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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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Published on: June 24, 2015

Learning priors for Bayesian computations in the nervous system.

Max Berniker1, Martin Voss, Konrad Kording

  • 1Department of Physical Medicine and Rehabilitation, Northwestern University and Rehabilitation Institute of Chicago, Chicago, Illinois, United States of America. mbernike@northwestern.edu

Plos One
|September 17, 2010
PubMed
Summary
This summary is machine-generated.

The brain learns new information by updating its prior knowledge, which is essential for efficient behavior. This study shows how the nervous system rapidly learns the mean of new priors but learns variance more slowly.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • The human nervous system integrates sensory input with lifelong acquired information (priors).
  • Optimal integration of sensory data with priors is known, but the learning mechanisms for priors are not well understood.

Purpose of the Study:

  • To investigate how the nervous system learns and updates priors.
  • To characterize the time course and mechanisms of prior learning.

Main Methods:

  • Experimental manipulation of prior distributions in an estimation task.
  • Measurement of subjects' evolving priors during learning.
  • Application of a Bayesian inference model to predict learning dynamics.

Main Results:

  • Subjects successfully learned the correct prior distribution with extensive practice.
  • Rapid learning of the prior's mean was observed, while variance learning was slower and variable.
  • A Bayesian model accurately predicted the observed learning time course.

Conclusions:

  • The nervous system continuously updates its priors to optimize behavior.
  • Learning of prior mean and variance occurs at different rates, suggesting distinct neural mechanisms.
  • Bayesian inference provides a valuable framework for understanding neural prior learning.