Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/teaching/scientificComputing

pointprocesses/exercises/psth.tex

@@ -14,7 +14,7 @@
 %%%%% 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 December, 04, 2018}}
+    }}{{\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}{}
 
@@ -38,23 +38,23 @@
 
 \begin{questions}
   \question Plot the time-dependent firing rate of a neuron. Calculate
-  the firing rate from the instantaneous firing rate (based on the
+  the firing rate from the \emph{instantaneous firing rate} (based on the
   interspike interval). Use the \code{lifoustim.mat}. The dataset
   contains three variables. 1st the spike times in different trials,
-  2nd the stimulus, and 3rd the temporal resolution. The total
+  2nd the stimulus, and 3rd the temporal resolution of the recording. The total
   duration of each trial is 30 seconds.
 
   \begin{parts}
     \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 the time values and the
-    firing rate in $Hz$.
+    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
-    the firing rate of each trial. Plot a single individual responses
-    and the average response as a function of time into the same plot.
-    \part{} Extend your program that it saves the figure with the width of 8.5\,cm using a fontsize of 10\,pt for labels.
-    See Chapter 3 in the script, or browse the Matlab help for information.
-    \part{} Store the figure in pdf format.
+    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.
+    See Chapter 3 in the script, or browse the Matlab help for more
+    information. Store the figure in pdf format.
   \end{parts}
 
   \question{} As before but use the binning method.
@@ -65,11 +65,11 @@
 
   \question{} Some trials are different than the others.
   \begin{parts}
-    \part{} Use the 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 size as above and make sure it is properly labeled.
-    \part{} Identify the trials in which the spike count
-    deviates more than $2\sigma$ from the average.
+    format. Use the same figure specifications as above and make sure it is properly labeled.
+    \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}
 

pointprocesses/lecture/pointprocesses-chapter.tex

@@ -23,6 +23,8 @@
 \item Explain difference stationary versus non-stationary point process
 \item Show different types of ISI histograms (regular, noisy, poisson, bursty, locking)
 \item Multitrial firing rates
+\item Better explain difference between ISI method and PSTHes. The
+  latter is dependent on precision of spike times the former not.
 \item Choice of bin width for PSTH, kernel width, also in relation sto
   stimulus time scale
 \item Kernle firing rate: discuss different kernel shapes, in

pointprocesses/lecture/pointprocesses.tex

@@ -15,8 +15,8 @@ series of spike times, which are termed \enterm{spiketrains}. If
 measurements are repeated we get several \enterm{trials} of
 spiketrains (\figref{rasterexamplesfig}).
 
-Spiketrains are times of events, the action potentials. The analysis
-of these leads into the realm of the so called \entermde[point
+Spiketrains are times of events, the action potentials. Analyzing
+spike trains leads into the realm of the so called \entermde[point
   process]{Punktprozess}{point processes}.
 
 \begin{figure}[ht]
@@ -25,11 +25,11 @@ of these leads into the realm of the so called \entermde[point
     ten trials of data illustrating the times of the action
     potentials. Each vertical dash illustrates the time at which an
     action potential was observed. Each line displays the events of
-    one trial. Shown is a stationary point process (left, homogeneous
-    point process with a rate $\lambda=20$\;Hz, left) and an
-    non-stationary point process (right, perfect integrate-and-fire
-    neuron dirven by Ohrnstein-Uhlenbeck noise with a time-constant
-    $\tau=100$\,ms, right).}
+    one trial. Shown is a stationary point process (homogeneous point
+    process with a rate $\lambda=20$\;Hz, left) and an non-stationary
+    point process (perfect integrate-and-fire neuron driven by
+    Ohrnstein-Uhlenbeck noise with a time-constant $\tau=100$\,ms,
+    right).}
 \end{figure}
 
 
@@ -46,7 +46,7 @@ of these leads into the realm of the so called \entermde[point
   \item Earthquake: defined by the dynamics of the pressure between
     tectonical plates.
   \item Communication calls in crickets/frogs/birds: shaped by
-    the dynamics of the nervous system and the muscle appartus.
+    the dynamics of the nervous system and the muscle apparatus.
   \end{itemize}
 \end{ibox}
 
@@ -333,10 +333,8 @@ How the firing rate $r(t)$ changes over time is the most important
 measure, when analyzing non-stationary spike trains. The unit of the
 firing rate is Hertz, i.e. the number of action potentials per
 second. There are different ways to estimate the firing rate and three
-of these methods are illustrated in \figref{psthfig}. All of
-these have their own justifications and pros- and cons. In the
-following we will discuss these methods more
-closely.
+of these are illustrated in \figref{psthfig}. All have their own
+justifications, their pros- and cons. 
 
 \begin{figure}[tp]
   \includegraphics[width=\columnwidth]{firingrates}

projects/README

@@ -1,10 +1,14 @@
-For new projects:
-Copy project_template/ and adapt according to your needs
+How to make a new project
+-------------------------
+Copy `project_template/` to your `project_NAME/` and adapt according to your needs.
+Rename `template.tex` to `NAME.tex` and write questions.
+Put data that are needed for the project into the `data/` subfolder.
+Put your solution into the `code/` subfolder.
+Don't forget to add the project files to git (`git add FILENAMES`).
 
