Difference between revisions of "Decoding Spike Trains"

From Ilya Nemenman: Theoretical Biophysics @ Emory
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(Created page with "{{Locomotion2020}} by '''Jesse Lauer''' ;Abstract :DeepLabCut is an open-source toolbox for markerless animal/human pose estimation achieving human accuracy with little train...")
 
 
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{{Locomotion2020}}
 
{{Locomotion2020}}
  
by '''Jesse Lauer'''
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by '''Ilya Nemenman''' and '''Sam Sober''', Emory University
 
;Abstract
 
;Abstract
:DeepLabCut is an open-source toolbox for markerless animal/human pose estimation achieving human accuracy with little training data. Participants will be walked through project creation, data curation, model training, and analysis. Special emphasis will be placed on current applications, best practices and common pitfalls, as well as what to do next with the data.
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:This tutorial will provide an introduction for using information theoretic and statistical physics-based methods for analysis of spike trains, such as detecting the scale of temporal features involved in the neural control, detection of multi-neuron synergies, and identification of multi spike patterns that are correlated with (and possibly control) behavior.

Latest revision as of 15:54, 2 March 2020

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by Ilya Nemenman and Sam Sober, Emory University

Abstract
This tutorial will provide an introduction for using information theoretic and statistical physics-based methods for analysis of spike trains, such as detecting the scale of temporal features involved in the neural control, detection of multi-neuron synergies, and identification of multi spike patterns that are correlated with (and possibly control) behavior.