Spiking flip-flop memory in resonant tunnelling diode neurons

We report a spiking flip-flop memory mechanism allowing to controllably switch between neural-like excitable spike-firing and quiescent dynamics in a resonant tunneling diode (RTD) neuron under low-amplitude (<150 mV pulses) and high-speed (ns-rates) 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 potentials to extend 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 (AI) hardware.

Publication Details

Journal
Strathprints: The University of Strathclyde institutional repository (University of Strathclyde)
Published
2024-12-24
Primary Topic
Magnetic confinement fusion research
Type
article
Field-Weighted Citation Impact
0.00

Funders

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article

Spiking flip-flop memory in resonant tunnelling diode neurons

Strathprints: The University of Strathclyde institutional repository (University of Strathclyde)
Magnetic confinement fusion research
article

Spiking flip-flop memory in resonant tunnelling diode neurons

article en

Abstract

We report a spiking flip-flop memory mechanism allowing to controllably switch between neural-like excitable spike-firing and quiescent dynamics in a resonant tunneling diode (RTD) neuron under low-amplitude (<150 mV pulses) and high-speed (ns-rates) 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 potentials to extend 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 (AI) hardware.

Strathprints: The University of Strathclyde institutional repository (University of Strathclyde)
UK Research and Innovation, Fundação para a Ciência e a Tecnologia
Affordable and clean energy
Openalex Percentile: Top 35%
Magnetic confinement fusion research
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