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.


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


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