# Neural population dynamics in premotor cortex during decision making

> **NIH NIH K99** · STANFORD UNIVERSITY · 2020 · $118,314

## Abstract

Project Summary / Abstract
Decision-making requires populations of neurons in the brain to collectively process sensory evidence and select
appropriate behavioral responses. Neural population dynamics (NPDs), which describe how the responses of a
population of individual neurons unfold over time, can provide an insightful view into these decision processes.
Much is known about how individual neurons respond in select decision making tasks. However, little is known
about how populations of neurons dynamically perform decision computations, and how resulting NPDs within a
brain area are structured across many decision-making tasks. Shared features in NPDs across many tasks could
indicate unifying neural mechanisms of computation that underlie the multi-functionality of a given neural circuit.
This proposal aims to uncover the details and structure of these decision-related NPDs in human and nonhuman
primate dorsal premotor cortex, an area tightly linked to both the function and dysfunction of decision making.
Particular attention will be devoted to examining how NPDs are organized across multiple decision-making tasks
and how those NPDs emerge during learning of new tasks. During the K99 phase, novel analytical tools will be
developed for extracting NPDs from simultaneously recorded neural population activity and, importantly, for
providing interpretable links between NPDs and their role in decision-related neural computation. With the goal
of identifying unifying principles of decision-related computation, large-scale analyses will then integrate existing
and newly collected neurophysiological datasets involving multiple decision-making tasks performed by humans
and nonhuman primates. During the R00 phase, the proposed research will pivot toward understanding how
NPDs emerge during learning to make new types of decisions. The proposal postulates that the ease, speed,
and efficacy of learning all hinge on the extent by which critical neural circuits can leverage pre-existing neural
mechanisms of computation. These concepts will be tested in collaborative human and nonhuman primate neu-
rophysiological experiments, with guidance provided by interrogations of artificial neural networks posed with
similar decision-related learning tasks. Upon completion, the proposed research will provide new fundamental
knowledge concerning i) the multi-functional and adaptive role of premotor cortex across many decision-making
tasks and ii) unifying principles of neural computation that support this flexibility. Better understanding decision-
related circuits and their neural mechanisms could ultimately elucidate the basis for the numerous psychiatric
disorders that impair decision making, which could eventually lead to improved diagnosis and treatment of these
debilitating conditions.

## Key facts

- **NIH application ID:** 10018951
- **Project number:** 5K99MH121533-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Matthew D. Golub
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $118,314
- **Award type:** 5
- **Project period:** 2019-09-16 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10018951

## Citation

> US National Institutes of Health, RePORTER application 10018951, Neural population dynamics in premotor cortex during decision making (5K99MH121533-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10018951. Licensed CC0.

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