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Issue 43, 2012
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Stochastic hybrid 3D matrix: learning and adaptation of electrical properties

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Abstract

Memristive devices are electronic elements with memory properties. This feature marks them out as possible candidates for mimicking synapse properties. Development of systems capable of performing simple brain operations demands a high level of integration of elements and their 3D organization into networks. Here, we demonstrate the formation and electrical properties of stochastic polymeric matrices. Several features of the network revealed similarities with those of the nervous system. In particular, applying different training protocols, we obtained two kinds of learning comparable to the “baby” and “adult” learning in animals and humans. To mimic “adult” learning, multi-task training was applied simultaneously resulting in the formation of few parallel pathways for a given task, modifiable by successive training. To mimic “baby” learning (imprinting), single task training was applied at one time, resulting in the formation of multiple parallel signal pathways, scarcely influenced by successive training.

Graphical abstract: Stochastic hybrid 3D matrix: learning and adaptation of electrical properties

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Article information


Submitted
30 Jul 2012
Accepted
06 Sep 2012
First published
11 Sep 2012

J. Mater. Chem., 2012,22, 22881-22887
Article type
Paper

Stochastic hybrid 3D matrix: learning and adaptation of electrical properties

V. Erokhin, T. Berzina, K. Gorshkov, P. Camorani, A. Pucci, L. Ricci, G. Ruggeri, R. Sigala and A. Schüz, J. Mater. Chem., 2012, 22, 22881
DOI: 10.1039/C2JM35064E

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