Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity.

Cortical circuits in the brain have long been recognised for their information processing capabilities and have been studied both experimentally and theoretically via spiking neural networks. Neuromorphic engineers are primarily concerned with translating the computational capabilities of biological...

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Autores principales:	Mostafa Rahimi Azghadi, Nicolangelo Iannella, Said Al-Sarawi, Derek Abbott
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6350b8ba797e4225bb68252a900552f0
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spelling	oai:doaj.org-article:6350b8ba797e4225bb68252a900552f02021-11-18T08:32:40ZTunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity.1932-620310.1371/journal.pone.0088326https://doaj.org/article/6350b8ba797e4225bb68252a900552f02014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24551089/?tool=EBIhttps://doaj.org/toc/1932-6203Cortical circuits in the brain have long been recognised for their information processing capabilities and have been studied both experimentally and theoretically via spiking neural networks. Neuromorphic engineers are primarily concerned with translating the computational capabilities of biological cortical circuits, using the Spiking Neural Network (SNN) paradigm, into in silico applications that can mimic the behaviour and capabilities of real biological circuits/systems. These capabilities include low power consumption, compactness, and relevant dynamics. In this paper, we propose a new accelerated-time circuit that has several advantages over its previous neuromorphic counterparts in terms of compactness, power consumption, and capability to mimic the outcomes of biological experiments. The presented circuit simulation results demonstrate that, in comparing the new circuit to previous published synaptic plasticity circuits, reduced silicon area and lower energy consumption for processing each spike is achieved. In addition, it can be tuned in order to closely mimic the outcomes of various spike timing- and rate-based synaptic plasticity experiments. The proposed circuit is also investigated and compared to other designs in terms of tolerance to mismatch and process variation. Monte Carlo simulation results show that the proposed design is much more stable than its previous counterparts in terms of vulnerability to transistor mismatch, which is a significant challenge in analog neuromorphic design. All these features make the proposed design an ideal circuit for use in large scale SNNs, which aim at implementing neuromorphic systems with an inherent capability that can adapt to a continuously changing environment, thus leading to systems with significant learning and computational abilities.Mostafa Rahimi AzghadiNicolangelo IannellaNicolangelo IannellaSaid Al-SarawiDerek AbbottPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 2, p e88326 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mostafa Rahimi Azghadi Nicolangelo Iannella Nicolangelo Iannella Said Al-Sarawi Derek Abbott Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity.
description	Cortical circuits in the brain have long been recognised for their information processing capabilities and have been studied both experimentally and theoretically via spiking neural networks. Neuromorphic engineers are primarily concerned with translating the computational capabilities of biological cortical circuits, using the Spiking Neural Network (SNN) paradigm, into in silico applications that can mimic the behaviour and capabilities of real biological circuits/systems. These capabilities include low power consumption, compactness, and relevant dynamics. In this paper, we propose a new accelerated-time circuit that has several advantages over its previous neuromorphic counterparts in terms of compactness, power consumption, and capability to mimic the outcomes of biological experiments. The presented circuit simulation results demonstrate that, in comparing the new circuit to previous published synaptic plasticity circuits, reduced silicon area and lower energy consumption for processing each spike is achieved. In addition, it can be tuned in order to closely mimic the outcomes of various spike timing- and rate-based synaptic plasticity experiments. The proposed circuit is also investigated and compared to other designs in terms of tolerance to mismatch and process variation. Monte Carlo simulation results show that the proposed design is much more stable than its previous counterparts in terms of vulnerability to transistor mismatch, which is a significant challenge in analog neuromorphic design. All these features make the proposed design an ideal circuit for use in large scale SNNs, which aim at implementing neuromorphic systems with an inherent capability that can adapt to a continuously changing environment, thus leading to systems with significant learning and computational abilities.
format	article
author	Mostafa Rahimi Azghadi Nicolangelo Iannella Nicolangelo Iannella Said Al-Sarawi Derek Abbott
author_facet	Mostafa Rahimi Azghadi Nicolangelo Iannella Nicolangelo Iannella Said Al-Sarawi Derek Abbott
author_sort	Mostafa Rahimi Azghadi
title	Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity.
title_short	Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity.
title_full	Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity.
title_fullStr	Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity.
title_full_unstemmed	Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity.
title_sort	tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/6350b8ba797e4225bb68252a900552f0
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Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity.

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