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.

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.

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