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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

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Engineering Spiking Neurons Using Threshold Switching Devices for High-Efficient Neuromorphic Computing.

Yanting Ding^1,2, Yajun Zhang³, Xumeng Zhang^1,2,4

¹Frontier Institute of Chip and System, Fudan University, Shanghai, China.

Frontiers in Neuroscience

|January 24, 2022

Summary

This summary is machine-generated.

This study explores how circuit parameters influence spiking neurons made from threshold switching (TS) devices. Understanding these factors is key to designing efficient neuromorphic systems.

Keywords:

frequency tunability influence factors reinforcement learning spiking neural network spiking neuron circuits threshold switching devices

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Area of Science:

Neuromorphic Engineering
Materials Science

Background:

Spike-based neuromorphic systems offer high energy efficiency and computational power, inspired by the human brain.
Spiking neurons and plastic synapses are fundamental to these systems.
Two-terminal threshold switching (TS) devices are promising for hardware implementation of spiking neurons.

Purpose of the Study:

To systematically investigate the impact of extrinsic and intrinsic circuit parameters on the spiking behavior of NbO-based TS neurons.
To develop an empirical model for NbO devices to analyze intrinsic factor effects.
To guide the design of efficient neuron circuits for neuromorphic systems.

Main Methods:

Utilized a leaky integrate-and-fire (LIF) neuron circuit model.
Systematically varied extrinsic factors: input intensities, synaptic weights, and parallel capacitances.
Developed and applied an empirical model for NbO devices to analyze intrinsic factors: threshold voltage, holding voltage, and resistance states.

Main Results:

Spiking frequency initially increases with input intensity, then decreases after a peak.
Most parameters, except synaptic weights, can modulate peak spiking frequency under sufficient input.
A relationship between energy consumption per spike and neuron frequency was established.
A spiking neural network (SNN) demonstrated practical application by controlling a cart-pole system with reinforcement learning, achieving a reward score of 450.

Conclusions:

Provides valuable guidance for constructing compact LIF neurons using TS devices.
Bolsters the development of highly efficient neuromorphic systems.
Highlights the importance of parameter tuning for optimizing TS-based neuron performance and energy efficiency.