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CrossNets: possible neuromorphic networks based on nanoscale components
Author(s) -
Türel Özgür,
Likharev Konstantin
Publication year - 2003
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.223
Subject(s) - neuromorphic engineering , network topology , crossbar switch , computer science , topology (electrical circuits) , cmos , artificial neural network , biological neural network , computer architecture , nanowire , neuroscience , electronic circuit , electronic engineering , nanotechnology , artificial intelligence , materials science , engineering , electrical engineering , biology , telecommunications , machine learning , operating system
Abstract Extremely dense neuromorphic networks may be based on hybrid 2D arrays of nanoscale components, including molecular latching switches working as adaptive synapses, nanowires as axons and dendrites, and nano‐CMOS circuits serving as neural cell bodies. Possible architectures include ‘free‐growing’ networks that may form topologies very close to those of cerebral cortex, and several species of distributed crossbar‐type networks, ‘CrossNets’ (including notably ‘InBar’ and ‘RandBar’), with better density and speed scaling. Numerical modelling show that the specific signal sign asymmetry used in CrossNets allows self‐excitation of recurrent networks with long‐range cell interaction, without a symmetry‐breaking global latchup. Our next goal is to develop methods of globally supervised teaching of extremely large networks with no external access to individual synapses. Such development would open a way towards cerebral‐cortex‐scale networks (with ∼1010 neural cells and ∼1014 synapses) capable of advanced information processing and self‐evolution at a speed several orders of magnitude higher than their biological prototypes. Copyright © 2003 John Wiley & Sons, Ltd.