Abstract
Gamma oscillation (GAMMA) in the local field potential (LFP) is a synchronized activity commonly found in many brain regions, and it has been thought as a functional signature of network connectivity in the brain, which plays important roles in information processing. Studies have shown that the response property of GAMMA is related to neural interaction through local recurrent connections (RC), feed-forward (FF), and feedback (FB) connections. However, the relationship between GAMMA and long-range horizontal connections (HC) in the brain remains unclear. Here, we aimed to understand this question in a large-scale network model for the primary visual cortex (V1). We created a computational model composed of multiple excitatory and inhibitory units with biologically plausible connectivity patterns for RC, FF, FB, and HC in V1; then, we quantitated GAMMA in network models at different strength levels of HC and other connection types. Surprisingly, we found that HC and FB, the two types of large-scale connections, play very different roles in generating and modulating GAMMA. While both FB and HC modulate a fast gamma oscillation (around 50-60 Hz) generated by FF and RC, HC generates a new GAMMA oscillating around 30 Hz, whose power and peak frequency can also be modulated by FB. Furthermore, response properties of the two GAMMAs in a network with both HC and FB are different in a way that is highly consistent with a recent experimental finding for distinct GAMMAs in macaque V1. The results suggest that distinct GAMMAs are signatures for neural connections in different spatial scales and they might be related to different functions for information integration. Our study, for the first time, pinpoints the underlying circuits for distinct GAMMAs in a mechanistic model for macaque V1, which might provide a new framework to study multiple gamma oscillations in other cortical regions.
Highlights
Gamma oscillation in the local field potential (LFP) of the visual cortex is thought to play important roles in synchronizing neurons’ response in the local network [1, 2] of many brain regions [3,4,5,6]
When the different local E-I units are connected through long-range horizontal connections (HC), the weights for HC are decayed with distance between two E-I units (Equation (7))
The model architectures for feed-forward connection (FF), recurrent connection (RC), HC, and feedback connection (FB) are based on existing models for studying gamma oscillation or other functional properties in V1 [17, 40, 61, 62]
Summary
Gamma oscillation in the LFP of the visual cortex is thought to play important roles in synchronizing neurons’ response in the local network [1, 2] of many brain regions [3,4,5,6]. How neural connectivity patterns affect the property of gamma oscillation in the primary visual cortex of macaque (V1) remains unclear. Anatomical studies [23,24,25,26,27,28] have shown that V1 has rich neural connection patterns, including feed-forward connection (FF), local recurrent connection (RC), long-distance horizontal connection (HC), and feedback connection (FB). These connection patterns have distinct characteristics and together they form a complex dynamic system for V1.
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