Simple, static, spiking nonlinearities can account for seemingly complex interactions between visual, motor and cognitive variables in the visual cortex.
- 1NIH
Abstract
Neural activity in the visual cortex is driven by the stimulus and modulated by multiple task-related variables, including spatial attention, eye movements, and perceptual decisions. These multiple influences are each typically described as nonlinear interactions such as gain or normalization, raising the question of how these diverse signals can be combined through cellular or circuit mechanisms. Here we address this problem by examining neuronal responses in macaque visual areas V2 and V3 during a visual discrimination task that dissociates spatial attention from motor planning and microsaccadic eye movements. Using a statistical modeling framework, we show that attentional, microsaccade-related, and choice-related influences on neural activity are best explained by a simple additive stimulus-independent input modulation, which manifests as a gain via a spiking nonlinearity. In addition to explaining how interactions between variables might be accomplished mechanistically, this framework also resolves a controversy about the relationship between microsaccades and spatial attention. We find that neither microsaccade direction nor microsaccade-related neural activity accounts for attentional modulation, although these variables are often correlated, leading to apparent interactions. Thus, visual cortex can integrate sensory, motor-related, and cognitive signals through a parsimonious mechanism that preserves stable stimulus representations while supporting flexible, context-dependent modulation.
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BibTeX
@misc{zajkowski2026simple,
title = {{Simple, static, spiking nonlinearities can account for seemingly complex interactions between visual, motor and cognitive variables in the visual cortex.}},
author = {W. K. Zajkowski and K. Quinn and L. Seillier and M. E. Bellet and D. A. Butts and H. Nienborg},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.01.763479},
url = {https://www.biorxiv.org/content/10.64898/2026.11.01.763479v1},
note = {CC0},
}
RIS
TY - UNPB
TI - Simple, static, spiking nonlinearities can account for seemingly complex interactions between visual, motor and cognitive variables in the visual cortex.
AU - W. K. Zajkowski
AU - K. Quinn
AU - L. Seillier
AU - M. E. Bellet
AU - D. A. Butts
AU - H. Nienborg
PY - 2026
DA - 2026/11/01
PB - bioRxiv
DO - 10.64898/2026.11.01.763479
UR - https://www.biorxiv.org/content/10.64898/2026.11.01.763479v1
AB - Neural activity in the visual cortex is driven by the stimulus and modulated by multiple task-related variables, including spatial attention, eye movements, and perceptual decisions. These multiple influences are each typically described as nonlinear interactions such as gain or normalization, raising the question of how these diverse signals can be combined through cellular or circuit mechanisms. Here we address this problem by examining neuronal responses in macaque visual areas V2 and V3 during a visual discrimination task that dissociates spatial attention from motor planning and microsaccadic eye movements. Using a statistical modeling framework, we show that attentional, microsaccade-related, and choice-related influences on neural activity are best explained by a simple additive stimulus-independent input modulation, which manifests as a gain via a spiking nonlinearity. In addition to explaining how interactions between variables might be accomplished mechanistically, this framework also resolves a controversy about the relationship between microsaccades and spatial attention. We find that neither microsaccade direction nor microsaccade-related neural activity accounts for attentional modulation, although these variables are often correlated, leading to apparent interactions. Thus, visual cortex can integrate sensory, motor-related, and cognitive signals through a parsimonious mechanism that preserves stable stimulus representations while supporting flexible, context-dependent modulation.
ER -