Articolo in rivista, 2011, ENG, 10.1007/s12668-011-0004-7

Material Memristive Device Circuits with Synaptic Plasticity: Learning and Memory

Erokhin V.; Berzina T.; Camorani P.; Smerieri A.; Vavoulis D.; Feng J.; Fontana M.P.

CNR-IPCF, Rome 43100, Italy; Department of Physics, University of Parma, Viale Usberti 7A, Parma 43100, Italy; Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom; Centre for Computational Systems Biology, Fudan University, Shanghai, China

An important endeavor in modern materials science is the synthesis of adaptive assemblies with information processing capabilities similar to those of biological neural systems. Recent developments concern materials functionally similar to the memristor, a notional electrical circuit whose conductivity is dependent on past activity. This feature is analogous to synaptic plasticity: the ability of neurons to modify their synaptic connections as a result of accumulated experience-the basis of learning and the formation of memory. In this paper, we present the first evidence that memristive device-based organic materials show adaptive behavior similar to biological cognitive systems, using learning in the feeding neural network of the pond snail, Lymnaea stagnalis, as a specific biological reference. The synthetic reproduction of synaptic plasticity reported here can create new paradigms for novel computing systems and give impetus to the search for bio-inspired nanoscale molecular architectures capable of learning and decision making. © 2011 Springer Science+Business Media, LLC.

BioNanoScience (New York. Print) 1 (1-2), pp. 24–30

Keywords

Conducting polymer, Heterojunction, Learning and memory, Organic memristive system, Solid electrolyte, Synapse analog

CNR authors

Ivanova Tatiana, Erokhin Victor

CNR institutes

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