Dynamically Evolving Spiking Neural network for pattern recognition

This paper presents a novel RBF-like fast dynamically Evolving Spiking Neural classifier (ESNC). The trained feed-forward SNN consists of three layers of spiking neurons: an encoding layer which temporally encodes real valued features into spatio-temporal spike patterns, a hidden layer of dynamically grown and pruned neurons which perform spatiotemporal clustering, and an evolving output layer for classification. Both the structure and weights of the SNN are learned dynamically through a combination of unsupervised and supervised learning paradigms. An unsupervised clustering method is implemented by the hidden layer for adjusting the synaptic weights of the hidden neurons afferent connections. The centre of each hidden RBF neuron is represented by a vector of temporal distances between the first spike of the hidden neuron and the presynaptic spikes. In addition, strategies are proposed to adjust the structure of the hidden and output layers as inputs are presented to the SNN, and classification at the output layer is achieved through supervised learning where a learning window is used to adjust the weights of the output neurons afferent connections. The proposed learning algorithm is demonstrated on several benchmark datasets from the UCL machine learning repository. The results show comparable performance with existing machine learning algorithms and demonstrate the ability of the proposed algorithm to learn incoming data samples in a hybrid way and in one epoch only.