# Multi-regional neural circuit dynamics underlying short-term memory

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2021 · $616,892

## Abstract

Abstract
The focus of this BRAIN Initiative funding opportunity is to use “innovative approaches to understand how
circuit activity gives rise to a specific behavior”. Cognitive behaviors arise from collective interactions of
multiple brain systems. Yet, for most cognitive processes, we do not yet know which brain areas are involved
and how multi-regional interactions mediate specific cognitive processes. This gap in knowledge arises
because separate parts of the brain are studied individually, yet, the brain circuits driving behavior vary from
one behavior to another. The goal of this proposal is to establish a working example of how brain-wide activity
dynamics collectively generate one cognitive behavior. We address this question by studying how a mouse
flexibly generate a volitional movement based on short-term memory. Neurons in multiple parts of the brain,
including the frontal cortex, thalamus, midbrain, and cerebellum respond robustly during this short-term
memory and causally contribute to the behavior. Taking advantage of this opportunity to establish how activity
distributed across multiple brain systems orchestrates one coherent behavior, in this proposal, we will use
newly developed experimental frameworks to analyze the underlying neural circuitry at brain-wide scale and
establish causal relationships between specific activity patterns and behavior. First, we will use brain-wide
loss-of-function screen, high-density silicon probe recording, and anatomical techniques to produce multi-
modal maps of core neural substrates of the short-term memory. The outcome datasets will be put into
standardized brain coordinates, making it possible to link the functional data to existing connectional and gene
expression atlases. Next, we will use simultaneous recordings and spatiotemporally-precise perturbations to
probe multi-regional interactions underlying the observed activity patterns and relationships to behavior.
Finally, we will build multi-regional models that offer interpretable description of the behaviorally-relevant
dynamics and relate them to underlying circuit connectivity. The outcome will disambiguate competing models
of how information distributed over multiple brain regions is coordinated during cognitive processes, how
information is dynamically routed and gated. The experimental, analysis, and modeling approaches will be
broadly useful for analyzing distributed circuits driving behavior, as is the focus of multiple collaborative U19
grants. All the data and code will be published in the well document Neurodata Without Border (NWB) format.

## Key facts

- **NIH application ID:** 10241924
- **Project number:** 5R01NS113110-03
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Shaul Druckmann
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $616,892
- **Award type:** 5
- **Project period:** 2019-09-15 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10241924

## Citation

> US National Institutes of Health, RePORTER application 10241924, Multi-regional neural circuit dynamics underlying short-term memory (5R01NS113110-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10241924. Licensed CC0.

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