CrossNets: possible neuromorphic networks based on nanoscale components

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 speci c 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 ∼ 10 neural cells and ∼ 10 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.