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  Practical on Machine Learning for Neuroscience

Besserve, M., & Safavi, S. (2016). Practical on Machine Learning for Neuroscience. Talk presented at Machine Learning Summer School (MLSS 2016). Cádiz, Spain. 2016-05-11 - 2016-05-21.

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 Creators:
Besserve, M1, 2, Author              
Safavi, S1, 2, Author              
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: The study of brain function requires collecting and analyzing highly complex and multivariate datasets. Modern machine learning techniques are useful at several stages of the analysis. First unsupervised learning techniques based on tools such as non-negative matrix factorization helps identify the relevant features and underlying structure of the data. Second, statistical analysis based on kernel embedding of distributions help identify complex interactions between different aspects of neural activity. Finally, causal inference allows estimating the directionality of information transfer across brain networks. During this tutorial, we will implement and use some of these tools to analyze intercortical recordings and explain how they help neuroscientists understand brain function.

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 Dates: 2016-05
 Publication Status: Published online
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Title: Machine Learning Summer School (MLSS 2016)
Place of Event: Cádiz, Spain
Start-/End Date: 2016-05-11 - 2016-05-21
Invited: Yes

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