Spiking Flip-Flop Memory in Resonant Tunneling Diode Neurons

IF 9 1区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY Physical review letters Pub Date : 2024-12-24 DOI:10.1103/physrevlett.133.267301
Giovanni Donati, Dafydd Owen-Newns, Joshua Robertson, Ekaterina Malysheva, Andrew Adair, Jose Figueiredo, Bruno Romeira, Victor Dolores-Calzadilla, Antonio Hurtado
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Abstract

We report a spiking flip-flop memory mechanism that allows controllably switching between neural-like excitable spike-firing and quiescent dynamics in a resonant tunneling diode (RTD) neuron under low-amplitude (<150mV pulses) and high-speed (ns rate) inputs pulses. We also show that the timing of the set-reset input pulses is critical to elicit switching responses between spiking and quiescent regimes in the system. The demonstrated flip-flop spiking memory, in which spiking regimes can be controllably excited, stored, and inhibited in RTD neurons via specific low-amplitude, high-speed signals (delivered at proper time instants) offers high promise for RTD-based spiking neural networks, with the potential to be extended further to optoelectronic implementations where RTD neurons and RTD memory elements are deployed alongside for fast and efficient photonic-electronic neuromorphic computing and artificial intelligence hardware. Published by the American Physical Society 2024
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共振隧道二极管神经元的脉冲触发器记忆
我们报道了一种脉冲触发器记忆机制,该机制允许在低振幅(150mV脉冲)和高速(ns速率)输入脉冲下,在共振隧道二极管(RTD)神经元中,在神经样可激发的脉冲放电和静态动态之间进行可控切换。我们还表明,设置-复位输入脉冲的时间对于引发系统中尖峰和静态状态之间的切换响应至关重要。通过特定的低振幅、高速信号(在适当的时间瞬间传递),可以在RTD神经元中可控地激发、存储和抑制脉冲机制,这为基于RTD的脉冲神经网络提供了很大的希望。有可能进一步扩展到光电子实现,其中RTD神经元和RTD存储元件与快速高效的光电子神经形态计算和人工智能硬件一起部署。2024年由美国物理学会出版
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来源期刊
Physical review letters
Physical review letters 物理-物理:综合
CiteScore
16.50
自引率
7.00%
发文量
2673
审稿时长
2.2 months
期刊介绍: Physical review letters(PRL)covers the full range of applied, fundamental, and interdisciplinary physics research topics: General physics, including statistical and quantum mechanics and quantum information Gravitation, astrophysics, and cosmology Elementary particles and fields Nuclear physics Atomic, molecular, and optical physics Nonlinear dynamics, fluid dynamics, and classical optics Plasma and beam physics Condensed matter and materials physics Polymers, soft matter, biological, climate and interdisciplinary physics, including networks
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