WOLIF: An efficiently tuned classifier that learns to classify non-linear temporal patterns without hidden layers

Abstract

We present in this paper a computationally efficient and biologically plausible classifier WOLIF, using Grey Wolf Optimizer (GWO) tuned error function obtained from Leaky-Integrate-and-Fire (LIF) spiking neuron. Unlike traditional artificial neuron, spiking neuron is capable of intelligently classifying non-linear temporal patterns without hidden layer(s), which makes a Spiking Neural Network (SNN) computationally efficient. There is no additional cost of adding hidden layer(s) in SNN, it is also biologically plausible, and energy efficient. Since supervised learning rule for SNN is still in infancy stage, we introduced WOLIF classifier and its supervised learning rule based on GWO algorithm. WOLIF uses a single LIF neuron thereby use less network parameters, and homo-synaptic static long-term synaptic weights (both excitatory and inhibitory). Note that, WOLIF also reduces the total simulation time which improves computational efficiency. It is benchmarked on seven different datasets drawn from the UCI machine learning repository and found better results both in terms of accuracy and computational cost than state-of-the-art methods.