Nanoelectronic neuromorphic networks (CrossNets): new results

Our group is developing neuromorphic network architectures for future hybrid semiconductor/nanowire/molecular ("CMOL") circuits. Estimates show that such networks ("CrossNets") may eventually overcome the cerebral cortex in areal density, operating at much higher speed, at acceptable power consumption. In this report, we demonstrate that CrossNets based on simple (two-terminal) molecular devices can be configured to reproduce the behavior of any known neural network, either feedforward or recurrent, using a synaptic weight import procedure. Two other training methods including the global reinforcement (that may enable CrossNets to perform more intelligent tasks) are also described in brief.