From 5e9ad55d1cea5848b36c1480a30048a259f6da06 Mon Sep 17 00:00:00 2001 From: Jan Benda Date: Sun, 27 Dec 2020 21:39:20 +0100 Subject: [PATCH] ideas for more chapters --- header.tex | 2 + pointprocesses/code/isiserialcorr.m | 1 - pointprocesses/lecture/pointprocesses.tex | 2 +- scientificcomputing-script.tex | 61 ++++++++++++++++++----- simulations/lecture/simulations.tex | 18 +++---- statistics/lecture/statistics-chapter.tex | 17 +++++-- 6 files changed, 73 insertions(+), 28 deletions(-) diff --git a/header.tex b/header.tex index a1b8961..464bb56 100644 --- a/header.tex +++ b/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}} diff --git a/pointprocesses/code/isiserialcorr.m b/pointprocesses/code/isiserialcorr.m index e5b07fb..21bb474 100644 --- a/pointprocesses/code/isiserialcorr.m +++ b/pointprocesses/code/isiserialcorr.m @@ -32,4 +32,3 @@ function isicorr = isiserialcorr(isivec, maxlag) ylabel('\rho_k') end end - diff --git a/pointprocesses/lecture/pointprocesses.tex b/pointprocesses/lecture/pointprocesses.tex index 1cf796e..d25cf08 100644 --- a/pointprocesses/lecture/pointprocesses.tex +++ b/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 diff --git a/scientificcomputing-script.tex b/scientificcomputing-script.tex index 5bacd1b..64db445 100644 --- a/scientificcomputing-script.tex +++ b/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) -% !!! this would be a nice and simple starter !!! -% introduces derivatives which are also needed for fitting +% add chapter on stochastic simulations (draw random numbers, draw random functions) %\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) % diff --git a/simulations/lecture/simulations.tex b/simulations/lecture/simulations.tex index 6d69396..0c1d3d1 100644 --- a/simulations/lecture/simulations.tex +++ b/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 -is a central application for simulations. Computers becoming available -from the second half of the twentieth century on pushed the exciting -field of nonlinear dynamical systems forward. Conceptually, many kinds -of simulations are very simple and are implemented in a few lines of -code. +predator-prey interactions or the activity of neurons or whole brains, +evolve in time is a central application for simulations. The advent of +computers at the second half of the twentieth century pushed the +exciting field of nonlinear dynamical systems forward. Conceptually, +many kinds of simulations are very simple and are implemented in a few +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.) -that distribution. How to illuastrate and quantify univariate data, no +the parameters of that distribution (mean, standard deviation, shape +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}. diff --git a/statistics/lecture/statistics-chapter.tex b/statistics/lecture/statistics-chapter.tex index 84833c0..c8edb4b 100644 --- a/statistics/lecture/statistics-chapter.tex +++ b/statistics/lecture/statistics-chapter.tex @@ -20,12 +20,21 @@ \section{TODO} \begin{itemize} -\item The content of this lecture easily covers two lectures! -\item 1. mymedian and debugging, rolling a die, normalized histogram -\item 2. densities, quantiles, cumulative distribution, kernel histogram -\item Adapt the exercises to that! +\item Statistics is to estimate some parameter from data (see below). +\item Introduce this concept on a range of examples: mean, other + parameters of a distribution, parameters of a function. +\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}