title: Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference.
abstract: One of the brain's most basic functions is integrating sensory data from diverse sources. This ability causes us to question whether the neural system is computationally capable of intelligently integrating data, not only when sources have known, fixed relative dependencies but also when it must determine such relative weightings based on dynamic conditions, and then use these learned weightings to accurately infer information about the world. We suggest that the brain is, in fact, fully capable of computing this parallel task in a single network and describe a neural inspired circuit with this property. Our implementation suggests the possibility that evidence learning requires a more complex organization of the network than was previously assumed, where neurons have different specialties, whose emergence brings the desired adaptivity seen in human online inference.
authors.affiliation: Department of Computer Science, University of Massachusetts at Amherst, Amherst, Massachusetts 01003, USA.
authors.forename: Hava T
authors.initials: HT
authors.lastname: Siegelmann
authors.forename: Lars E
authors.initials: LE
authors.lastname: Holzman
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D007858:Learning; D008954:Models, Biological; D009415:Nerve Net / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology