# Recurrent Circuit Model of Neural Response Dynamics in V1

> **NIH NIH R01** · NEW YORK UNIVERSITY · 2024 · $463,527

## Abstract

Project Summary/Abstract
Primary visual cortex (V1) is one of the most studied areas of the cerebral cortex, but we lack a theoretical
framework for a comprehensive understanding of V1 neurophysiology. Through the proposed research, we aim
to provide one. A class of circuit models, called Oscillatory Recurrent Gated Neural Integrator Circuits
(ORGaNICs), simulates many key neurophysiological phenomena. Our goal is to develop a theory for the full
range of neurophysiological phenomena in V1, i.e., a single predictive model with biophysically-realistic
parameters, and to test that theory with previously published datasets acquired with a wide range of
methodologies.
Preliminary results demonstrate predictions of the theory commensurate with experimental observations of V1
response dynamics (including onset transients and the stimulus-dependence of gamma oscillations), the
dynamics of attentional modulation, experimental evidence for recurrent amplification and inhibitory
stabilization, experimental observations about adaptation including tuning changes and decorrelation, noise
quenching, the dependence of noise correlations on similarity in orientation preference and attention, and
psychophysical contrast discrimination.
Aim 1 key contributions: 1) an analytical theory (i.e., closed-form expressions) that makes experimentally-
testable predictions about a wide range of phenomena related to the dynamics of V1 activity; 2) closed-form
expressions derived from the theory for LFP power spectra; 3) a novel explanation for oscillatory activity in
visual cortex.
Aim 2 key contributions: 1) an analytical theory of adaptation in V1 that makes experimentally-testable
predictions about a wide range of neurophysiological phenomena related to adaptation; 2) the demonstration
that adaptation maintains an efficient neural code, subject to finite resources (overall activity in the circuit),
despite dynamically changing stimulus statistics.
Aim 3 key contributions: 1) an analytical theory that makes experimentally-testable predictions about the
variability and covariability of neural responses; 2) experimentally-testable predictions about psychophysical
discrimination.
The proposed research has the potential to be transformative. We will provide a new set of analytical results
and computational tools for characterizing a broad range of neural circuit models, which will have a significant
impact on the analysis of experimental data and experimental design, and will make new experimentally-
testable predictions for both ORGaNICs and alternative models. We will provide a roadmap for understanding
the underlying circuit mechanisms (the cell types, their interconnections and biophysics), and how manipulating
those mechanisms may change circuit function to correct disorders of visual perception and attention.

## Key facts

- **NIH application ID:** 10915640
- **Project number:** 5R01EY035242-02
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** DAVID J HEEGER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $463,527
- **Award type:** 5
- **Project period:** 2023-09-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10915640

## Citation

> US National Institutes of Health, RePORTER application 10915640, Recurrent Circuit Model of Neural Response Dynamics in V1 (5R01EY035242-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10915640. Licensed CC0.

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