A novel un-supervised burst time dependent plasticity learning approach for biologically pattern recognition networks

Abstract

Bio-inspired computing is an appropriate platform for developing artificial intelligent machines based on the behavioral and functional principles of the brain. Bio-inspired machines have been proven to play a significant role in the development of intelligent systems with spike-based operation being a key feature. However, spikes by themselves do not contain much information and may not cross the synapse and stimulate the post-synaptic neuron while bursts consisting of short trains of high-frequency spikes provide more potent information coding facilities. In this study, a pattern recognition network is proposed that consists of an input layer (adapted from a retinal model), middle layer (bio-inspired spiking neural network with bursting neurons and excitatory and inhibitory AMPA (alpha-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid) and GABA (Gamma-aminobutyric acid) synapses) and output layer (pyramidal neurons as classifying neurons). For the first time, a novel unsupervised burst-based learning algorithm inspired by spike-time-dependent-plasticity (STDP) is developed, called Burst Time Dependent Plasticity (BTDP). Compared to STDP, BTDP yields a higher performance accuracy and faster convergence rate of spiking pattern recognition networks when classifying EMNIST and CIFAR10 datasets compared to existing spiking networks. The proposed spiking network, trained by the novel unsupervised learning algorithm, is able to compete with advanced deep networks in recognizing complex patterns while being amenable to implementation on neuromorphic hardware platforms.