Merge branch 'master' of whale.am28.uni-tuebingen.de:scientificComputing

plotting/code/errorbarplot.m

@@ -0,0 +1,43 @@
+% fake some data
+x_data = 0:2*pi/10:2*pi;
+sym_data = sin(repmat(x_data, 10,1)) * 0.75 + (randn(10, length(x_data)) .* cos(repmat(x_data, 10,1)));
+asym_data = sin(repmat(x_data, 10,1)) * 0.75 + (randn(10, length(x_data)) .* cos(repmat(x_data, 10,1)) + 0.5);
+
+% get some data characteristics
+avg_sym = mean(sym_data, 1);
+err_sym = std(sym_data, [], 1);
+avg_asym = median(asym_data, 1);
+err_upper_asym = prctile(asym_data, 75, 1);
+err_lower_asym = prctile(asym_data, 25, 1);
+
+fig = figure();
+set(fig, 'paperunits', 'centimeters', 'papersize', [15 6.5], ...
+    'paperposition', [0.0 0.0 15, 6.5], 'color', 'white')
+subplot(1,3,1)
+errorbar(x_data, avg_sym, err_sym, 'marker', 'o')
+xlim([-.5, 6.5])
+xlabel('x-data')
+ylabel('y-data')
+box('off')
+
+subplot(1,3,2)
+errorbar(x_data, avg_asym, err_lower_asym, err_upper_asym, 'marker', 'o')
+xlim([-.5, 6.5])
+yticklabels([])
+xlabel('x-data')
+box('off')
+
+subplot(1,3,3)
+hold on
+p = fill(cat(2, x_data, fliplr(x_data)), ...
+    cat(2, avg_sym - err_sym, fliplr(avg_sym + err_sym)), ...
+    'b');
+p.FaceAlpha = 0.125;
+p.EdgeColor = 'w';
+xlim([-.5, 6.5])
+yticklabels([])
+box('off')
+xlabel('x-data')
+plot(x_data, avg_sym, 'b', 'linewidth', 1.)
+
+saveas(fig, '../lecture/images/errorbars', 'pdf')

projects/project_ficurves/ficurves.tex

@@ -0,0 +1,73 @@
+\documentclass[a4paper,12pt,pdftex]{exam}
+
+\newcommand{\ptitle}{F-I curves}
+\input{../header.tex}
+\firstpagefooter{Supervisor: Jan Grewe}{phone: 29 74588}%
+{email: jan.grewe@uni-tuebingen.de}
+
+\begin{document}
+
+\input{../instructions.tex}
+
+
+%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Quantifying the responsiveness of a neuron by its F-I curves}
+The responsiveness of a neuron is often quantified using an F-I
+curve. The F-I curve plots the \textbf{F}iring rate of the neuron as a
+function of the stimulus \textbf{I}ntensity.
+
+\begin{questions}
+  \question In the accompanying datasets you find the
+  \textit{spike\_times} of an P-unit electroreceptor of the weakly
+  electric fish \textit{Apteronotus leptorhynchus} to a stimulus of a
+  certain intensity, i.e. the \textit{contrast}. The spike times are
+  given in milliseconds relative to the stimulus onset.
+  \begin{parts}
+    \part For each stimulus intensity estimate the average response
+    (PSTH) and plot it. You will see that there are three parts.  (i)
+    The first 200\,ms is the baseline (no stimulus) activity. (ii)
+    During the next 1000\,ms the stimulus was switched on. (iii) After
+    stimulus offset the neuronal activity was recorded for further
+    825\,ms.
+
+    \part Extract the neuron's activity for every 50\,ms after
+    stimulus onset and for one 50\,ms slice before stimulus onset.
+
+    For each time slice plot the resulting F-I curve by plotting the
+    computed firing rates against the corresponding stimulus
+    intensity, respectively the contrast.
+
+    \part Fit a Boltzmann function to each of the F-I-curves. The
+    Boltzmann function is a sigmoidal function and is defined as
+    \begin{equation}
+       f(x) = \frac{\alpha-\beta}{1+e^{-k(x-x_0)}}+\beta \; .
+    \end{equation}
+    $x$ is the stimulus intensity, $\alpha$ is the starting
+    firing rate, $\beta$ the saturation firing rate, $x_0$ defines the
+    position of the sigmoid, and $k$ (together with $\alpha-\beta$)
+    sets the slope.
+
+    Before you do the fitting, familiarize yourself with the four
+    parameter of the Boltzmann function. What is its value for very
+    large or very small stimulus intensities? How does the Boltzmann
+    function change if you change either of the parameter?
+
+    How could you get good initial estimates for the parameter?
+
+    Do the fits and show the resulting Boltzmann functions together
+    with the corresponding data.
+
+    \part Illustrate how the F-I curves change in time also by means
+    of the parameter you obtained from the fits with the Boltzmann
+    function. 
+
+    Which parameter stay the same, which ones change with time?
+
+    Support your conclusion with appropriate statistical tests.
+
+    \part Discuss you results with respect to encoding of different
+    stimulus intensities.
+  \end{parts}
+\end{questions}
+
+\end{document}

