Sequential Attractors in Combinatorial Threshold-Linear Networks.
Sequential Attractors in Combinatorial Threshold-Linear Networks.
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DOI：
10.1137/21m1445120
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发表时间：
2022
影响因子：
2.1
通讯作者：
中科院分区：
文献类型：
--
作者：
神经活动的序列出现在许多大脑区域，包括皮层、海马体和构成运动等有节奏行为的中央模式产生电路。虽然支持序列生成的网络体系结构差异很大，但一个共同的特征是大量的抑制。在这项工作中，我们专注于支持具有抑制主导动态的递归连接网络中的顺序活动的体系结构。具体地说，我们研究了一类特殊的门限线性网络中的涌现序列，称为组合门限线性网络(CTLN)，其连通性矩阵是由有向图定义的。这样的网络自然会产生大量的序列，它们的动态与底层的图形紧密相连。我们发现，基于圈图推广的体系结构产生极限环吸引子，该极限环吸引子可以被激活以产生瞬时或持久(重复)序列。每种体系结构类型都会产生一个无限的图族，这些图族可以从任意的组件子图中构建。此外，我们还证明了每个族中对应的CTLN的若干图规则。图规则允许我们强烈地约束网络的不动点，并且在某些情况下完全确定网络的不动点。最后，我们还展示了某些结构的结构如何洞察相应吸引子的顺序动力学。
Sequences of neural activity arise in many brain areas, including cortex, hippocampus, and central pattern generator circuits that underlie rhythmic behaviors like locomotion. While network architectures supporting sequence generation vary considerably, a common feature is an abundance of inhibition. In this work, we focus on architectures that support sequential activity in recurrently connected networks with inhibition-dominated dynamics. Specifically, we study emergent sequences in a special family of threshold-linear networks, called combinatorial threshold-linear networks (CTLNs), whose connectivity matrices are defined from directed graphs. Such networks naturally give rise to an abundance of sequences whose dynamics are tightly connected to the underlying graph. We find that architectures based on generalizations of cycle graphs produce limit cycle attractors that can be activated to generate transient or persistent (repeating) sequences. Each architecture type gives rise to an infinite family of graphs that can be built from arbitrary component subgraphs. Moreover, we prove a number of graph rules for the corresponding CTLNs in each family. The graph rules allow us to strongly constrain, and in some cases fully determine, the fixed points of the network in terms of the fixed points of the component subnetworks. Finally, we also show how the structure of certain architectures gives insight into the sequential dynamics of the corresponding attractor.
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影响因子：
16.2
作者：
Fino E;Yuste R
通讯作者：
Yuste R
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
5.3
作者：
Arriaga, Moises;Han, Edward B.
通讯作者：
Han, Edward B.
影响因子：
25
作者：
Girardeau, Gabrielle;Benchenane, Karim;Zugaro, Michael B.
通讯作者：
Zugaro, Michael B.
影响因子：
6
作者：
Aviel, Y;Pavlov, E;Horn, D
通讯作者：
Horn, D