한빛사 논문
Hansem Sohn1,4, Devika Narain1,3,4, Nicolas Meirhaeghe2,4, Mehrdad Jazayeri1 5,*
1 Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
2 Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139, USA
3 Erasmus Medical Center, Rotterdam 3015CN, the Netherlands
4These authors contributed equally
5Lead Contact
*Corresponding author : Mehrdad Jazayeri
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.
Keywords : Bayesian integration; Bayesian inference; frontal cortex; neural trajectories; recurrent neural networks; neural manifold
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