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CrossNets: Possible neuromorphic networks based on nanoscale components. (English) Zbl 1016.94045

Summary: 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, which may form topologies very close to those of the cerebral cortex, and several species of distributed crossbar-type networks, ‘CrossNets’ (including notably ‘InBar’ and ‘RandBar’), with better density and speed scaling. Numerical modelling shows 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 \(\sim 10^{10}\) neural cells and \(\sim 10^{14}\) synapses) capable of advanced information processing and self-evolution at a speed several orders of magnitude higher than their biological prototypes.

MSC:

94C05 Analytic circuit theory
68T05 Learning and adaptive systems in artificial intelligence
68M07 Mathematical problems of computer architecture
92B20 Neural networks for/in biological studies, artificial life and related topics

Software:

CrossNets
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