Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

ABSTRACT: Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of

SUBMITTER: Orhan AE 

PROVIDER: S-EPMC5527101 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature