1. BRAIN Initiative - Theories, Models and Methods
  2. Neural mechanisms and behavioral consequences of non-Gaussian likelihoods in sensorimotor learning

Neural mechanisms and behavioral consequences of non-Gaussian likelihoods in sensorimotor learning

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PI:  NEMENMAN, ILYA M

Email:  ilya.nemenman@emory.edu

Institution:  EMORY UNIVERSITY

Multiple PI:  SOBER, SAMUEL

Title:  Neural mechanisms and behavioral consequences of non-Gaussian likelihoods in sensorimotor learning

We are interested in understanding complex motor skills acquisition by animals. We proposed and validated the theory that posits that animals control the entire distribution of motor commands they generate, and update it using Bayesian principles. The distribution of motor commands has long, non-Gaussian tails, which explains why many animals do not respond to large sensory errors, but correct small ones. The data analyzed includes pitch sung by birds after a sensorimotor perturbation, and additional data sets are being tried now. Numerical implementation of the model can be found at https://github.com/EmoryUniversityTheoreticalBiophysics/NonGaussianLearning

Grant #:  EB022872-01

Status:  Completed 2019/06/30

Deliverables:

BRAIN Math Project - Nemenman.pptx

Ilya Nemenman at 2019 poster
Ilya Nemenman at 2019 BRAIN PI Meeting

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2021 Brain PI Meeting

Update:

Link to Poster:

Demo:

 

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