Volatile Memristor in Leaky Integrate-and-Fire Neurons: Circuit Simulation and Experimental Study

Natasa M Samardzic,Dalibor L Sekulic,Jovan S Bajic,Stanisa Dautovic

doi:10.3390/electronics11060894

Natasa M Samardzic, Dalibor L Sekulic + Show 2 more

Open Access

https://doi.org/10.3390/electronics11060894

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Journal: Electronics	Publication Date: Mar 13, 2022
Citations: 9	License type: CC BY 4.0

Affiliation: University of Novi Sad

Abstract

In this paper, circuit implementation of a leaky integrate-and-fire neuron model with a volatile memristor was proposed and simulated in the SPICE simulation environment. We demonstrate that simple leaky integrate-and-fire (LIF) neuron models composed of: volatile memristor, membrane capacitance and neuron resistance can mimic spatial and temporal integration, firing function and signal decay. The existing leaky term originates from the recovery of the initial resistive state in the memristor in the spontaneous reset cycle, which is essential for emulating the forgetting process in all-memristive neural networks (MNNs). Furthermore, a diffusive perovskite memristor was used to validate the model where intrinsic memristors’ capacitance acts as neuron membrane capacitance. Good agreement with experimental and simulation results was observed. Volatility, as an inherent property of specific memristors, eliminates the need for usage of an additional peripheral circuit which will reinitialize device state, thus allowing the development of energy-efficient, large scale complex memristive neural networks. The presented circuit level model of LIF neurons can facilitate the design of MNNs.

Highlights

The application of memristors as part of artificial neural networks (ANN), especially spiking neural networks (SNN) has been extensively studied in recent years [1–4] due to their unique performances such as: adjustable conductance, multilevel resistance states, fast operation speed, low dissipation, and prominent scaling potential [2,5]
The additional peripheral circuit could be placed after the artificial neuron, in order to distinguish a spike event from an integration period
Improved performances of volatile memristor eliminate the need for a peripheral circuit in leaky integrate-and-fire model (LIF) neuron as presented in [17]

Summary

Introduction

The application of memristors as part of artificial neural networks (ANN), especially spiking neural networks (SNN) has been extensively studied in recent years [1–4] due to their unique performances such as: adjustable conductance, multilevel resistance states, fast operation speed, low dissipation, and prominent scaling potential [2,5]. An all-memristive neural network was presented with a simple memristive LIF neuron circuit and additional membrane capacitance and axon resistance, where leaky functionality was realized using volatility in diffusive memristor, while drift memristor was used in a synaptic circuit composed of one memristor and one transistor (1M-1T) [17]. In a recent study [25], physical models of the filamentary type of volatile resistive switching devices were presented accounting for Ag nanoparticles motion, which defines disruption and formation of the filament This model is numerical and based on Monte Carlo simulations and molecular dynamics, whereas the same group of authors presented the analytical model [26], for volatile memristors confirmed through measurement of ac characteristics and filament disruption dynamics. This circuit model of LIF neuron allows decoupling of memristive and capacitive influence of neuron response, without the usage of additional optimization tools, such as genetic algorithm [15], and can be potentially used for simulation of complex large memristive neural networks

Materials and Methods

Simulation of LIF Neuron with Volatile Memristor

Conclusions