# Using perceptual decision-making to understand the role of selective inhibitory activity in cortical computation

> **NIH NIH F32** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $71,390

## Abstract

Cortical circuits perform computations to generate appropriate behaviors based upon diverse
sensory inputs. These computations are central to an animal maintaining its health and long-
term survival. An example of this type of computation are perceptual decision-making tasks
where an animal must weigh sensory evidence to choose a behavior which will elicit a reward.
The classical circuit models of decision-making focus solely on the effects of recurrent
excitation, treating inhibitory neurons as agnostic facilitators of competition between excitatory
subpopulations. However, this view of inhibitory neurons is at odds with experiment results
which show a diversity of interneuron tuning and connectivity across the cortex, recently in the
decision-making context. I propose to develop new models of cortical decision-making circuits
which parameterizes selectivity of connections between subpopulations within the excitatory
and inhibitory populations to understand how selectivity shapes the attractor dynamics
underlying decision-making and how these dynamics represent animal choice. Based on the
analysis of these models, I will establish an updated theoretical framework for the neural circuit
mechanisms of decision-making behaviors which more fully account the intricacies of cortical
circuit structure and more fully represent the diversity of neuronal cell-types. The role of
inhibitory selectivity in facilitating task learning will be investigated using artificial neural
networks as a proxy. Finally, single cell resolution calcium activity will be measured from a
labeled inhibitory cell-type. This work will address how circuit structure and cell-type shape
population dynamics underlying decision-making and how local cortical processes generate
meaningful behaviors.

## Key facts

- **NIH application ID:** 10164614
- **Project number:** 5F32MH123011-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** James Patrick Roach
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $71,390
- **Award type:** 5
- **Project period:** 2020-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10164614

## Citation

> US National Institutes of Health, RePORTER application 10164614, Using perceptual decision-making to understand the role of selective inhibitory activity in cortical computation (5F32MH123011-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10164614. Licensed CC0.

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