Remapping in a recurrent neural network model of navigation and context inference

Author:

Low Isabel IC1ORCID,Giocomo Lisa M2ORCID,Williams Alex H34ORCID

Affiliation:

1. Zuckerman Mind Brain Behavior Institute, Columbia University

2. Department of Neurobiology, Stanford University

3. Center for Computational Neuroscience, Flatiron Institute

4. Center for Neural Science, New York University

Abstract

Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns (‘remap’) in response to changing contextual factors such as environmental cues, task conditions, and behavioral states, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference.

Funder

Wu Tsai Neurosciences Institute, Stanford University

Office of Naval Research

Simons Foundation

National Institute of Mental Health

Vallee Foundation

James S. McDonnell Foundation

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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