ideas for more chapters

header.tex

@@ -1,6 +1,8 @@
 %%%%% title %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \title{\textbf{\huge\sffamily\tr{Introduction to\\[1ex] Scientific Computing}%
        {Einf\"uhrung in die\\[1ex] wissenschaftliche Datenverarbeitung}}}
+%\title{\textbf{\huge\sffamily\tr{Scientific Computing for Neurobiologists}%
+%       {Wissenschaftliche Datenverarbeitung f\"ur Neurobiologen}}}
 
 \author{{\LARGE Jan Grewe \& Jan Benda}\\[5ex]Neuroethology Lab\\[2ex]%
         \includegraphics[width=0.3\textwidth]{UT_WBMW_Rot_RGB}\vspace{3ex}}

pointprocesses/code/isiserialcorr.m

@@ -32,4 +32,3 @@ function isicorr = isiserialcorr(isivec, maxlag)
         ylabel('\rho_k')
     end
 end
-

pointprocesses/lecture/pointprocesses.tex

@@ -50,7 +50,7 @@ spike trains leads into the realm of the so called \entermde[point
   \end{itemize}
 \end{ibox}
 
-\begin{figure}[t]
+\begin{figure}[tb]
   \texpicture{pointprocessscetch}
   \titlecaption{\label{pointprocessscetchfig} Statistics of point
     processes.}{A point process is a sequence of instances in time

scientificcomputing-script.tex

@@ -39,8 +39,22 @@
 \listofiboxfs
 
 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \mainmatter
 
+% add introduction:
+% - data analysis skills way beyond basic statistical test are at the
+%   core of modern neuroscience
+% - *understanding* of basic concepts of data analysis concepts is important
+% - most concepts are also quite relevant for the brain itself!
+% - modern approaches:
+%   * open source science (open data, open code, open algorithmns in contrast 
+%     to closed source license models)
+%   * knowledge is freedom ...
+%   * do not consume what companies offer you but know what you want and implement it
+%   * python as a modern and popular programming language in modern science
+%     (i.e. machine learning, others?) with a strong community support.
+
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \part{Programming basics}
 
@@ -57,10 +71,10 @@
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \part{Data analysis}
+%\part{Basic statistics}
+% With an intro saying statistical tests are overrated!
 
-% add chapter on simulations (draw random numbers, draw random functions, euler forward, odeint)
+% add chapter on stochastic simulations (draw random numbers, draw random functions)
-% !!! this would be a nice and simple starter !!!
-% introduces derivatives which are also needed for fitting
 %\includechapter{simulations}
 
 \includechapter{statistics}
@@ -71,32 +85,53 @@
 
 \includechapter{regression}
 
+% add chapter on nonlinear fitting (methods, initial values, local minima, power law fits on log-log data)
+
 \includechapter{likelihood}
 
-% add chapter on generalized linear models (versus ANOVA)
+% add chapter on multivariate analysis/generalized linear models (in addition to ANOVA)
 
-% add chapter on nonlinear fitting (methods, initial values, local minima, power law fits on log-log data)
 
-\includechapter{pointprocesses}
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%\part{Time series}
+
+% add chapter on simulations of dynamical systems/time series (euler
+% forward, odeint, iterated maps, dynamical system, bifurcations?)
+% !!! this would be a nice and simple starter !!!
+% introduces derivatives which are also needed for fitting
 
 % add chapter on time series (amplitude distribution, autocorrelation, crosscorrelation)
 %\includechapter{spectral}
 
-% add chapter on filtering and envelopes
+% add chapter on filtering (1. order, butter and kernel convolution) and envelopes
 
-% add chapter on event detection, signal detection, ROC
+% add chapter on event detection (local maxima, threshold crossings,
+% peak detection), signal detection, ROC
 
-% add chapter on mutual information
+\includechapter{pointprocesses}
 
-% add chapters on linear algebra, PCA, clustering, see linearalgebra/
+% add a chapter on simulating point-processes/spike trains
+% (Poisson spike trains, integrate-and-fire models)
+
+% add chapter on information theory, mutual information, stimulus reconstruction, coherence
+
+% add a chapter on Bayesian inference (the Neuroscience of it and a
+% bit of application for statistical problems).
 
-% add chapter on simple machine learning
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%\part{Machine learning}
 
+% add chapters on linear algebra, PCA, clustering, see linearalgebra/
+
+% add chapter on simple machine learning, perceptron
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %\part{Tools}
-% Latex -> see latex/
+
-% markdown, html
+% add chapter on Markup and LaTeX -> see latex/
+% with an introduction on mark-up in general, an outlook to markdown, html, xml, jason, yaml
+
 % Makefile
 % distributed computing (ssh and grid engines)
 %

simulations/lecture/simulations.tex

@@ -12,14 +12,14 @@ well controlled data sets that are free of confounding pecularities of
 real data sets. With simulated data we can also test our own analysis
 functions. More importantly, by means of simulations we can explore
 possible outcomes of our experiments before we even started the
-experiment or we can explore possible results for regimes that we
+experiment. We could even explore possible results for regimes that we
 cannot test experimentally. How dynamical systems, like for example
-predator-prey interactions or the activity of neurons, evolve in time
+predator-prey interactions or the activity of neurons or whole brains,
-is a central application for simulations. Computers becoming available
+evolve in time is a central application for simulations. The advent of
-from the second half of the twentieth century on pushed the exciting
+computers at the second half of the twentieth century pushed the
-field of nonlinear dynamical systems forward. Conceptually, many kinds
+exciting field of nonlinear dynamical systems forward. Conceptually,
-of simulations are very simple and are implemented in a few lines of
+many kinds of simulations are very simple and are implemented in a few
-code.
+lines of code.
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Random numbers}
@@ -83,8 +83,8 @@ given distribution like, for example, the normal distribution. This
 simulates repeated measurements of some quantity (e.g., weight of
 tigers or firing rate of neurons). Doing so we must specify from which
 probability distribution the data should originate from and what are
-the parameters (mean, standard deviation, shape parameters, etc.)
+the parameters of that distribution (mean, standard deviation, shape
-that distribution. How to illuastrate and quantify univariate data, no
+parameters, ...). How to illustrate and quantify univariate data, no
 matter whether they have been actually measured or whether they have
 been simulated as described in the following, is described in
 chapter~\ref{descriptivestatisticschapter}.

statistics/lecture/statistics-chapter.tex

@@ -20,12 +20,21 @@
 
 \section{TODO}
 \begin{itemize}
-\item The content of this lecture easily covers two lectures!
+\item Statistics is to estimate some parameter from data (see below).
-\item 1. mymedian and debugging, rolling a die, normalized histogram
+\item Introduce this concept on a range of examples: mean, other
-\item 2. densities, quantiles, cumulative distribution, kernel histogram
+  parameters of a distribution, parameters of a function.
-\item Adapt the exercises to that!
+\item This is also what the brain needs to do: estimate some
+  information about the environment from noisy/incomplete data!
 \end{itemize}
 
+\section{Exercises}
+The content of this lecture easily covers two lectures!
+\begin{enumerate}
+\item mymedian and debugging, rolling a die, normalized histogram
+\item densities, quantiles, cumulative distribution, kernel histogram
+\end{enumerate}
+ Adapt the exercises to that!
+
 \end{document}