Rahimi Azghadi, Mostafa, Al-Sarawi, Said, Iannella, Nicolangelo, and Abbott, Derek (2012) Efficient design of triplet based spike-timing dependent plasticity. In: Proceedings of the 2012 International Joint Conference on Neural Networks. pp. 1-7. From: 2012 International Joint Conference on Neural Networks (IJCNN), 10-15 June 2012, Brisbane, QLD, Australia.

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Abstract

Spike-Timing Dependent Plasticity (STDP) is believed to play an important role in learning and the formation of computational function in the brain. The classical model of STDP which considers the timing between pairs of pre-synaptic and post-synaptic spikes (p-STDP) is incapable of reproducing synaptic weight changes similar to those seen in biological experiments which investigate the effect of either higher order spike trains (e.g. triplet and quadruplet of spikes) [1]-[3], or, simultaneous effect of the rate and timing of spike pairs [4] on synaptic plasticity. In this paper, we firstly investigate synaptic weight changes using a p-STDP circuit [5] and show how it fails to reproduce the mentioned complex biological experiments. We then present a new STDP VLSI circuit which acts based on the timing among triplets of spikes (t-STDP) that is able to reproduce all the mentioned experimental results. We believe that our new STDP VLSI circuit improves upon previous circuits, whose learning capacity exceeds current designs due to its capability of mimicking the outcomes of biological experiments more closely; thus plays a significant role in future VLSI implementation of neuromorphic systems.

Item ID:	45725
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-4673-1489-3
Funders:	Australian Research Council (ARC)
Date Deposited:	29 Aug 2017 02:09
FoR Codes:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 50% 09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090604 Microelectronics and Integrated Circuits @ 50%
SEO Codes:	97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 100%
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