-All projects:
-
-check for time information
 
+Projects
+--------
 
 1) project_activation_curve
 medium
@@ -22,83 +26,96 @@ b_0 is not defined
 OK, difficult
 no statistics, but kmeans
 
-5) project_face_selectivity
-medium-difficult
-(Marius monkey data)
-
-6) project_fano_slope
+5) project_fano_slope
 OK, difficult
 
-7) project_fano_test
-OK -
-
-8) project_fano_time
+6) project_fano_time
 OK, medium-difficult
 
-9) project_ficurves
+7) project_ficurves
 OK, medium
 Maybe add correlation test or fit statistics
 
-10) project_input_resistance
-medium
-What is the problem with this project? --> No difference between segments
-Improve questions
-
-11) project_isicorrelations
-medium-difficult
-Need to finish solution
-
-12) project_isipdffit
-Too technical
-
-13) project_lif
+8) project_lif
 OK, difficult
 no statistics
 
-14) project_mutualinfo
+9) project_mutualinfo
 OK, medium
 
-15) project_noiseficurves
+10) project_noiseficurves
 OK, simple-medium
 no statistics
 
-16) project_numbers
+11) project_numbers
 simple
 We might add some more involved statistical analysis
 
-17) project_pca_natural_images
+12) project_pca_natural_images
 medium
 Make a solution (->Lukas)
 
-18) project_photoreceptor
+13) project_photoreceptor
 OK, simple
 
-19) project_populationvector
+14) project_populationvector
 difficult
 OK
 
-20) project_power_analysis
+15) project_power_analysis
 medium
 
-21) project_qvalues
--
-Interesting! But needs solution.
-
-22) project_random_walk
+16) project_random_walk
 simple-medium
 
-23) project_serialcorrelation
+17) project_serialcorrelation
 OK, simple-medium
 
-24) project_shorttermpotentiation
-Write questions
+18) project_stimulus_reconstruction
+OK, difficult
 
-25) project_spectra
+19) project_vector_strength
+OK, medium-difficult
+
+
+Unfinished or bad projects
+--------------------------
+
+7) project_fano_test
+OK
+
+10) project_input_resistance
+medium
+What is the problem with this project? --> No difference between segments
+Improve questions
+
+12) project_isipdffit
+Too technical
+
+11) project_isicorrelations
+medium-difficult
+Quite technical, need to finish solution
+
+21) project_qvalues
 -
+Interesting! But needs solution.
+
+25) project_spectra
+Once we have the spectral chapter finished
 Needs improvements and a solution
 
-26) project_stimulus_reconstruction
-OK, difficult
 
-27) project_vector_strength
-OK, medium-difficult
+New project ideas:
+------------------
+
+1) project_face_selectivity
+Marius monkey data
+We need to work out a solution and results
+
+2) Firing rates and spikeing precision
+Data: Noise AM of grasshoppers
+Analysis: Spike detection, convolution rate versus ISI rate
+Discussion: How does spike precision influence rate measures?
+
+3) project_shorttermpotentiation
+We need better STD data (Alex Loebel? Jan G might have them!) Write questions.

projects/project_activation_curve/activation_curve.tex

@@ -0,0 +1,47 @@
+\documentclass[a4paper,12pt,pdftex]{exam}
+
+\newcommand{\ptitle}{Activation curve}
+\input{../header.tex}
+\firstpagefooter{Supervisor: Lukas Sonnenberg}{}%
+{email: lukas.sonnenberg@student.uni-tuebingen.de}
+
+\begin{document}
+
+\input{../instructions.tex}
+
+
+%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Estimation of the activation curve}
+Mutations in genes, encoding for ion channels, can result in a variety of neurological diseases like epilepsy, autism and intellectual disability. One way to find a possible treatment is to compare the voltage dependent kinetics of the mutated channel with its corresponding wild-type. These kinetics are described in voltage-clamp experiments and the subsequent data analysis. 
+
+In this task you will compute and compare the activation curves of the Nav1.6 wild-type channel and a variation named A1622D (the amino acid Alanine (A) at the 1622nd position is replaced by Aspartic acid (D)) that causes intellectual disability in humans.
+
+
+
+\begin{questions}
+    \question In the accompanying datasets you find recordings of cells with WT or A1622D transfections. The cells were all clamped to -70mV for some time to bring all ion channels in the same closed states. They are activated by a step change in the command voltage to a value described in the "steps" vector. The corresponding recorded current (in pA) and time (in ms) traces are also saved in the files.
+
+\begin{parts}
+    \part Plot the current traces of a WT and a A1622D cell. Because the number of transfected channels can vary the peak values have little value. Normalize the curves accordingly (what kind of normalization would be appropriate?). Can you already spot differences between the cells? 
+
+    \part \textbf{IV curve}: Find the peak values for each voltage step and plot them against the steps.
+
+    \part \textbf{Reversal potential}: Use the IV-curve to estimate the reversal potential of the sodium current. Consider a linear interpolation to increase the accuracy of your estimation.
+
+    \part \textbf{Activation curve}: The activation curve is a representation of the voltage dependence of the sodium conductivity. It is computed with a variation of Ohm's law:
+    \begin{equation}
+        g_{Na}(V) = \frac{I_{peak}}{V - V_{reversal}}
+    \end{equation}
+
+    \part \textbf{Compare the two variants}: To compare WT and A1622D activation curves you should first parameterise your data. Fit a sigmoid curve
+    \begin{equation}
+        g_{Na}(V) = g_{max,Na} / ( 1 + e^{ - \frac{V-V_{1/2}}{k}} )
+    \end{equation} 
+    to the activation curves. With $g_{max,Na}$ being the maximum conductivity, $V_{1/2}$ the half activation voltage and $k$ a slope factor. Now you can compare the two variants with a few simple parameters. What do the differences mean?
+
+    \part \textbf{BONUS question}: Take a good look at your raw data. What other differences can you see? How could you analyse these?
+
+  \end{parts}
+\end{questions}
+
+\end{document}