Modeling triplet spike-timing-dependent plasticity using memristive devices

Aghnout, Soraya, Karimi, Gholamreza, and Rahimiazghadi, Mostafa (2017) Modeling triplet spike-timing-dependent plasticity using memristive devices. Journal of Computational Electronics, 16 (2). pp. 401-410.

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Abstract

Triplet-based spike-timing-dependent plasticity (TSTDP) is an advanced synaptic plasticity rule that results in improved learning capability compared to the conventional pair-based STDP (PSTDP). The TSTDP rule can reproduce the results of many electrophysiological experiments, where the PSTDP fails. This paper proposes a novel memristive circuit that implements the TSTDP rule. The proposed circuit is designed using three voltage (flux)-driven memristors. Simulation results demonstrate that our memristive circuit induces synaptic weight changes that arise due to the timing differences among pairs and triplets of spikes. The presented memristive design is an initial step toward developing asynchronous TSTDP learning architectures using memristive devices. These architectures may facilitate the implementation of advanced large-scale neuromorphic systems with applications in real-world engineering tasks such as pattern classification.

Item ID: 47975
Item Type: Article (Research - C1)
ISSN: 1572-8137
Keywords: memristor; synapse; spike; spike-timing-dependent plasticity (STDP)
Date Deposited: 24 Mar 2017 05:36
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 25%
10 TECHNOLOGY > 1007 Nanotechnology > 100705 Nanoelectronics @ 50%
09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090604 Microelectronics and Integrated Circuits @ 25%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970110 Expanding Knowledge in Technology @ 25%
97 EXPANDING KNOWLEDGE > 970108 Expanding Knowledge in the Information and Computing Sciences @ 50%
97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 25%
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