# Fast neural population dynamics in primate V1 captured by a genetically-encoded voltage indicator

**Authors:** Jingyang Zhou, Yuzhi Chen, Matt Whitmire, Pin Kwang Tan, Jimin Wu, Ashok Veeraraghavan, Jacob T. Robinson, Wilson Geisler, Vincent A. Pieribone, Eyal Seidemann

PMC · DOI: 10.21203/rs.3.rs-5851261/v1 · 2025-01-20

## TL;DR

This study demonstrates the first use of a genetically encoded voltage indicator in macaque V1 neurons to capture fast neural dynamics in response to visual stimuli.

## Contribution

The first successful expression of a GEVI in primate V1 neurons and a new model to predict response dynamics to arbitrary stimuli.

## Key findings

- GEVI captures faster response dynamics and tracks higher temporal frequencies than GECI.
- GEVI responds to lower contrast stimuli compared to GECI and VSD.
- A nonlinear model was developed to predict response dynamics to arbitrary temporal waveforms and contrasts.

## Abstract

Genetically encoded voltage indicators (GEVIs) can measure millisecond-scale subthreshold neural responses with cell type specificity. Here, we successfully expressed, for the first time, a GEVI in excitatory V1 neurons in macaque monkeys. We then used widefield fluorescent imaging to measure V1 dynamics in response to visual stimuli with diverse temporal waveforms and contrasts, and compared these responses to signals measured using a genetically encoded calcium indicator (GECI) and a synthetic voltage-sensitive dye (VSD). When compared to GECI, GEVI captures faster response dynamics, tracks higher temporal frequencies, and responds to lower contrasts. To quantitatively characterize these three signals, we developed a simple nonlinear model that predicts the response dynamics to stimuli with arbitrary temporal waveforms and contrasts. Our results are consistent with the hypothesis that GEVI signals reflect the dynamics of locally summed membrane potentials, thus opening the door for a new class of experiments in behaving primates.

## Figures

7 figures with captions in the complete paper: https://tomesphere.com/paper/PMC11838743/full.md

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Source: https://tomesphere.com/paper/PMC11838743