Delayed normalization model captures disparate nonlinear neural dynamics measured with different techniques in macaque and human V1

Abstract

Neuronal representations throughout the primate visual system display a wide range of nonlinear dynamics in response to static visual stimuli. To understand the neural basis of visual perception, it is important to quantify these nonlinearities and to develop general models that allow one to predict response dynamics to visual stimuli with arbitrary temporal waveform. Voltage-sensitive dye imaging (VSDI) is a powerful method for measuring neural population responses from the cortex of awake, behaving, subjects. Here we used VSDI to measure the dynamics of neural population responses in macaque V1 to visual stimuli with a wide range of time courses. We found that beyond clear nonlinearities for briefly presented visual stimuli, stimulus-evoked VSDI responses are surprisingly near-additive in time. These results are qualitatively different from neural dynamics to similar stimuli previously measured in human visual cortex using fMRI and electrocorticography (ECoG), which show strong sub-additivity in time. To test whether this discrepancy is specific to VSDI, a signal dominated by sub-threshold neural activity, we repeated our measurements using a genetically encoded calcium indicator (GCaMP), a signal dominated by spiking activity. We found that GCaMP signals in macaque V1 are also near additive. Therefore, the discrepancies in the degree of additivity between these different measurements are not attributed to the difference between sub- and supra-threshold neural response. Finally, we show that a simple yet flexible delayed normalization model can capture the dynamics of all of these measurements, suggesting that dynamic gain-control is an important mechanism contributing to neural processing in the brain.

Delayed accumulation of inhibitory input explains gamma frequency variation with changing contrast in an Inhibition Stabilized Network

“…Such slow dynamics of inhibition also yield transients in non-oscillatory activity. In recent studies (Zhou et al, 2019(Zhou et al, , 2023…”