SPA: Stochastic Probability Adjustment for System Balance of Unsupervised SNNs
- Resource Type
- Conference
- Authors
- Yang, Xingyu; Meng, Mingyuan; Xiao, Shanlin; Yu, Zhiyi
- Source
- 2020 25th International Conference on Pattern Recognition (ICPR) Pattern Recognition (ICPR), 2020 25th International Conference on. :6417-6424 Jan, 2021
- Subject
- Computing and Processing
Signal Processing and Analysis
Training
Transmitters
Computational modeling
Biological system modeling
Neurons
Stochastic processes
Computer architecture
Spiking neural network
Stochastic model
Unsupervised learning
Brownian process
- Language
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.