Bayesian Computation through Cortical Latent Dynamics
Name
nihms-1534474.pdf
Description
Accepted version
Size
2.06 MB
Format
Adobe PDF
Checksum (MD5)
99e4585411436550359d895d5ea0aee3
Author(s)
Sohn, Hansem
Narain, Devika
Jazayeri, Mehrdad
Date Issued
September 2019
Journal
Neuron
Publisher
Elsevier BV
Citation
Sohn, Hansem et al. “Bayesian Computation through Cortical Latent Dynamics.” Neuron, 103, 5 (September 2019): 934–947.e5 © 2019 The Author(s)
Version
Author's final manuscript
Abstract
Statistical regularities in the environment create prior beliefs that we rely on to optimize our behavior when sensory information is uncertain. Bayesian theory formalizes how prior beliefs can be leveraged and has had a major impact on models of perception, sensorimotor function, and cognition. However, it is not known how recurrent interactions among neurons mediate Bayesian integration. By using a time-interval reproduction task in monkeys, we found that prior statistics warp neural representations in the frontal cortex, allowing the mapping of sensory inputs to motor outputs to incorporate prior statistics in accordance with Bayesian inference. Analysis of recurrent neural network models performing the task revealed that this warping was enabled by a low-dimensional curved manifold and allowed us to further probe the potential causal underpinnings of this computational strategy. These results uncover a simple and general principle whereby prior beliefs exert their influence on behavior by sculpting cortical latent dynamics. Sohn et al. found that prior beliefs warp neural representations in the frontal cortex. This warping provides a substrate for the optimal integration of prior beliefs with sensory evidence during sensorimotor behavior.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.NEURON.2019.06.012
