A robust modular spiking neural networks training methodology for time-series datasets

Abstract

Neurons in Spiking Neural Networks (SNNs) communicate through spikes, similarly that neurons in the brain communicate, thus mimicking the brain. The working of SNNs is temporally based, as the spikes are time-dependent. SNNs have the benefit to perform continual classification, and are inherently more low-power than other Artificial Neural Networks (ANNs). Both the SNNs and other ANNs need to be trained to perform specific tasks. There are several types of methodologies to train SNNs, but there is yet no silver bullet. Backpropagation algorithms can train other ANNs, but SNNs cannot be trained using this algorithm since the spikes are not differentiable. Methods like Spike Timing Dependent Plasticity (STDP) or Liquid State Machine (LSM) have their limits. Where the complexity of SNNs depends on the dataset, the number of neurons, and other factors. There were currently no known SNN implementations for the given gesture, achieving near state-of-the-art results. The problem with the dataset is that usually no gesture is performed in front of the . The datasets contain noise, and some data samples belong to two or more classes simultaneously. The objective of this work is to develop an architecture and training methodology, that allows the classification of the dataset using SNNs. This work presents a novel, architectural training methodology Suino, which addresses the above problems. The architecture consists of two components: the first is the spatial classifier, and the second component is the temporal classifier. The frames from the dataset are filtered in the first stage, i.e. the spatial classifier. The output of the spatial classifier is the input for the temporal classifier, which deals with the temporal properties of the data. Suino does not provide false positives, that is the neurons do not spike on the input dataset if the input dataset does not belong to any of the trained classes; hence the method is robust. The training method is built around these components, existing of different classical training methods: backpropagation, clustering, or any other consisting of fixed threshold method for auto label correction. The second stage consists of a temporal classification method, trained using the Tempotron learning rule. The time-series dataset of gestures validated Suino. On the test set, the baseline method had an accuracy of 97.0% with 35K parameters, while the presented method had an accuracy of 90.87% with 21K parameters. Hence, the method is more robust against false detections and continuously performs classification.