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Nat Commun. 2017 Jul 26;8(1):138. doi: 10.1038/s41467-017-00181-8.

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

Author information

1
Center for Neural Science, New York University, New York, NY, 10003, USA. aeminorhan@gmail.com.
2
Center for Neural Science, New York University, New York, NY, 10003, USA. weijima@nyu.edu.
3
Department of Psychology, New York University, New York, NY, 10003, USA. weijima@nyu.edu.

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.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

PMID:
28743932
PMCID:
PMC5527101
DOI:
10.1038/s41467-017-00181-8
[Indexed for MEDLINE]
Free PMC Article

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