# Validating Theoretical Models with Neurophysiology and Optogenetics

> **NIH NIH U19** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $372,165

## Abstract

ABSTRACT
Theoretical models of neural circuits aim to provide a set of fundamental principles that capture neural dynamics
and explain neural computation. While their value scales with the range of dynamics they can capture and
explain, the models’ validity depends on their ability to make accurate predictions in the face of defined
perturbations. The goal of this specific research project is to employ defined perturbations that definitively test
core predictions of the new models. Unquestionably, the most powerful approach for perturbing neural activity is
optogenetics, yet one photon optogenetics is not up to the task. Therefore, we will use a novel all-optical
multiphoton approach that combines calcium imaging with multiphoton holographic optogenetics. This new
technology allows large ensembles of neurons to be targeted with single cell resolution for photo-activation in
three dimensions. Using this new approach, we will test a concerted set of concrete predictions with extremely
well-defined perturbations, that were never before possible. These optogenetic perturbations thus close the loop
of iterative model refinement and testing to arrive at the most powerful and predictive model yet of any cortical
circuit.

## Key facts

- **NIH application ID:** 10438696
- **Project number:** 5U19NS107613-05
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Hillel Adesnik
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $372,165
- **Award type:** 5
- **Project period:** 2018-09-15 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10438696

## Citation

> US National Institutes of Health, RePORTER application 10438696, Validating Theoretical Models with Neurophysiology and Optogenetics (5U19NS107613-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10438696. Licensed CC0.

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