# Behavioral consequences and cellular substrates of plasticity in visual cortex

> **NIH NIH R01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2020 · $486,777

## Abstract

Detection of novel stimuli that may predict reward or punishment requires long-term
memory for, and recognition of, stimuli that are familiar. Novelty detection and familiarity
recognition are often impaired in neuropsychiatric disease, so understanding the
neurobiological underpinnings is an important goal. We recently discovered that memory
of visual stimulus familiarity is stored via synaptic modifications in primary visual cortex
of mice. The primary aims of our research are now to (a) identify how information is
stored by the collective activity of neurons in primary visual cortex, (b) pinpoint the key
sites in the cortical microcircuit where the essential synaptic modifications occur, and (c)
examine a specific hypothesis that memory is expressed by switching the state of
activity in the reciprocal connections between visual cortex and thalamus. Beyond the
relevance of our proposed research to identifying the mechanisms underlying visual
recognition memory, they will broaden our understanding of how primary sensory areas
are modified by sensory experience in order to modify behavior, which remains one of
the great challenges in basic neuroscience.

## Key facts

- **NIH application ID:** 9999570
- **Project number:** 5R01EY023037-08
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Mark F Bear
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $486,777
- **Award type:** 5
- **Project period:** 2013-08-01 → 2021-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9999570

## Citation

> US National Institutes of Health, RePORTER application 9999570, Behavioral consequences and cellular substrates of plasticity in visual cortex (5R01EY023037-08). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9999570. Licensed CC0.

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