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

A. Emin Orhan () and Wei Ji Ma ()
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A. Emin Orhan: New York University
Wei Ji Ma: New York University

Nature Communications, 2017, vol. 8, issue 1, 1-14

Abstract: 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 performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.

Date: 2017
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DOI: 10.1038/s41467-017-00181-8

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