# CRCNS: Multiscale dynamics of cortical circuits for visual recognition & memory

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2021 · $381,972

## Abstract

This proposal aims to integrate two streams of research on learning and memory in an attempt to strengthen
the links between theory and experiment, build models that explain experimental observations and use
model predictions to guide new experiments. The experimental stream will record neuronal population
activity in inferior temporal, perirhinal and prefrontal cortices during performance of delayed matching tasks
which require maintenance of visual information in short term memory, using visual stimuli with various
degrees of familiarity (from entirely novel to highly familiar). The modeling stream will investigate learning
and memory in network models that include learning rules inferred from data, using a combination of mean
field analysis and simulation. Models will generate predictions on patterns of delay period activity that will be
tested using experimental data. The goals of this combined experimental and theoretical project will be to
answer the following questions:
· How do changes in synaptic connectivity induced by learning due to repeated presentation of a particular
stimulus affect the distributions of visual responses of neurons? In other words, how do neuronal
representations change in cortex as a novel stimulus becomes familiar? Can we infer the learning rule in
cortical circuits from experimentally observed changes in distributions of neuronal responses as the stimuli
become familiar?
· Do changes in synaptic connectivity induced by learning rules that are consistent with the statistics of
visual responses lead to delay period activity in a task such as the OMS task? Is delay period activity
already present upon the first presentation of a stimulus, or does it develop over time? If it is not present
during the initial presentations, how is sample information maintained in memory during the delayed match
to sample task?
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RELEVANCE (See instructions):
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## Key facts

- **NIH application ID:** 10147161
- **Project number:** 5R01MH115555-05
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Yali Amit
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $381,972
- **Award type:** 5
- **Project period:** 2017-07-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10147161

## Citation

> US National Institutes of Health, RePORTER application 10147161, CRCNS: Multiscale dynamics of cortical circuits for visual recognition & memory (5R01MH115555-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10147161. Licensed CC0.

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