Decomposing spiking neural networks with Graphical Neural Activity Threads

TL;DR

This paper introduces Graphical Neural Activity Threads (GNATs), a novel method for decomposing spiking neural network activity into causal, interpretable threads, revealing underlying compositional structures and offering new computational abstractions.

Contribution

The paper presents a new technique for analyzing spiking neural networks by decomposing activity into causal threads using directed acyclic graphs, advancing understanding of neural computation.

Findings

GNATs naturally decompose neural activity into overlapping, causally related threads.

Similar activity threads recur across large datasets, indicating compositionality.

The method provides new insights into the spatiotemporal dynamics of neural networks.

Abstract

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.