Written report and cumulative oral exam

In recent years experimental methods to record brain activity have been revolutionized.
As the complexity of the data acquired in neuroscience increases,
neural data analysis becomes ever more important: The complex multidimensional
signals recorded with e.g. multielectrode arrays or two-photon imaging
can no longer be interpreted by eye, but rigorous data analytic techniques are
needed.
In this course we will cover a selection of topics related to the analysis of
different kinds of neural data based on concepts of machine learning: time
series analysis, spike sorting, spike triggered average/covariance, dimensionality
reduction techniques and information theory. The focus will be on applying
state-of-the-art concepts in hands-on data analysis of real data sets.

(1) In this course students will acquire knowledge of basic and advanced techniques
necessary to analyze discrete (spike trains) and continuous (cellular voltage/
calcium signals, LFP, EEG) neural signals. (2) Students will implement
important techniques (Filtering, MoG, STA, etc) and evaluate them on artificial
and real data. (3) Students will learn how to work with real neural data
and cope with the challenges this brings about.

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.
Dayan and Abbott: Theoretical Neuroscience. MIT Press.
Rieke, Warland, Ruyter van Stevenik and Bialek: Spikes – Exploring the neural
code. MIT Press.