title: Generating neural circuits that implement probabilistic reasoning.
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.
authors.affiliation: Universidade da Madeira, Centro de Ciências Matemáticas, Campus Universitário da Penteada, 9000-390 Funchal, Portugal. mjb@uma.pt
authors.forename: M J
authors.initials: MJ
authors.lastname: Barber
authors.forename: J W
authors.initials: JW
authors.lastname: Clark
authors.forename: C H
authors.initials: CH
authors.lastname: Anderson
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D012677:Sensation / Q000502:physiology*