Synaptic Plasticity in Memristive Artificial Synapses and Their Robustness Against Noisy Inputs.

Nan Du,Xianyue Zhao,Heidemarie Schmidt,Ziang Chen,Bhaskar Choubey,Danilo Bürger,Massimiliano Di Ventra,Ilona Skorupa

doi:10.3389/fnins.2021.660894

Abstract

Emerging brain-inspired neuromorphic computing paradigms require devices that can emulate the complete functionality of biological synapses upon different neuronal activities in order to process big data flows in an efficient and cognitive manner while being robust against any noisy input. The memristive device has been proposed as a promising candidate for emulating artificial synapses due to their complex multilevel and dynamical plastic behaviors. In this work, we exploit ultrastable analog BiFeO3 (BFO)-based memristive devices for experimentally demonstrating that BFO artificial synapses support various long-term plastic functions, i.e., spike timing-dependent plasticity (STDP), cycle number-dependent plasticity (CNDP), and spiking rate-dependent plasticity (SRDP). The study on the impact of electrical stimuli in terms of pulse width and amplitude on STDP behaviors shows that their learning windows possess a wide range of timescale configurability, which can be a function of applied waveform. Moreover, beyond SRDP, the systematical and comparative study on generalized frequency-dependent plasticity (FDP) is carried out, which reveals for the first time that the ratio modulation between pulse width and pulse interval time within one spike cycle can result in both synaptic potentiation and depression effect within the same firing frequency. The impact of intrinsic neuronal noise on the STDP function of a single BFO artificial synapse can be neglected because thermal noise is two orders of magnitude smaller than the writing voltage and because the cycle-to-cycle variation of the current–voltage characteristics of a single BFO artificial synapses is small. However, extrinsic voltage fluctuations, e.g., in neural networks, cause a noisy input into the artificial synapses of the neural network. Here, the impact of extrinsic neuronal noise on the STDP function of a single BFO artificial synapse is analyzed in order to understand the robustness of plastic behavior in memristive artificial synapses against extrinsic noisy input.

Highlights

The human brain can be considered as an advanced information storage and computation platform, capable of processing large volumes of real-time data in a massively parallel, fault-tolerant, and adaptive manner with extremely low energy consumption of ∼10 W (Townsley et al, 2020)
The normalized long-term plasticity (LTP) current ILTP and longterm depression (LTD) current ILTD are plotted against the spike timing differences from | t| = tp up to | t| = 10∗tp
At | t| = tp, the ILTP/ ILTD is dramatically depressed at decreased spike amplitudes of Vp = 3 and 2 V

Summary

Introduction

The human brain can be considered as an advanced information storage and computation platform, capable of processing large volumes of real-time data in a massively parallel, fault-tolerant, and adaptive manner with extremely low energy consumption of ∼10 W (Townsley et al, 2020). A memristive device intrinsically provides electrically tunable conductance, i.e., it enables updating of its conductance (artificial synaptic weight), upon electrical stimuli (neuronal activity), and demonstrates stable resistive states within its dynamic range (analog behavior) (Zhang et al, 2019; Huang et al, 2021). Such memristive artificial synapses show significant energy savings over traditional computing which involves separate processing of information and storage into separate memory. In most of these works, the neuromorphic devices are exploited to emulate one of the synaptic plastic behaviors, i.e., spike timing-dependent plasticity (STDP), cycle number-dependent plasticity (CNDP), spiking rate-dependent plasticity (SRDP), or long-term plasticity (LTP)/short-term plasticity (STP), and metaplasticity (Pedretti et al, 2017; Zang et al, 2017; John et al, 2018; Xu W.T. et al, 2018; Zhong et al, 2018; Guo et al, 2019; Kiani et al, 2019)

Methods

Results

Discussion

Conclusion