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Spike timing dependent plasticity with memristive synapse in neuromorphic systems

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2 Author(s)
William Chan ; Department of Electrical and Computer Engineering, Carnegie Mellon University, NASA Research Park, Moffett Field, California 94043, USA ; Jason Lohn

A methodology to realize spike-timing dependent plasticity and Hebbian learning in a neural network through the usage of memristive synapses is presented. Memristors act as a modulating synapse interconnection between neurons; plasticity is accomplished through adjusting the memristance via current spikes based on the relative timings of pre-synaptic and post-synaptic neuron spikes. The learning plasticity presented is continuous, asynchronous and deterministic. A CMOS implementation is presented along with SPICE simulations validating the methodology and design.

Published in:

The 2012 International Joint Conference on Neural Networks (IJCNN)

Date of Conference:

10-15 June 2012