论文标题
具有结构化连通性的神经网络中的空间和时间相关性
Spatial and temporal correlations in neural networks with structured connectivity
论文作者
论文摘要
神经种群活动的相关波动反映了网络的动态和连通性。神经相关性的时间和空间维度相互依存。但是，事先理论工作主要分析了空间或时间领域中的相关性，却忽略了它们的相互作用。我们表明，网络动力学和连通性共同定义了神经相关性的时空特征。我们在一个和二维中具有空间布置的连通性的二进制单元网络中的成对相关性得出分析表达式。我们发现单位之间的空间相互作用在自动和互相关中产生多个时间尺度。每个时间尺度都与特定空间频率的波动有关，从而对相关产生层次贡献。当空间相互作用是非线性时，外部输入可以调节相关时间尺度，并且调制效果取决于网络动力学的工作状态。这些理论结果通过测量时空神经相关性，开辟了新的方法，以将皮质网络中的连通性和动力学联系起来。
Correlated fluctuations in the activity of neural populations reflect the network's dynamics and connectivity. The temporal and spatial dimensions of neural correlations are interdependent. However, prior theoretical work mainly analyzed correlations in either spatial or temporal domains, oblivious to their interplay. We show that the network dynamics and connectivity jointly define the spatiotemporal profile of neural correlations. We derive analytical expressions for pairwise correlations in networks of binary units with spatially arranged connectivity in one and two dimensions. We find that spatial interactions among units generate multiple timescales in auto- and cross-correlations. Each timescale is associated with fluctuations at a particular spatial frequency, making a hierarchical contribution to the correlations. External inputs can modulate the correlation timescales when spatial interactions are nonlinear, and the modulation effect depends on the operating regime of network dynamics. These theoretical results open new ways to relate connectivity and dynamics in cortical networks via measurements of spatiotemporal neural correlations.
