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STDP and STDP variations with memristors for spiking neuromorphic learning systems

STDP and STDP variations with memristors for spiking neuromorphic learning systems
STDP and STDP variations with memristors for spiking neuromorphic learning systems
In this paper we review several ways of realizing asynchronous Spike-Timing-Dependent-Plasticity (STDP) using memristors as synapses. Our focus is on how to use individual memristors to implement synaptic weight multiplications, in a way such that it is not necessary to (a) introduce global synchronization and (b) to separate memristor learning phases from memristor performing phases. In the approaches described, neurons fire spikes asynchronously when they wish and memristive synapses perform computation and learn at their own pace, as it happens in biological neural systems. We distinguish between two different memristor physics, depending on whether they respond to the original “moving wall” or to the “filament creation and annihilation” models. Independent of the memristor physics, we discuss two different types of STDP rules that can be implemented with memristors: either the pure timing-based rule that takes into account the arrival time of the spikes from the pre- and the post-synaptic neurons, or a hybrid rule that takes into account only the timing of pre-synaptic spikes and the membrane potential and other state variables of the post-synaptic neuron. We show how to implement these rules in cross-bar architectures that comprise massive arrays of memristors, and we discuss applications for artificial vision.
memristor/cmos, artificial-learning-synapses, spike-timing-dependent-plasticity, spiking-neural-networks
1662-4548
1-15
Serrano-Gotarredona, T.
8a2751a4-8752-46e3-8a64-1e82eeb84f1f
Masquelier, T.
0e9b3fac-4de9-476c-9e0e-fb0be8a11664
Prodromakis, T.
d58c9c10-9d25-4d22-b155-06c8437acfbf
Indiveri, G.
fbcc86fb-e8a6-44cc-94b4-501532a32ff9
Linares-Barranco, B.
3f44925c-96cb-48ac-a83c-a781e264bd3c
Serrano-Gotarredona, T.
8a2751a4-8752-46e3-8a64-1e82eeb84f1f
Masquelier, T.
0e9b3fac-4de9-476c-9e0e-fb0be8a11664
Prodromakis, T.
d58c9c10-9d25-4d22-b155-06c8437acfbf
Indiveri, G.
fbcc86fb-e8a6-44cc-94b4-501532a32ff9
Linares-Barranco, B.
3f44925c-96cb-48ac-a83c-a781e264bd3c

Serrano-Gotarredona, T., Masquelier, T., Prodromakis, T., Indiveri, G. and Linares-Barranco, B. (2013) STDP and STDP variations with memristors for spiking neuromorphic learning systems. Frontiers in Neuroscience, 7 (2), 1-15. (doi:10.3389/fnins.2013.00002).

Record type: Article

Abstract

In this paper we review several ways of realizing asynchronous Spike-Timing-Dependent-Plasticity (STDP) using memristors as synapses. Our focus is on how to use individual memristors to implement synaptic weight multiplications, in a way such that it is not necessary to (a) introduce global synchronization and (b) to separate memristor learning phases from memristor performing phases. In the approaches described, neurons fire spikes asynchronously when they wish and memristive synapses perform computation and learn at their own pace, as it happens in biological neural systems. We distinguish between two different memristor physics, depending on whether they respond to the original “moving wall” or to the “filament creation and annihilation” models. Independent of the memristor physics, we discuss two different types of STDP rules that can be implemented with memristors: either the pure timing-based rule that takes into account the arrival time of the spikes from the pre- and the post-synaptic neurons, or a hybrid rule that takes into account only the timing of pre-synaptic spikes and the membrane potential and other state variables of the post-synaptic neuron. We show how to implement these rules in cross-bar architectures that comprise massive arrays of memristors, and we discuss applications for artificial vision.

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More information

Published date: 18 February 2013
Keywords: memristor/cmos, artificial-learning-synapses, spike-timing-dependent-plasticity, spiking-neural-networks
Organisations: Nanoelectronics and Nanotechnology

Identifiers

Local EPrints ID: 351557
URI: https://eprints.soton.ac.uk/id/eprint/351557
ISSN: 1662-4548
PURE UUID: 52dadcb2-5317-4a14-8b9b-d12b962aed72

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Date deposited: 26 Apr 2013 11:39
Last modified: 18 Jul 2017 04:25

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Contributors

Author: T. Serrano-Gotarredona
Author: T. Masquelier
Author: T. Prodromakis
Author: G. Indiveri
Author: B. Linares-Barranco

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