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On-device STDP and synaptic normalization for neuromemristive spiking neural network

Spiking Neural Networks offer low precision communication, robustness, and low power consumption and are attractive for autonomous applications. One of the well accepted learning rules for these networks is spike time dependent plasticity which is governed by the pre- and postsynaptic spike timings. To stabilize the plasticity and avoid saturation in these learning rules, synaptic normalization is used. In this work, we propose a circuit to efficiently realize synaptic normalization in a neuromemristive system and show how it improves the plasticity for unsupervised on-device learning. High-level modeling shows the efficacy of synaptic normalization in pattern recognition and feature extraction.