Journal Article

FZJ-2019-01067

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors

Yang, R. (Corresponding author) ; Huang, H.-M. ; Hong, Q.-H. ; Yin, X.-B. ; Tan, Z.-H. ; Shi, T. ; Zhou, Y.-X. ; Miao, X.-S. ; Wang, X.-P. ; Mi, S.-B. ; Jia, C.-L.FZJ* ; Guo, X.

2018
Wiley-VCH Weinheim

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Please use a persistent id in citations: doi:10.1002/adfm.201704455

Abstract: The synaptic weight modification depends not only on interval of the pre‐/postspike pairs according to spike‐timing dependent plasticity (classical pair‐STDP), but also on the timing of the preceding spike (triplet‐STDP). Triplet‐STDP reflects the unavoidable interaction of spike pairs in natural spike trains through the short‐term suppression effect of preceding spikes. Second‐order memristors with one state variable possessing short‐term dynamics work in a way similar to the biological system. In this work, the suppression triplet‐STDP learning rule is faithfully demonstrated by experiments and simulations using second‐order memristors. Furthermore, a leaky‐integrate‐and‐fire (LIF) neuron is simulated using a circuit constructed with second‐order memristors. Taking the advantage of the LIF neuron, various neuromimetic dynamic processes, including local graded potential leaking out, postsynaptic impulse generation and backpropagation, and synaptic weight modification according to the suppression triplet‐STDP rule, are realized. The realized weight‐dependent pair‐ and triplet‐STDP rules are clearly in line with findings in biology. The physically realized triplet‐STDP rule is powerful in developing direction and speed selectivity for complex pattern recognition and tracking tasks. These scalable artificial synapses and neurons realized in second‐order memristors can intrinsically capture the neuromimetic dynamic processes; they are the promising building blocks for constructing brain‐inspired computation systems.

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Contributing Institute(s):

1. Physik Nanoskaliger Systeme (ER-C-1)

Research Program(s):

1. 143 - Controlling Configuration-Based Phenomena (POF3-143) (POF3-143)

Appears in the scientific report 2018

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; IF >= 10 ; JCR ; NCBI Molecular Biology Database ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Web of Science Core Collection

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