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

Frontiers in Neuroscience

Massimiliano Di Ventra¹  Danilo Bürger³  Ilona Skorupa⁴  Heidemarie Schmidt⁵  Xianyue Zhao⁶  Ziang Chen⁶  Bhaskar Choubey⁷  Nan Du⁸ 

[1] Systems, ATTRACT Group Microelectronic Intelligence, Duisburg, Germany;Analogue Circuits and Image Sensors, Universität Siegen, Siegen, Germany;Department Nano Device Technology, Fraunhofer Institute for Electronic Nano Systems, Chemnitz, Germany;Department of Physics, University of California, San Diego, La Jolla, CA, United States;Department of Quantum Detection, Leibniz Institute of Photonic Technology, Jena, Germany;Faculty of Electrical Engineering and Information Technology, Chemnitz University of Technology, Chemnitz, Germany;;Fraunhofer Institute of Microelectronics Circuits &Institute for Solid State Physics, Friedrich Schiller University Jena, Jena, Germany;Institute of Ion Beam Physics and Materials Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany;

关键词: artificial synapse;    resistive switching;    synaptic plasticity;    neuronal noise;    spike-timing dependent plasticity;    cycle-number dependent plasticity;   

DOI  :  10.3389/fnins.2021.660894

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown