Rosenbaum Robert, Smith Matthew A, Kohn Adam, Rubin Jonathan E, Doiron Brent
Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, USA.
Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana, USA.
Nat Neurosci. 2017 Jan;20(1):107-114. doi: 10.1038/nn.4433. Epub 2016 Oct 31.
Shared neural variability is ubiquitous in cortical populations. While this variability is presumed to arise from overlapping synaptic input, its precise relationship to local circuit architecture remains unclear. We combine computational models and in vivo recordings to study the relationship between the spatial structure of connectivity and correlated variability in neural circuits. Extending the theory of networks with balanced excitation and inhibition, we find that spatially localized lateral projections promote weakly correlated spiking, but broader lateral projections produce a distinctive spatial correlation structure: nearby neuron pairs are positively correlated, pairs at intermediate distances are negatively correlated and distant pairs are weakly correlated. This non-monotonic dependence of correlation on distance is revealed in a new analysis of recordings from superficial layers of macaque primary visual cortex. Our findings show that incorporating distance-dependent connectivity improves the extent to which balanced network theory can explain correlated neural variability.
共享神经变异性在皮层群体中普遍存在。虽然这种变异性被认为源于重叠的突触输入，但其与局部电路结构的确切关系仍不清楚。我们结合计算模型和体内记录来研究神经回路中连接性的空间结构与相关变异性之间的关系。扩展具有平衡兴奋和抑制的网络理论，我们发现空间局部化的侧向投射促进弱相关的放电，但更广泛的侧向投射产生独特的空间相关结构：相邻神经元对呈正相关，中间距离的神经元对呈负相关，远距离的神经元对呈弱相关。这种相关性对距离的非单调依赖性在对猕猴初级视觉皮层表层记录的新分析中得以揭示。我们的研究结果表明，纳入距离依赖性连接性可提高平衡网络理论解释相关神经变异性的程度。
