# Understanding V1 circuit dynamics and computations

> **NIH NIH U19** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $3,307,280

## Abstract

Understanding the cerebral cortex requires data-based theoretical models that can yield in-
sight into the circuit mechanisms of cortical computation, and reproduce detailed cortical dynamics across stimuli
and brain states. The primary visual cortex (V1) is the best-studied cortical area by both theorists and experimen-
talists, yet current models - whether statistical or circuit based – only poorly capture how V1 neurons respond
to complex stimuli, such as natural scenes. The ultimate goal of this team project is to obtain the necessary
experimental data and build the detailed circuit-based models that explain how V1 circuits encode natural visual
stimuli. In so doing, we aim not only to provide a mechanistic understanding for how V1 dynamics forms the
basis of vision, but also to establish a more generalizable paradigm for understanding any cortical area. Our
assumption is that current models fall short for two reasons: on the experimental side, we are still missing most
of the fundamental details about the synaptic connectivity and physiological responses of V1 cell types; while
on the theory side, prevailing circuit-based models reduce V1 to just a few cell types, and either capture the
static responses of V1 neurons to simple stimuli but not their trial to trial fluctuations, or capture fluctuations, but
not their rich array of non-linear responses properties that are central to visual computation. Our hypothesis is
that we can achieve a circuit-based model that explains cortical responses and dynamics to natural stimuli by
implementing the following three steps: 1) identify and incorporate all the differentiable V1 neuronal cell types
into our model; 2) measure and incorporate the synaptic connectivity and intrinsic properties of these cell types;
3) measure and accurately predict the visual responses of each of these cell types to diverse visual stimuli and
in multiple brain states. We focus on circuit-based rather than statistical models of V1 for two reasons: they can
provide insight into neural mechanisms of visual computation and the regimes of cortical operation, and because
they will permit us to test their accuracy by validating their predictions for how V1 responds to defined experimen-
tal perturbations. To implement these perturbations, we will employ multiphoton holographic optogenetics, which
allows us to manipulate V1 circuits with the level of precision formerly only possible in the realm of theory. Here
we bring together an outstanding team of theorists, experimentalists, and data scientists to leverage cutting edge
new brain mapping technologies that we will use to build and validate dramatically improved models of visual
cortical function and dynamics.

## Key facts

- **NIH application ID:** 9967166
- **Project number:** 5U19NS107613-03
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** KENNETH D MILLER
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $3,307,280
- **Award type:** 5
- **Project period:** 2018-09-15 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9967166

## Citation

> US National Institutes of Health, RePORTER application 9967166, Understanding V1 circuit dynamics and computations (5U19NS107613-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9967166. Licensed CC0.

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