Generating neural circuits that implement probabilistic reasoning.

M J Barber; J W Clark; C H Anderson

doi:10.1103/physreve.68.041912

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Generating neural circuits that implement probabilistic reasoning.

M J Barber·J W Clark·C H Anderson

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Abstract

We extend the hypothesis that neuronal populations represent and process analog variables in terms of probability density functions (PDFs). Aided by an intermediate representation of the probability density based on orthogonal functions spanning an underlying low-dimensional function space, it is shown how neural circuits may be generated from Bayesian belief networks. The ideas and the formalism of this PDF approach are illustrated and tested with several elementary examples, and in particular through a problem in which model-driven top-down information flow influences the processing of bottom-up sensory input.

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Action Potentials Artificial Intelligence Bayes Theorem Brain Cognition Models, Neurological Models, Statistical Nerve Net Neurons Sensation Evaluation Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

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M J Barber, J W Clark, C H Anderson. 2003-10-21. Generating neural circuits that implement probabilistic reasoning.. https://doi.org/10.1103/physreve.68.041912

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