Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois, United States of America.
Department of Neurobiology, University of Chicago, Chicago, Illinois, United States of America.
PLoS Comput Biol. 2020 Sep 30;16(9):e1007409. doi: 10.1371/journal.pcbi.1007409. eCollection 2020 Sep.
A basic-yet nontrivial-function which neocortical circuitry must satisfy is the ability to maintain stable spiking activity over time. Stable neocortical activity is asynchronous, critical, and low rate, and these features of spiking dynamics contribute to efficient computation and optimal information propagation. However, it remains unclear how neocortex maintains this asynchronous spiking regime. Here we algorithmically construct spiking neural network models, each composed of 5000 neurons. Network construction synthesized topological statistics from neocortex with a set of objective functions identifying naturalistic low-rate, asynchronous, and critical activity. We find that simulations run on the same topology exhibit sustained asynchronous activity under certain sets of initial membrane voltages but truncated activity under others. Synchrony, rate, and criticality do not provide a full explanation of this dichotomy. Consequently, in order to achieve mechanistic understanding of sustained asynchronous activity, we summarized activity as functional graphs where edges between units are defined by pairwise spike dependencies. We then analyzed the intersection between functional edges and synaptic connectivity- i.e. recruitment networks. Higher-order patterns, such as triplet or triangle motifs, have been tied to cooperativity and integration. We find, over time in each sustained simulation, low-variance periodic transitions between isomorphic triangle motifs in the recruitment networks. We quantify the phenomenon as a Markov process and discover that if the network fails to engage this stereotyped regime of motif dominance "cycling", spiking activity truncates early. Cycling of motif dominance generalized across manipulations of synaptic weights and topologies, demonstrating the robustness of this regime for maintenance of network activity. Our results point to the crucial role of excitatory higher-order patterns in sustaining asynchronous activity in sparse recurrent networks. They also provide a possible explanation why such connectivity and activity patterns have been prominently reported in neocortex.
大脑新皮质电路必须满足的一个基本但并非微不足道的功能是能够随着时间的推移维持稳定的尖峰活动。稳定的新皮质活动是异步的、关键的和低速率的，这些尖峰动力学特征有助于高效计算和最优信息传递。然而，目前尚不清楚新皮质如何维持这种异步尖峰状态。在这里，我们算法地构建了尖峰神经网络模型，每个模型由 5000 个神经元组成。网络构建从新皮质中综合了拓扑统计数据，并使用一组目标函数来识别自然的低速率、异步和关键活动。我们发现，在相同拓扑结构上进行的模拟在某些组的初始膜电压下表现出持续的异步活动，但在其他组下则表现出截断的活动。同步性、速率和关键性能并不能完全解释这种二分法。因此，为了实现对持续异步活动的机制理解，我们将活动总结为功能图，其中单元之间的边由成对的尖峰依赖性定义。然后，我们分析了功能边和突触连接之间的交点——即招募网络。更高阶的模式，如三元组或三角形模式，与协同作用和整合有关。我们发现，在每个持续模拟中，随着时间的推移，招募网络中的同构三角形模式之间会出现低方差的周期性转换。我们将该现象量化为一个马尔可夫过程，并发现如果网络未能参与这种模式主导的刻板规则“循环”，那么尖峰活动就会提前截断。模式主导的循环在突触权重和拓扑结构的各种操作中都得到了推广，证明了这种规则对于维持网络活动的稳健性。我们的结果表明，兴奋性高阶模式在维持稀疏递归网络中的异步活动中起着至关重要的作用。它们还为为什么这种连接和活动模式在新皮质中得到了突出的报道提供了可能的解释。
