Flexible control of representational dynamics in a disinhibition-based model of decision making

Abstract
Data availability
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Abstract

Inhibition is crucial for brain function, regulating network activity by balancing excitation and implementing gain control. Recent evidence suggests that beyond simply inhibiting excitatory activity, inhibitory neurons can also shape circuit function through disinhibition. While disinhibitory circuit motifs have been implicated in cognitive processes including learning, attentional selection, and input gating, the role of disinhibition is largely unexplored in the study of decision-making. Here, we show that disinhibition provides a simple circuit motif for fast, dynamic control of network state and function. This dynamic control allows a disinhibition-based decision model to reproduce both value normalization and winner-take-all dynamics, the two central features of neurobiological decision-making captured in separate existing models with distinct circuit motifs. In addition, the disinhibition model exhibits flexible attractor dynamics consistent with different forms of persistent activity seen in working memory. Fitting the model to empirical data shows it captures well both the neurophysiological dynamics of value coding and psychometric choice behavior. Furthermore, the biological basis of disinhibition provides a simple mechanism for flexible top-down control of the network states, enabling the circuit to capture diverse task-dependent neural dynamics. These results suggest a biologically plausible unifying mechanism for decision-making and emphasize the importance of local disinhibition in neural processing.

Data availability

The empirical data presented in this paper and MATLAB code used for simulations and fitting the empirical data is available at DOI 10.17605/OSF.IO/YGR57.

The following data sets were generated

(2023) Flexible control of representational dynamics in a disinhibition-based model of decision making
DOI 10.17605/OSF.IO/YGR57.

https://cdn.elifesciences.org/articles/82426/Flexible control of representational dynamics in a disinhibition-based model of decision making

The following previously published data sets were used

1. Roitman JD
2. Shadlen MN
(2002) Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task
Matlab m files.

https://shadlenlab.columbia.edu/resources/RoitmanDataCode.html

Article and author information

Author details

Bo Shen

Neuroscience Institute, New York University, New York, United States

For correspondence
bs3667@nyu.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-5796-0844
Kenway Louie

Center for Neural Science, New York University, New York, United States

Competing interests
The authors declare that no competing interests exist.
Paul W Glimcher

Center for Neural Science, New York University, New York, United States

Competing interests
The authors declare that no competing interests exist.

Funding

National Institutes of Health (R01DA038063)

Paul W Glimcher

National Institutes of Health (R01DA043676)

Paul W Glimcher

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.