Articolo in rivista, 2018, ENG, 10.1016/j.mee.2017.10.017

Spike-timing-dependent plasticity of polyaniline-based memristive element

Lapkin D.A.; Emelyanov A.V.; Demin V.A.; Berzina T.S.; Erokhin V.V.

National Research Center, Kurchatov Institute, Moscow, 123182, Russian Federation; Moscow Institute of Physics and Technology (State University), Dolgoprudny, Moscow Region, 141700, Russian Federation; CNR-IMEM (National Research Council, Institute of Materials for Electronics and Magnetism), Parco Area delle Scienze, 37A, Parma, 43124, Italy;

A phenomenological model of the polyaniline (PANI) based memristive element's conductivity evolution during the application of varying voltages is presented in this work. The model is based on the experimental data on the conductance versus time dependencies for a set of applied voltages. The model could be used for simulation of complex artificial neural networks (ANNs) based on PANI memristive elements. We have experimentally shown that organic PANI-based memristive element could be trained by the biologically inspired spike-timing-dependent plasticity mechanism. The results obtained by the simulation using the developed model are in a good agreement with the experimental data. It allows considering the usage of the organic memristive element as a synaptic element in a hardware realization of spiking ANNs capable of non-supervised learning.

Microelectronic engineering 185-186 , pp. 43–47

Keywords

Artificial neural networks, Memristor, Polyaniline, Resistive switching, Spike-timing-dependent plasticity

CNR authors

Erokhin Victor, Ivanova Tatiana

CNR institutes

IMEM – Istituto dei materiali per l'elettronica ed il magnetismo