# Disparate nonlinear neural dynamics measured with different techniques in macaque and human V1

## TL;DR

The study compares how neural activity in macaque and human visual cortex responds to visual stimuli using different imaging techniques.

## Contribution

The study reveals that nonlinear neural dynamics differ across imaging techniques and species, and introduces a model to explain these differences.

## Key findings

- VSDI responses in macaque V1 are near-additive, unlike sub-additive dynamics seen in human fMRI/ECoG.
- GCaMP6f measurements in macaque V1 also show near-additive dynamics, ruling out subthreshold vs spiking activity as the cause.
- A delayed normalization model captures the dynamics across measurements and suggests dynamic gain-control as a canonical computation.

## Abstract

Diverse neuro-imaging techniques measure different aspects of neural responses with distinct spatial and temporal resolutions. Relating measured neural responses across different methods has been challenging. Here, we take a step towards overcoming this challenge, by comparing the nonlinearity of neural dynamics measured across methods. We used widefield voltage-sensitive dye imaging (VSDI) to measure neural population responses in macaque V1 to visual stimuli with a wide range of temporal waveforms. We found that stimulus-evoked VSDI responses are surprisingly near-additive in time. These results are qualitatively different from the strong sub-additive dynamics previously measured using fMRI and electrocorticography (ECoG) in human visual cortex with a similar set of stimuli. To test whether this discrepancy is specific to VSDI—a signal dominated by subthreshold neural activity, we repeated our measurements using widefield imaging of a genetically encoded calcium indicator (GcaMP6f)—a signal dominated by spiking activity, and found that GCaMP signals in macaque V1 are also near-additive. Therefore, the discrepancies in the extent of sub-additivity between the macaque and the human measurements are unlikely due to differences between sub- and supra-threshold neural responses. Finally, we use a simple yet flexible delayed normalization model to capture these different dynamics across measurements (with different model parameters). The model can potentially generalize to a broader set of stimuli, which aligns with previous suggestion that dynamic gain-control is a canonical computation contributing to neural processing in the brain.