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Bayesian computation in recurrent neural circuits.

Rajesh P N Rao1

  • 1Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA. rao@cs.washington.edu

Neural Computation
|March 10, 2004
PubMed
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This study demonstrates how a common neural network model of the cerebral cortex can perform Bayesian inference. This framework offers a new interpretation of cortical activity as log posterior probabilities of stimuli.

Area of Science:

  • Computational neuroscience
  • Cognitive neuroscience
  • Machine learning

Background:

  • Bayesian models successfully explain human psychophysical data.
  • The neural basis for Bayesian inference in the brain remains largely unknown.

Purpose of the Study:

  • To demonstrate that a common cortical network architecture can implement Bayesian inference.
  • To provide a novel interpretation of neural activity in terms of log posterior probabilities.

Main Methods:

  • Utilized a network architecture modeling the cerebral cortex.
  • Applied Bayesian inference to arbitrary hidden Markov models.
  • Illustrated the approach with orientation discrimination and visual motion detection tasks.

Main Results:

Related Experiment Videos

  • The model network infers posterior distributions and estimates orientation under noise.
  • The network exhibits direction selectivity and computes posterior probabilities for motion.
  • Model responses mimic evidence-accumulating neurons in LIP and FEF during motion discrimination.

Conclusions:

  • A cortical network architecture can implement Bayesian inference for hidden Markov models.
  • This framework offers a new interpretation of neural activity related to stimulus probabilities.
  • The model provides a biologically plausible mechanism for complex perceptual tasks.