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arXiv 2008.01032math.COq-bio.NC
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非线性网络的组合几何

Combinatorial Geometry of Threshold-Linear Networks

Carina Curto, Christopher Langdon, Katherine Morrison

摘要:

神经网络的架构限制了可能出现的潜在动态。有些架构可能只允许单一的动态机制,而另一些架构则表现出很大的灵活性,具有不同的动态特性,可以通过调节连接强度来实现。在这项工作中,我们开发了新的数学技术来研究竞争阈值线性网络(TLN)的背景下,不同的网络架构所施加的动态约束。任何给定的TLN都自然地以$\mathbb{R}^n$中的超平面排列为特征,并且这种排列的组合性质决定了动力学的不动点的模式。这一观察使我们能够重铸的语言的定向拟阵的网络灵活性的问题,使我们能够采用工具和结果,从这个理论,以表征不同的动态制度的一个给定的架构可以支持。特别地,TLN的不动点对应于相关联的定向拟阵的余回路;并且拟阵的突变对应于不动点的集合中的分叉。作为一个应用程序,我们提供了一个完整的表征所有可能的不动点,可以出现在网络中通过大小$n=3$,以及如何调制突触强度的网络,以访问不同的动态制度的描述。这些结果为研究在真实神经网络中观察到的各种基序的可能计算角色提供了一个框架。

英文摘要:

The architecture of a neural network constrains the potential dynamics that can emerge. Some architectures may only allow for a single dynamic regime, while others display a great deal of flexibility with qualitatively different dynamics that can be reached by modulating connection strengths. In this work, we develop novel mathematical techniques to study the dynamic constraints imposed by different network architectures in the context of competitive threshold-linear networks (TLNs). Any given TLN is naturally characterized by a hyperplane arrangement in $\mathbb{R}^n$, and the combinatorial properties of this arrangement determine the pattern of fixed points of the dynamics. This observation enables us to recast the question of network flexibility in the language of oriented matroids, allowing us to employ tools and results from this theory in order to characterize the different dynamic regimes a given architecture can support. In particular, fixed points of a TLN correspond to cocircuits of an associated oriented matroid; and mutations of the matroid correspond to bifurcations in the collection of fixed points. As an application, we provide a complete characterization of all possible sets of fixed points that can arise in networks through size $n=3$, together with descriptions of how to modulate synaptic strengths of the network in order to access the different dynamic regimes. These results provide a framework for studying the possible computational roles of various motifs observed in real neural networks.

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