A Basic Compositional Model for Spiking Neural Networks
This provides a foundational framework for modeling SNNs, which is incremental as it builds on existing concurrency theory paradigms.
The authors developed a formal mathematical model for synchronous, stochastic spiking neural networks (SNNs) based on concurrency theory, defining composition and hiding operators to analyze network behavior and problem-solving capabilities, with illustrative examples including Boolean circuits and attention networks.
We present a formal, mathematical foundation for modeling and reasoning about the behavior of $synchronous$, $stochastic$ $Spiking$ $Neural$ $Networks$ $(SNNs)$, which have been widely used in studies of neural computation. Our approach follows paradigms established in the field of concurrency theory. Our SNN model is based on directed graphs of neurons, classified as input, output, and internal neurons. We focus here on basic SNNs, in which a neuron's only state is a Boolean value indicating whether or not the neuron is currently firing. We also define the $external$ $behavior$ of an SNN, in terms of probability distributions on its external firing patterns. We define two operators on SNNs: a $composition$ $operator$, which supports modeling of SNNs as combinations of smaller SNNs, and a $hiding$ $operator$, which reclassifies some output behavior of an SNN as internal. We prove results showing how the external behavior of a network built using these operators is related to the external behavior of its component networks. Finally, we define the notion of a $problem$ to be solved by an SNN, and show how the composition and hiding operators affect the problems that are solved by the networks. We illustrate our definitions with three examples: a Boolean circuit constructed from gates, an $Attention$ network constructed from a $Winner$-$Take$-$All$ network and a $Filter$ network, and a toy example involving combining two networks in a cyclic fashion.