Department of Physics, Institute of Neuroscience, University of Oregon, Eugene, Oregon, United States of America.
Deptartment of Neuroscience, Center for Theoretical Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, College of Physicians and Surgeons, and Morton B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America.
PLoS Comput Biol. 2024 Jun 27;20(6):e1012190. doi: 10.1371/journal.pcbi.1012190. eCollection 2024 Jun.
When stimulated, neural populations in the visual cortex exhibit fast rhythmic activity with frequencies in the gamma band (30-80 Hz). The gamma rhythm manifests as a broad resonance peak in the power-spectrum of recorded local field potentials, which exhibits various stimulus dependencies. In particular, in macaque primary visual cortex (V1), the gamma peak frequency increases with increasing stimulus contrast. Moreover, this contrast dependence is local: when contrast varies smoothly over visual space, the gamma peak frequency in each cortical column is controlled by the local contrast in that column's receptive field. No parsimonious mechanistic explanation for these contrast dependencies of V1 gamma oscillations has been proposed. The stabilized supralinear network (SSN) is a mechanistic model of cortical circuits that has accounted for a range of visual cortical response nonlinearities and contextual modulations, as well as their contrast dependence. Here, we begin by showing that a reduced SSN model without retinotopy robustly captures the contrast dependence of gamma peak frequency, and provides a mechanistic explanation for this effect based on the observed non-saturating and supralinear input-output function of V1 neurons. Given this result, the local dependence on contrast can trivially be captured in a retinotopic SSN which however lacks horizontal synaptic connections between its cortical columns. However, long-range horizontal connections in V1 are in fact strong, and underlie contextual modulation effects such as surround suppression. We thus explored whether a retinotopically organized SSN model of V1 with strong excitatory horizontal connections can exhibit both surround suppression and the local contrast dependence of gamma peak frequency. We found that retinotopic SSNs can account for both effects, but only when the horizontal excitatory projections are composed of two components with different patterns of spatial fall-off with distance: a short-range component that only targets the source column, combined with a long-range component that targets columns neighboring the source column. We thus make a specific qualitative prediction for the spatial structure of horizontal connections in macaque V1, consistent with the columnar structure of cortex.
当受到刺激时，视觉皮层中的神经元群体表现出具有伽马频段（30-80Hz）的快速节奏活动。伽马节律在记录的局部场电位的功率谱中表现为一个宽的共振峰，其表现出各种刺激依赖性。特别是在猕猴初级视觉皮层（V1）中，伽马峰值频率随刺激对比度的增加而增加。此外，这种对比度依赖性是局部的：当对比度在视觉空间中平滑变化时，每个皮层柱的伽马峰值频率由该柱感受野中的局部对比度控制。尚未提出对 V1 伽马振荡的这些对比度依赖性的简约机制解释。稳定超线性网络（SSN）是皮层电路的机制模型，它解释了一系列视觉皮层反应的非线性和上下文调制，以及它们的对比度依赖性。在这里，我们首先表明，没有视网膜拓扑的简化 SSN 模型可以稳健地捕捉伽马峰值频率的对比度依赖性，并基于观察到的 V1 神经元的非饱和和超线性输入-输出函数，为这种效应提供机制解释。鉴于此结果，局部对比度依赖性可以在具有视网膜拓扑的 SSN 中简单地捕获，但是它缺乏皮层柱之间的水平突触连接。然而，V1 中的长程水平连接实际上很强，并构成了上下文调制效应，例如环绕抑制。因此，我们探索了具有强兴奋性水平连接的 V1 的具有视网膜拓扑的 SSN 模型是否可以表现出环绕抑制和伽马峰值频率的局部对比度依赖性。我们发现，视网膜拓扑 SSN 可以解释这两种效应，但前提是水平兴奋性投射由具有不同空间衰减模式的两个分量组成：仅针对源柱的短程分量，与针对源柱相邻柱的长程分量相结合。因此，我们对猕猴 V1 中的水平连接的空间结构做出了具体的定性预测，这与皮层的柱状结构一致。
