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

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84221

Berzina T, Gorshkov K, Camorani P, Pucci A, Ricci L, Ruggeri G, Sigala,  R
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84202

Schüz,  A
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Erokhin, V., Berzina T, Gorshkov K, Camorani P, Pucci A, Ricci L, Ruggeri G, Sigala, R., & Schüz, A. (2012). Stochastic hybrid 3D matrix: learning and adaptation of electrical properties. Journal of Materials Chemistry, 22(43), 22881-22887. doi:10.1039/C2JM35064E.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-B62C-7
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.