Course content / topics
In recent years our experimental methods to record brain activity have been revolutionized. As the complexity of the data acquired by neurophysiologists increases, neural data analysis becomes ever more important: The complex multidimensional signals recorded with multielectrode arrays or two-photon imaging can no longer be interpreted by eye, but mathematical and statistical techniques are needed.
In this practical course we will cover a selection of topics related to the analysis of different kinds of neural data: basic descriptive and inferential statistics, time series analysis, spike triggered average/covariance, spike sorting, dimensionality reduction techniques and information theory. The focus will be on hands-on experience in data analysis.
In this course students will acquire the techniques necessary to analyze multidimensional discrete (spike trains) and continuous (cellular voltage/calcium signals, LFP, EEG, etc.) neural signals. In the computer exercises and the homework assignments, they will acquire hands-on knowledge and learn to deal with the difficulties of applying those techniques to real data.
Programming skills (Matlab/Python)
Basic mathematical skills (vector algebra, probability theory)
Emery N Brown, Robert E Kass, und Partha P Mitra, „Multiple neural spike train data analysis: state-of-the-art and future challenges“, Nat Neurosci 7, Nr. 5 (Mai 2004): 456-461.
Robert E. Kass, Valérie Ventura, und Emery N. Brown, „Statistical Issues in the Analysis of Neuronal Data“, Journal of Neurophysiology 94, Nr. 1 (Juli 1, 2005): 8 -25.
Liam Paninski’s course ‘Statistical analysis of neural data’ www.stat.columbia.edu/~liam/teaching/neurostat-spr11/ (we will cover only a few of these topics)
Dayan and Abbott: Theoretical Neuroscience. MIT Press.
Rieke, Warland, Ruyter van Stevenik and Bialek: Spikes – Exploring the neural code. MIT Press.