Lammie, Corey, and Rahimiazghadi, Mostafa (2017) Digital, analog, and memristive implementation of Spike-based Synaptic Plasticity. In: Abstracts from the Systems & Computational Neuroscience Down Under Meeting. p. 46. From: SCiNDU: Systems & Computational Neuroscience Down Under, 13-15 December 2017, Brisbane, QLD, Australia.

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Abstract

Synaptic platicity is believed to play an essential role in learning and memory in the brain. To date, many plasticity algorithms have been devised, some of which confirmed in electrophysiological experiments. Perhaps the most popular synaptic platicity rule, or learning algorithm, among neuromorphic engineers is the Spike Timing Dependent Plasticity (STDP). The conventional form of STDP has been implemented in various forms by many groups and using different hardware approaches. It has been used for applications such as pattern classification. Hoever, a newer form of STDP, which elicits synaptic efficacy modification based on the timing among a triplet of pre- and post-synaptic spikes, has not been well explored in hardware.

Item ID:	52725
Item Type:	Conference Item (Abstract / Summary)
Date Deposited:	01 Mar 2018 01:38
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