Unsupervised Character Recognition with a Simplified FPGA Neuromorphic System

Abstract

Neuromorphic hardware platforms have demonstrated significant promise in cognitive tasks such as visual processing and classification. These platforms usually consist of several layers of spiking neurons for feature extraction and various learning mechanisms, which renders the associated networks power and hardware hungry. In this paper, we have implemented a simplified proof-of-concept Spiking Neural Network (SNN) on a Field Programmable Gate Array (FPGA) and trained it using Spike Timing Dependent Plasticity (STDP) to identify temporally encoded characters, in an unsupervised manner. The constructed one-layer network consists of excitatory synapses, which receive input characters in the form of Poissonian spike trains from the pre-synaptic side. From the post-synaptic side, the synapses are connected to output Izhikevich neurons. In addition, non-plastic inhibitory synapses between the output neurons are introduced to implement lateral inhibition and competitive learning. The implemented neural hardware demonstrates a powerful and fast learning scheme, which brings about a significant unsupervised classification accuracy of 94 %. In addition, since the proposed network receives the characters in the form of spike trains, it is amenable to being interfaced to neuromorphic event-driven sensors such as silicon retina, making the proposed platform useful for online unsupervised template matching applications.