Journal of Computational Electronics

, Volume 16, Issue 2, pp 401–410

Modeling triplet spike-timing-dependent plasticity using memristive devices

  • Soraya Aghnout
  • Gholamreza Karimi
  • Mostafa Rahimi Azghadi
Article
  • 142 Downloads

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.

Keywords

Memristor Synapse Spike Spike-timing-dependent plasticity (STDP) 

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Soraya Aghnout
    • 1
  • Gholamreza Karimi
    • 1
  • Mostafa Rahimi Azghadi
    • 2
    • 3
  1. 1.Electrical Engineering Department, Engineering FacultyRazi UniversityKermanshahIran
  2. 2.School of Electrical and Information EngineeringThe University of SydneySydneyAustralia
  3. 3.College of Science and EngineeringJames Cook UniversityTownsvilleAustralia

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