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Spike-timing-dependent learning in memristive nanodevices

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1 Author(s)
Snider, G.S. ; Inf. & Quantum Syst. Lab., Palo Alto, CA

The neuromorphic paradigm is attractive for nanoscale computation because of its massive parallelism, potential scalability, and inherent defect-, fault-, and failure-tolerance. We show how to implement timing-based learning laws, such as spike-timing-dependent plasticity (STDP), in simple, memristive nanodevices, such as those constructed from certain metal oxides. Such nano-scale ldquosynapsesrdquo can be combined with CMOS ldquoneuronsrdquo to create neuromorphic hardware several orders of magnitude denser than is possible in conventional CMOS. The key ideas are: (1) to factor out two synaptic state variables to pre- and post-synaptic neurons; and (2) to separate computational communication from learning by time-division multiplexing of pulse-width-modulated signals through synapses. This approach offers the advantages of: better control over power dissipation; fewer constraints on the design of memristive materials used for nanoscale synapses; learning dynamics can be dynamically turned on or off (e.g. by attentional priming mechanisms communicated extra-synaptically); greater control over the precise form and timing of the STDP equations; the ability to implement a variety of other learning laws besides STDP; better circuit diversity since the approach allows different learning laws to be implemented in different areas of a single chip using the same memristive material for all synapses.

Published in:

Nanoscale Architectures, 2008. NANOARCH 2008. IEEE International Symposium on

Date of Conference:

12-13 June 2008

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