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An ultra-compact leaky-integrate-and-fire model for building spiking neural networks

We introduce an ultra-compact electronic circuit that realizes the leaky-integrate-and-fire model of artificial neurons. Our circuit has only three active devices, two transistors and a silicon controlled rectifier (SCR). We demonstrate the implementation of biologically realistic features, such as...

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Detalles Bibliográficos
Autores principales: Rozenberg, M. J., Schneegans, O., Stoliar, P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6668387/
https://www.ncbi.nlm.nih.gov/pubmed/31366958
http://dx.doi.org/10.1038/s41598-019-47348-5
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author Rozenberg, M. J.
Schneegans, O.
Stoliar, P.
author_facet Rozenberg, M. J.
Schneegans, O.
Stoliar, P.
author_sort Rozenberg, M. J.
collection PubMed
description We introduce an ultra-compact electronic circuit that realizes the leaky-integrate-and-fire model of artificial neurons. Our circuit has only three active devices, two transistors and a silicon controlled rectifier (SCR). We demonstrate the implementation of biologically realistic features, such as spike-frequency adaptation, a refractory period and voltage modulation of spiking rate. All characteristic times can be controlled by the resistive parameters of the circuit. We built the circuit with out-of-the-shelf components and demonstrate that our ultra-compact neuron is a modular block that can be associated to build multi-layer deep neural networks. We also argue that our circuit has low power requirements, as it is normally off except during spike generation. Finally, we discuss the ultimate ultra-compact limit, which may be achieved by further replacing the SCR circuit with Mott materials.
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spelling pubmed-66683872019-08-06 An ultra-compact leaky-integrate-and-fire model for building spiking neural networks Rozenberg, M. J. Schneegans, O. Stoliar, P. Sci Rep Article We introduce an ultra-compact electronic circuit that realizes the leaky-integrate-and-fire model of artificial neurons. Our circuit has only three active devices, two transistors and a silicon controlled rectifier (SCR). We demonstrate the implementation of biologically realistic features, such as spike-frequency adaptation, a refractory period and voltage modulation of spiking rate. All characteristic times can be controlled by the resistive parameters of the circuit. We built the circuit with out-of-the-shelf components and demonstrate that our ultra-compact neuron is a modular block that can be associated to build multi-layer deep neural networks. We also argue that our circuit has low power requirements, as it is normally off except during spike generation. Finally, we discuss the ultimate ultra-compact limit, which may be achieved by further replacing the SCR circuit with Mott materials. Nature Publishing Group UK 2019-07-31 /pmc/articles/PMC6668387/ /pubmed/31366958 http://dx.doi.org/10.1038/s41598-019-47348-5 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Rozenberg, M. J.
Schneegans, O.
Stoliar, P.
An ultra-compact leaky-integrate-and-fire model for building spiking neural networks
title An ultra-compact leaky-integrate-and-fire model for building spiking neural networks
title_full An ultra-compact leaky-integrate-and-fire model for building spiking neural networks
title_fullStr An ultra-compact leaky-integrate-and-fire model for building spiking neural networks
title_full_unstemmed An ultra-compact leaky-integrate-and-fire model for building spiking neural networks
title_short An ultra-compact leaky-integrate-and-fire model for building spiking neural networks
title_sort ultra-compact leaky-integrate-and-fire model for building spiking neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6668387/
https://www.ncbi.nlm.nih.gov/pubmed/31366958
http://dx.doi.org/10.1038/s41598-019-47348-5
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