  Spiking neural networks (SNNs) receive widespread attention because of their
low-power hardware characteristic and brain-like signal response mechanism, but
currently, the performance of SNNs is still behind Artificial Neural Networks
(ANNs). We build an information theory-inspired system called Stochastic
Probability Adjustment (SPA) system to reduce this gap. The SPA maps the
synapses and neurons of SNNs into a probability space where a neuron and all
connected pre-synapses are represented by a cluster. The movement of synaptic
transmitter between different clusters is modeled as a Brownian-like stochastic
process in which the transmitter distribution is adaptive at different firing
phases. We experimented with a wide range of existing unsupervised SNN
architectures and achieved consistent performance improvements. The
improvements in classification accuracy have reached 1.99% and 6.29% on the
MNIST and EMNIST datasets respectively.
