# CRCNS: Network mechanisms of the learning and encoding of timed motor responses

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2020 · $296,942

## Abstract

The brain is an inherently dynamic system, it evolved under strong selective pressures to allow animals to
interact with the environment in real-time, and predict and prepare for future events. For these reasons,
understanding neural dynamics, and how the brain tells and encodes time is fundamental to understanding
brain function. The importance of neural dynamics and timing to brain function emphasizes the need for
techniques that allow for the collection and analysis of massively parallel single neuron recordings across
multiple structures in behaving animals. This project will combine novel electrophysiological, behavioral,
analytical and computational methods to reverse engineer the neural circuits underlying learning and timing.
The first aim is to combine large-scale neural recordings with computational approaches to determine how
time is represented in the striatum and prefrontal cortex, two interacting brain areas that are closely
implicated in temporal processing. We will specifically examine whether encoding of time relies on absolute,
relative, or stimulus-specific coding mechanisms. Recordings will be carried out in awake, head-fixed mice
trained on a classical trace reward conditioning task in which two cues predict reward with a different delay
period. When animals learn the cue-reward association, they engage in robust anticipatory licking that
precedes the reward presentation; moreover, the timing of this behavior is dependent on the cue-reward
delay time. The second aim is to combine electrophysiology and optogenetics to determine if temporal
coding in the striatum and prefrontal cortex is perturbed by transiently disrupting network activity. The
hypothesis is that if dynamics of the timing circuits are perturbed then the ensuing activity patterns will be
irreversibly altered, thus reducing the accuracy or precision of timed behavioral responses. The third aim is
to develop a novel computational framework based on recurrent neural networks models that can predict
"future" patterns of neural ensemble activity based on "present" patterns. The ultimate goal of this work is to
integrate highly innovative electrophysiological and computational methods for reverse engineering brain
circuit function at the level of networks of hundreds of neurons in the striatum and prefrontal cortex.
RELEVANCE (See instructions):
Learning to produce appropriately timed actions is fundamental to many aspects of behavior, and disruption
of the brain circuits underlying this process is implicated in many neurological and psychiatric disorders. This
project will develop an integrated approach to studying the mechanisms of timed motor behavior by
combining large-scale neural recordings from multiple brain areas and computational modeling of neural
networks.

## Key facts

- **NIH application ID:** 10017326
- **Project number:** 5R01NS100050-05
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** DEAN V BUONOMANO
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $296,942
- **Award type:** 5
- **Project period:** 2016-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10017326

## Citation

> US National Institutes of Health, RePORTER application 10017326, CRCNS: Network mechanisms of the learning and encoding of timed motor responses (5R01NS100050-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10017326. Licensed CC0.

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