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Neural circuit models for evidence accumulation through choice-selective sequences
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Neural circuit models for evidence accumulation through choice-selective sequences

Lindsey S. Brown, Jounhong Ryan Cho, Scott S. Bolkan, Edward H. Nieh, Manuel Schottdorf, David W. Tank, Carlos D. Brody, Ilana B. Witten and Mark S. Goldman
bioRxiv
Cold Spring Harbor Laboratory, 1.4
12/27/2023
DOI: 10.1101/2023.09.01.555612
PMCID: PMC10793437
PMID: 38234715
url
https://doi.org/10.1101/2023.09.01.555612View
Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

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Abstract

Decision making is traditionally thought to be mediated by neurons that accumulate evidence through persistent activity. However, recent decision-making experiments in rodents have observed neurons across the brain that fire sequentially, rather than persistently, with the subset of neurons in the sequence depending on the animal’s choice. We developed two new candidate circuit models in which neurons are active sequentially and transfer evidence faithfully to the next active population. One model encodes evidence in the relative firing of two competing chains of neurons, and the other in the network location of a stereotyped pattern (“bump”) of neural activity. Neural recordings from four brain regions during an evidence accumulation task revealed that different regions displayed evidence tuning consistent with different candidate models. This work provides a mechanistic explanation for how graded information may be precisely accumulated within and transferred between neural populations, a set of computations fundamental to many cognitive operations.
 Neuroscience

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