# Cortical plasticity during reinforcement learning

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2023 · $421,546

## Abstract

Reinforcement learning (RL) is a fundamental learning process that is common across species and essential
for cognitive flexibility and survival. In addition to neuroscience and psychology, RL also has proved valuable in
the field of artificial intelligence (AI), achieving super-human performance in complex tasks. Understanding of
the neural mechanism of RL would facilitate not only the development of diagnosis and treatment for learning
and cognitive disorders but also development of novel deep RL architectures in AI. In this proposal, we aim to
gain insights into how different brain areas work together to support RL.
To tackle this problem, we use a behavioral task that entails two layers of RL at different timescales: fast RL to
solve the task within a session and slow RL to learn the fast RL strategy over weeks/months of training. By
leveraging mouse genetics and cutting-edge technology such as longitudinal two-photon calcium imaging of
neural population activity and dopamine signaling, optogenetics, plasticity perturbation, and modeling with
artificial deep RL networks, we will investigate the neural mechanisms of the fast and slow. In particular, we
hypothesize that synaptic plasticity in the orbitofrontal cortex (OFC) plays a central role in slow RL. We will
investigate the functions of OFC and its dopamine signaling in RL in Aims 1 and 2. In Aim 3, we will examine
how OFC interacts with other cortical areas in RL. These aims will uncover the large-scale circuit basis of
different aspects of RL and offer a novel conceptual framework to understand how RL is implemented in the
brain.

## Key facts

- **NIH application ID:** 10694996
- **Project number:** 5R01MH128746-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Takaki Komiyama
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $421,546
- **Award type:** 5
- **Project period:** 2022-09-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10694996

## Citation

> US National Institutes of Health, RePORTER application 10694996, Cortical plasticity during reinforcement learning (5R01MH128746-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10694996. Licensed CC0.

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