Allen Institute for Brain Science, United States.
Center for Brain Science, Harvard University, United States.
Curr Opin Neurobiol. 2017 Oct;46:109-119. doi: 10.1016/j.conb.2017.07.011. Epub 2017 Aug 30.
An essential step toward understanding neural circuits is linking their structure and their dynamics. In general, this relationship can be almost arbitrarily complex. Recent theoretical work has, however, begun to identify some broad principles underlying collective spiking activity in neural circuits. The first is that local features of network connectivity can be surprisingly effective in predicting global statistics of activity across a network. The second is that, for the important case of large networks with excitatory-inhibitory balance, correlated spiking persists or vanishes depending on the spatial scales of recurrent and feedforward connectivity. We close by showing how these ideas, together with plasticity rules, can help to close the loop between network structure and activity statistics.
理解神经回路的一个重要步骤是将它们的结构和动力学联系起来。一般来说，这种关系可能非常复杂。然而，最近的理论工作已经开始确定一些普遍存在于神经回路集体尖峰活动中的基本原则。第一个原则是，网络连接的局部特征可以在预测整个网络的活动全局统计方面非常有效。第二个原则是，对于具有兴奋-抑制平衡的大型网络的重要情况，相关的尖峰活动取决于递归和前馈连接的空间尺度，要么持续存在，要么消失。最后，我们展示了这些想法如何与可塑性规则一起，有助于在网络结构和活动统计之间形成闭环。
