Spiking ink drop spread clustering algorithm and its memristor crossbar conceptual hardware design

Abstract

In this study, a new clustering algorithm that combines neural networks and fuzzy logic properties is proposed based on spiking neural network and ink drop spread (IDS) concepts. The proposed structure is a single-layer artificial neural network with leaky integrate and fire (LIF) neurons. The structure implements the IDS algorithm as a fuzzy concept. Each training data will result in firing the corresponding input neuron and its neighboring neurons. A synchronous time coding algorithm is used to manage input and output neurons firing time. For an input data, one or several output neurons of the network will fire; confidence degree of the network to outputs is defined as the relative delay of the firing times with respect to the synchronous pulse. A memristor crossbar-based hardware is introduced for implementation of the proposed algorithm as a processing hardware. The simulation result corroborates that the proposed algorithm can be used as a neuro-fuzzy clustering and vector quantization algorithm.