A satisfactory understanding of information processing in spiking neural networks requires appropriate computational abstractions of neural activity. Traditionally, the neural population state vector has been the most common abstraction applied to spiking neural networks, but this requires artificially partitioning time into bins that are not obviously relevant to the network itself. We introduce a distinct set of techniques for analyzing spiking neural networks that decomposes neural activity into multiple, disjoint, parallel threads of activity. We construct these threads by estimating the degree of causal relatedness between pairs of spikes, then use these estimates to construct a directed acyclic graph that traces how the network activity evolves through individual spikes. We find that this graph of spiking activity naturally decomposes into disjoint connected components that overlap in space and time, which we call Graphical Neural Activity Threads (GNATs). We provide an efficient algorithm for finding analogous threads that reoccur in large spiking datasets, revealing that seemingly distinct spike trains are composed of similar underlying threads of activity, a hallmark of compositionality. The picture of spiking neural networks provided by our GNAT analysis points to new abstractions for spiking neural computation that are naturally adapted to the spatiotemporally distributed dynamics of spiking neural networks.

翻译：对脉冲神经网络中信息处理的充分理解需要合适的神经活动计算抽象。传统上，神经群体状态向量是对脉冲神经网络最常用的抽象方法，但这需要人为地将时间划分为与网络本身无明显关联的时段。我们引入了一套独特的脉冲神经网络分析技术，将神经活动分解为多个互不相交的并行活动线程。通过估计脉冲对之间的因果关联程度构建这些线程，进而利用这些估计值建立有向无环图，追踪网络活动如何通过单个脉冲演化。我们发现该脉冲活动图自然分解为时空重叠的独立连通分量，称之为图神经活动线程（GNATs）。我们提出高效算法来寻找大规模脉冲数据集中重复出现的相似线程，揭示看似不同的脉冲序列实际上由相同的基础活动线程构成——这一特性体现了组合性。通过GNAT分析呈现的脉冲神经网络图景，为脉冲神经计算提供了自然适配于时空分布动态特性的新抽象方法。