# A mechanistic dissection of short and long term spatiotemporal learning in V1

> **NIH NIH R01** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2020 · $412,500

## Abstract

Project Summary
The primary visual cortex (V1) can learn to encode spatiotemporal relationships based on visual
experience and uses the resulting functional memory to actively predict how the visual scene will unfold in
time. This demonstrates expression of a canonical cortical function localized in an experimentally
accessible region. We propose to leverage the tools of modern neuroscience to mechanistically dissect
this ability in the mouse, with the overall aim of developing a description of how the neocortex learns to
represent temporal information. The primary goals of this work are first to understand how similar forms of
visual stimulation drive different forms of short and long-term plasticity that can encode either spatial or
temporal information, and second to identify the distinct mechanisms involved. In addition to their direct
relevance to sensory neurobiology, various psychiatric and neurological disorders, and visual physiology
our experiments will address the wider question of how cortical circuits learn to use temporal relationships
to build predictive models of the world, the answer to which remains as murky as it is critical for our
understanding of the brain.

## Key facts

- **NIH application ID:** 9974010
- **Project number:** 1R01EY030200-01A1
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Jeffrey Peter Gavornik
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $412,500
- **Award type:** 1
- **Project period:** 2020-03-01 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9974010

## Citation

> US National Institutes of Health, RePORTER application 9974010, A mechanistic dissection of short and long term spatiotemporal learning in V1 (1R01EY030200-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9974010. Licensed CC0.

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