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.

DefectTolerant Crossbar Training of Memristor Ternary Neural Networks A memristor Ternary Neural Network (TNN) is a promising candidate for implementing neural networks for InternetofThings (IoT) applications, where low power and simple hardware are important. One important concern in the memristor TNN is that the real memristor crossbar has various defects such as stuckatfaults, memristance variation, etc., like human brainu0027s biological neurons and synapses. To mitigate the inference loss due to the memristive defects, we need to retrain the defective crossbar. However, the crossbaru0027s retraining needs a long time and a large amount of energy of programming, because the memristors should be programmed one by one using Incremental Step Pulse Programming (ISPP) of flash memories. Here, we combine the partialgated training scheme with the asymmetrical training for not only minimizing the recognition rate loss, but also saving the crossbaru0027s programming time and energy. The CF scheme with 10% retraining indicates the programming energy can be saved by as large as xe2x88xbc98%, in sacrifice with the MNIST rate loss of xe2x88xbc0.6%, compared to the FF scheme with 100% retraining. The simulation indicates that the CF with 10% retraining will be useful for realizing the crossbarbased neuralnetwork in large scale for future IoT applications.