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Choice selective inhibition drives stability and competition in decision circuits

Author

Listed:
  • James P. Roach

    (Cold Spring Harbor Laboratory
    University of California Los Angeles)

  • Anne K. Churchland

    (University of California Los Angeles)

  • Tatiana A. Engel

    (Cold Spring Harbor Laboratory)

Abstract

During perceptual decision-making, the firing rates of cortical neurons reflect upcoming choices. Recent work showed that excitatory and inhibitory neurons are equally selective for choice. However, the functional consequences of inhibitory choice selectivity in decision-making circuits are unknown. We developed a circuit model of decision-making which accounts for the specificity of inputs to and outputs from inhibitory neurons. We found that selective inhibition expands the space of circuits supporting decision-making, allowing for weaker or stronger recurrent excitation when connected in a competitive or feedback motif. The specificity of inhibitory outputs sets the trade-off between speed and accuracy of decisions by either stabilizing or destabilizing the saddle-point dynamics underlying decisions in the circuit. Recurrent neural networks trained to make decisions display the same dependence on inhibitory specificity and the strength of recurrent excitation. Our results reveal two concurrent roles for selective inhibition in decision-making circuits: stabilizing strongly connected excitatory populations and maximizing competition between oppositely selective populations.

Suggested Citation

  • James P. Roach & Anne K. Churchland & Tatiana A. Engel, 2023. "Choice selective inhibition drives stability and competition in decision circuits," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-35822-8
    DOI: 10.1038/s41467-023-35822-8
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    References listed on IDEAS

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    1. Nadim A. A. Atiya & Iñaki Rañó & Girijesh Prasad & KongFatt Wong-Lin, 2019. "A neural circuit model of decision uncertainty and change-of-mind," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
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    Cited by:

    1. Yue Liu & Xiao-Jing Wang, 2024. "Flexible gating between subspaces in a neural network model of internally guided task switching," Nature Communications, Nature, vol. 15(1), pages 1-20, December.

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