Neuromorphic architectures for nanoetectronic circuits

Abstract

AbstractThis paper reviews recent important results in the development of neuromorphic network architectures (‘CrossNets’) for future hybrid semiconductor/nanodevice‐integrated circuits. In particular, we have shown that despite the hardware‐imposed limitations, a simple weight import procedure allows the CrossNets using simple two‐terminal nanodevices to perform functions (such as image recognition and pattern classification) that had been earlier demonstrated in neural networks with continuous, deterministic synaptic weights. Moreover, CrossNets can also be trained to work as classifiers by the faster error‐backpropagation method, despite the absence of a layered structure typical for the ..