Effect of long-range connections on surface inhibition in V1 area based on large-scale neural network modeling

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Abstract

The differentiation of neural activity in the primary visual cortex (V1) for the edges and surface regions of stimuli is a key feature in representing visual information. This differentiation is closely related to the phenomenon of surface inhibition, where neural activity in central surface regions is suppressed relative to edges of square stimuli. However, due to the challenges in manipulating biological experiments and the limitations of simplified models, the detailed synaptic-level analysis of the regulatory mechanisms underlying this differentiation has not been revealed. In this study, a visual information transmission pathway from visual stimuli to the lateral geniculate nucleus (LGN) of the thalamus, and further to the input and output layers of V1, is constructed by using real biological anatomical data to investigate the regulatory mechanisms underlying the aforementioned neural activity differentiation. The model successfully replicates the surface inhibition characteristics observed in biological experiments: the input layer exhibits relatively uniform responses to both surface and edge stimuli, while the output layer shows strong activity in edge regions and suppressed activity in central surface regions, creating a “hole” effect. Through control experiments-specifically, by eliminating long-range connections in L2/3-we find that long-range connections in layer 2/3 of V1 are the necessary conditions for generating the surface inhibition phenomenon within this layer. Furthermore, modulating either the proportion or the spatial distribution of long-range connections in L2/3 can exert regulatory effects on the surface inhibition phenomenon. This not only facilitates our understanding of the neural mechanisms underlying visual processing but also demonstrates the advantages of computational modeling in elucidating internal cortical mechanisms that are difficult to manipulate in biological experiments.

Keywords

computational model / primary visual cortex / long-range connections / hole effect

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