Publication

Bottlenecks, Modularity, and the Neural Control of Behavior

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Last modified
  • 05/20/2025
Type of Material
Authors
    Anjalika Nande, Harvard UniversityVeronika Dubinkina, University of Illinois Urbana-ChampaignRiccardo Ravasio, École Polytechnique Fédérale de LausanneGrace H Zhang, Harvard UniversityGordon Berman, Emory University
Language
  • English
Date
  • 2022-04-06
Publisher
  • Frontiers Media S.A
Publication Version
Copyright Statement
  • © 2022 Nande, Dubinkina, Ravasio, Zhang and Berman.
License
Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 16
Grant/Funding Information
  • GB was supported by the Simons Foundation and a Cottrell Scholar Award, a program of the Research Corporation for Science Advancement (25999). AN was supported by a grant from the US National Institutes of Health (DP5OD019851). GZ acknowledges support from the Paul and Daisy Soros Fellowship and the National Science Foundation Graduate Research Fellowship under Grant No. DGE1745303. RR was supported by the Swiss National Science Foundation under grant No. 200021-165509/1.
Supplemental Material (URL)
Abstract
  • In almost all animals, the transfer of information from the brain to the motor circuitry is facilitated by a relatively small number of neurons, leading to a constraint on the amount of information that can be transmitted. Our knowledge of how animals encode information through this pathway, and the consequences of this encoding, however, is limited. In this study, we use a simple feed-forward neural network to investigate the consequences of having such a bottleneck and identify aspects of the network architecture that enable robust information transfer. We are able to explain some recently observed properties of descending neurons—that they exhibit a modular pattern of connectivity and that their excitation leads to consistent alterations in behavior that are often dependent upon the desired behavioral state of the animal. Our model predicts that in the presence of an information bottleneck, such a modular structure is needed to increase the efficiency of the network and to make it more robust to perturbations. However, it does so at the cost of an increase in state-dependent effects. Despite its simplicity, our model is able to provide intuition for the trade-offs faced by the nervous system in the presence of an information processing constraint and makes predictions for future experiments.
Author Notes
Keywords
Research Categories
  • Biology, General
  • Health Sciences, Medicine and Surgery

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