English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Talk

Practical on Machine Learning for Neuroscience

MPS-Authors
/persons/resource/persons75278

Besserve,  M
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons192723

Safavi,  S
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Ressource
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: http://hdl.handle.net/21.11116/0000-0007-8E78-3
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.