projects/project_onset_fi/onset_fi.tex

@@ -1,47 +0,0 @@
-\documentclass[a4paper,12pt,pdftex]{exam}
-
-\newcommand{\ptitle}{Onset f-I curve}
-\input{../header.tex}
-\firstpagefooter{Supervisor: Jan Grewe}{phone: 29 74588}%
-{email: jan.grewe@uni-tuebingen.de}
-
-\begin{document}
-
-\input{../instructions.tex}
-
-
-%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
-\section{Quantifying the responsiveness of a neuron by its F-I curve}
-The responsiveness of a neuron is often quantified using an F-I
-curve. The F-I curve plots the \textbf{F}iring rate of the neuron as a
-function of the stimulus \textbf{I}ntensity.
-
-\begin{questions}
-  \question In the accompanying datasets you find the
-  \textit{spike\_times} of an P-unit electroreceptor of the weakly
-  electric fish \textit{Apteronotus leptorhynchus} to a stimulus of a
-  certain intensity, i.e. the \textit{contrast}. The spike times are
-  given in milliseconds relative to the stimulus onset.
-  \begin{parts}
-    \part For each stimulus intensity estimate the average response
-    (PSTH) and plot it. You will see that there are three parts.  (i)
-    The first 200\,ms is the baseline (no stimulus) activity. (ii)
-    During the next 1000\,ms the stimulus was switched on. (iii) After
-    stimulus offset the neuronal activity was recorded for further
-    825\,ms.
-    \part Extract the neuron's activity in the first 50\,ms after
-    stimulus onset and plot it against the stimulus intensity,
-    respectively the contrast, in an appropriate way.
-    \part Fit a Boltzmann function to the FI-curve. The Boltzmann function
-    is defined as:
-    \begin{equation}
-       y=\frac{\alpha-\beta}{1+e^{(x-x_0)/\Delta x}}+\beta,
-    \end{equation}
-    where $\alpha$ is the starting firing rate, $\beta$ the saturation
-    firing rate, $x$ the current stimulus intensity, $x_0$ starting
-    stimulus intensity, and $\Delta x$ a measure of the slope.
-    \part Plot the fit into the data.
-  \end{parts}
-\end{questions}
-
-\end{document}

projects/project_steady_state_fi/Makefile

@@ -1,3 +0,0 @@
-ZIPFILES=
-
-include ../project.mk

projects/project_steady_state_fi/steady_state_fi.tex

@@ -1,45 +0,0 @@
-\documentclass[a4paper,12pt,pdftex]{exam}
-
-\newcommand{\ptitle}{Steady-state f-I curve}
-\input{../header.tex}
-\firstpagefooter{Supervisor: Jan Grewe}{phone: 29 74588}%
-{email: jan.grewe@uni-tuebingen.de}
-
-\begin{document}
-
-\input{../instructions.tex}
-
-
-%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
-\section*{Quantifying the responsiveness of a neuron using the F-I curve.}
-The responsiveness of a neuron is often quantified using an F-I
-curve. The F-I curve plots the \textbf{F}iring rate of the neuron as a function
-of the stimulus \textbf{I}ntensity.
-
-\begin{questions}
-  \question In the accompanying datasets you find the
-  \textit{spike\_times} of an P-unit electrorecptor of the weakly
-  electric fish \textit{Apteronotus leptorhynchus} to a stimulus of a
-  certain intensity, i.e. the \textit{contrast}. The contrast is also
-  part of the file name itself.
-  \begin{parts}
-    \part Estimate for each stimulus intensity the average response 
-    (PSTH) and plot it. You will see that there are three parts.  (i)
-    The first 200 ms is the baseline (no stimulus) activity. (ii) During
-    the next 1000 ms the stimulus was switched on. (iii) After stimulus
-    offset the neuronal activity was recorded for further 825 ms.
-    \part Extract the neuron's activity in the last 200 ms before
-    stimulus offset and plot it against the stimulus intensity or the
-    contrast, respectively.
-    \part Fit a Boltzmann function to the FI-curve. The Boltzmann function
-    is defined as:
-    \begin{equation}
-       y=\frac{\alpha-\beta}{1+e^{(x-x_0)/\Delta x}}+\beta,
-    \end{equation}
-    where $\alpha$ is the starting firing rate, $\beta$ the saturation
-    firing rate, $x$ the current stimulus intensity, $x_0$ starting
-    stimulus intensity, and $\Delta x$ a measure of the slope.
-  \end{parts}
-\end{questions}
-
-\end{document}