Neural circuit models for evidence accumulation through choice-selective sequences

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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, and suggests that different brain regions may accumulate evidence through different circuit mechanisms.
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Funding Information
  • NIH (T32MH065214)
  • NIH (F32MH132179)
  • NIH (U19NS104648)
  • NIH (U19NS132720)
  • NIH (K99DA053388)
  • C. V. Starr Fellowship

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