstelle einen Plot, der den besten Fit in die Daten plottet.
   \end{enumerate}
+  \pagebreak
 \end{exercise}