Adjustable Leaky-Integrate-and-fire neurons based on memristor-coupled capacitors

To address the von Neumann bottleneck, artificial neural networks (ANNs) are aroused to construct neuromorphic computing systems. The artificial neuron is one of the essential components that collect the weight updating information of artificial synapses. Leaky-Integrate-and-Fire (LIF) neuron mimick...

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Autores principales:	T. Guo, K. Pan, B. Sun, L. Wei, Y. Yan, Y.N. Zhou, Y.A. Wu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Neuromorphic computing Artificial neuron Memristor Capacitor Genetic algorithm Materials of engineering and construction. Mechanics of materials TA401-492
Acceso en línea:	https://doaj.org/article/db77a01098cb4bc8966c8d206a68eefb
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id	oai:doaj.org-article:db77a01098cb4bc8966c8d206a68eefb
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spelling	oai:doaj.org-article:db77a01098cb4bc8966c8d206a68eefb2021-12-02T05:03:35ZAdjustable Leaky-Integrate-and-fire neurons based on memristor-coupled capacitors2590-049810.1016/j.mtadv.2021.100192https://doaj.org/article/db77a01098cb4bc8966c8d206a68eefb2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S259004982100062Xhttps://doaj.org/toc/2590-0498To address the von Neumann bottleneck, artificial neural networks (ANNs) are aroused to construct neuromorphic computing systems. The artificial neuron is one of the essential components that collect the weight updating information of artificial synapses. Leaky-Integrate-and-Fire (LIF) neuron mimicking the cell membrane of biological neurons is a promising neural model due to its simplicity. To adjust the performances of artificial neurons, multiple resistors with different resistive values need to be integrated into the circuit. Whereas more components mean higher manufacturing costs, more complex circuits, and more complicated control systems. In this work, the first adjustable LIF neuron was developed, which can further simplify the circuits. To achieve adjustable fashions, a memristor-coupled capacitor with binary intrinsic resistant states was employed to integrate input signals. The intrinsic tunable resistance can modify the charge leaking rate, which determines the neural spiking features. Another contribution of this work is to overcome the hinder of credible circuit design using novel memristor-coupled capacitors with entangled capacitive and memristive effects. The genetic algorithm (GA) was utilized to detach the entanglement of memristive and capacitive effects, which is crucial for circuit design. This method can be generalized to other entangled physical behaviors, facilitating the development of novel circuits. The results will not only strengthen neuromorphic computing capability but also provides a methodology to mathematically decode electronic devices with entangled physical behaviors for novel circuits.T. GuoK. PanB. SunL. WeiY. YanY.N. ZhouY.A. WuElsevierarticleNeuromorphic computingArtificial neuronMemristorCapacitorGenetic algorithmMaterials of engineering and construction. Mechanics of materialsTA401-492ENMaterials Today Advances, Vol 12, Iss , Pp 100192- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neuromorphic computing Artificial neuron Memristor Capacitor Genetic algorithm Materials of engineering and construction. Mechanics of materials TA401-492
spellingShingle	Neuromorphic computing Artificial neuron Memristor Capacitor Genetic algorithm Materials of engineering and construction. Mechanics of materials TA401-492 T. Guo K. Pan B. Sun L. Wei Y. Yan Y.N. Zhou Y.A. Wu Adjustable Leaky-Integrate-and-fire neurons based on memristor-coupled capacitors
description	To address the von Neumann bottleneck, artificial neural networks (ANNs) are aroused to construct neuromorphic computing systems. The artificial neuron is one of the essential components that collect the weight updating information of artificial synapses. Leaky-Integrate-and-Fire (LIF) neuron mimicking the cell membrane of biological neurons is a promising neural model due to its simplicity. To adjust the performances of artificial neurons, multiple resistors with different resistive values need to be integrated into the circuit. Whereas more components mean higher manufacturing costs, more complex circuits, and more complicated control systems. In this work, the first adjustable LIF neuron was developed, which can further simplify the circuits. To achieve adjustable fashions, a memristor-coupled capacitor with binary intrinsic resistant states was employed to integrate input signals. The intrinsic tunable resistance can modify the charge leaking rate, which determines the neural spiking features. Another contribution of this work is to overcome the hinder of credible circuit design using novel memristor-coupled capacitors with entangled capacitive and memristive effects. The genetic algorithm (GA) was utilized to detach the entanglement of memristive and capacitive effects, which is crucial for circuit design. This method can be generalized to other entangled physical behaviors, facilitating the development of novel circuits. The results will not only strengthen neuromorphic computing capability but also provides a methodology to mathematically decode electronic devices with entangled physical behaviors for novel circuits.
format	article
author	T. Guo K. Pan B. Sun L. Wei Y. Yan Y.N. Zhou Y.A. Wu
author_facet	T. Guo K. Pan B. Sun L. Wei Y. Yan Y.N. Zhou Y.A. Wu
author_sort	T. Guo
title	Adjustable Leaky-Integrate-and-fire neurons based on memristor-coupled capacitors
title_short	Adjustable Leaky-Integrate-and-fire neurons based on memristor-coupled capacitors
title_full	Adjustable Leaky-Integrate-and-fire neurons based on memristor-coupled capacitors
title_fullStr	Adjustable Leaky-Integrate-and-fire neurons based on memristor-coupled capacitors
title_full_unstemmed	Adjustable Leaky-Integrate-and-fire neurons based on memristor-coupled capacitors
title_sort	adjustable leaky-integrate-and-fire neurons based on memristor-coupled capacitors
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/db77a01098cb4bc8966c8d206a68eefb
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Adjustable Leaky-Integrate-and-fire neurons based on memristor-coupled capacitors

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