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Implementation of a spike-based perceptron learning rule using TiO2-x memristors

Implementation of a spike-based perceptron learning rule using TiO2-x memristors
Implementation of a spike-based perceptron learning rule using TiO2-x memristors
Synaptic plasticity plays a crucial role in allowing neural networks to learn and adapt to various input environments. Neuromorphic systems need to implement plastic synapses to obtain basic “cognitive” capabilities such as learning. One promising and scalable approach for implementing neuromorphic synapses is to use nano-scale memristors as synaptic elements. In this paper we propose a hybrid CMOS-memristor system comprising CMOS neurons interconnected through TiO2-x memristors, and spike-based learning circuits that modulate the conductance of the memristive synapse elements according to a spike-based Perceptron plasticity rule. We highlight a number of advantages for using this spike-based plasticity rule as compared to other forms of spike timing dependent plasticity (STDP) rules. We provide experimental proof-of-concept results with two silicon neurons connected through a memristive synapse that show how the CMOS plasticity circuits can induce stable changes in memristor conductances, giving rise to increased synaptic strength after a potentiation episode and to decreased strength after a depression episode
1662-4548
1-11
Mostafa, H.
a716024d-2a2b-4171-a5eb-40d9d22728b6
Khiat, A.
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Serb, Alexander
30f5ec26-f51d-42b3-85fd-0325a27a792c
Mayr, C.
957c7a5d-12f4-473f-8757-87fff6e7eeaa
Indiveri, G.
fbcc86fb-e8a6-44cc-94b4-501532a32ff9
Prodromakis, T.
d58c9c10-9d25-4d22-b155-06c8437acfbf
Mostafa, H.
a716024d-2a2b-4171-a5eb-40d9d22728b6
Khiat, A.
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Serb, Alexander
30f5ec26-f51d-42b3-85fd-0325a27a792c
Mayr, C.
957c7a5d-12f4-473f-8757-87fff6e7eeaa
Indiveri, G.
fbcc86fb-e8a6-44cc-94b4-501532a32ff9
Prodromakis, T.
d58c9c10-9d25-4d22-b155-06c8437acfbf

Mostafa, H., Khiat, A., Serb, Alexander, Mayr, C., Indiveri, G. and Prodromakis, T. (2015) Implementation of a spike-based perceptron learning rule using TiO2-x memristors. Frontiers in Neuroscience, 9 (357), 1-11. (doi:10.3389/fnins.2015.00357).

Record type: Article

Abstract

Synaptic plasticity plays a crucial role in allowing neural networks to learn and adapt to various input environments. Neuromorphic systems need to implement plastic synapses to obtain basic “cognitive” capabilities such as learning. One promising and scalable approach for implementing neuromorphic synapses is to use nano-scale memristors as synaptic elements. In this paper we propose a hybrid CMOS-memristor system comprising CMOS neurons interconnected through TiO2-x memristors, and spike-based learning circuits that modulate the conductance of the memristive synapse elements according to a spike-based Perceptron plasticity rule. We highlight a number of advantages for using this spike-based plasticity rule as compared to other forms of spike timing dependent plasticity (STDP) rules. We provide experimental proof-of-concept results with two silicon neurons connected through a memristive synapse that show how the CMOS plasticity circuits can induce stable changes in memristor conductances, giving rise to increased synaptic strength after a potentiation episode and to decreased strength after a depression episode

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Accepted/In Press date: 18 September 2015
Published date: 2 October 2015
Organisations: Nanoelectronics and Nanotechnology

Identifiers

Local EPrints ID: 377545
URI: https://eprints.soton.ac.uk/id/eprint/377545
ISSN: 1662-4548
PURE UUID: 4f2facb6-698e-45c4-ac40-d125d66d431f
ORCID for T. Prodromakis: ORCID iD orcid.org/0000-0002-6267-6909

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Date deposited: 09 Jun 2015 10:36
Last modified: 27 Jun 2018 00:29

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Contributors

Author: H. Mostafa
Author: A. Khiat
Author: Alexander Serb
Author: C. Mayr
Author: G. Indiveri
Author: T. Prodromakis ORCID iD

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