Theory of Cellular Nonlinear Networks with Analogue Dynamic Memristors Cellular Nonlinear Networkbased visual microprocessors offer the opportunity to process and store data at the detection place, but suffer from low spatial resolution due to the allocation of memory units to each cell. Memristor technologies offer the opportunity to resolve this issue. In fact, nonvolatile memristors, employed in a novel cell design, may contribute to process data, and serve as memory units. Using a memristor with scalar state in each cell calls for the generalization of the Dynamic Route Map technique for analysing and designing arrays with secondorder processing elements. This novel systemtheoretical concept is introduced here through illustrative examples.

A Continuoustime Learning Rule for Memristorxe2x80x93based Recurrent Neural Networks Among the recent disruptive technologies, volatilenonvolatile memoryresistor (memristor) has attracted the researchersu0027 attention as a fundamental computation element. It has been experimentally shown that memristive elements can emulate synaptic dynamics and are even capable of supporting spike timing dependent plasticity (STDP), an important adaptation rule for competitive Hebbian learning. The overall goal of this work is to provide a novel analogue computing platform based on memristor devices and recurrent neural networks that exploit the memristor device physics to implement the backpropagation algorithm. Back propagation for recurrent neural networks requires a side network for the propagation of error derivatives. The use of memristorbased synaptic weights permit to propagate the error signals in the network via the nonlinear dynamics without the need of a digital side network. Experimental results show that the approach significantly outperforms conventional architectures used for pattern reconstruction. Further results will be reported in an extended work.