This paper investigates noise cancellation problem of memristive neural networks. Based on the reproducible gradual resistance tuning in bipolar mode, a first-order voltage-controlled memristive model is employed with asymmetric voltage thresholds. Since memristive devices are especially tiny to be densely packed in crossbar-like structures and possess long time memory needed by neuromorphic synapses, this paper shows how to approximate the behavior of synapses in neural networks using this memristive device. Also certain templates of memristive neural networks are established to implement the noise cancellation
Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operatio...
Memristive devices represent a promising technology for building neuromorphic electronic systems. In...
Memristive devices present a new device technology allowing for the realization of compact non-volat...
This book covers a range of models, circuits and systems built with memristor devices and networks i...
Neuromorphic computing describes the use of electrical circuits to mimic biological architecture pre...
A linearized programming method of memristor-based neural weights is proposed. Memristor is known as...
The paper introduces a class of memristor neural networks (NNs) that are characterized by the follow...
Artificial neural networks are successfully used for classification, prediction, estimation, modelin...
The advancements in the field of Artificial Intelligence (AI) and technology has led to an evolution...
Pulse-coupled neural network (PCNN) is inspired from the visual cortex of cats. It is superior to th...
The invention of neuromorphic computing architecture is inspired by the working mechanism of human-b...
Analog switching memristive devices can be used as part of the acceleration block of Neural Network...
Data-intensive computing operations, such as training neural networks, are essential but energy-inte...
A simple neural circuit coupled by magnetic flux-controlled memristor (MFCM) can be controlled to de...
Memristors are memory resistors that promise the efficient implementation of synaptic weights in art...
Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operatio...
Memristive devices represent a promising technology for building neuromorphic electronic systems. In...
Memristive devices present a new device technology allowing for the realization of compact non-volat...
This book covers a range of models, circuits and systems built with memristor devices and networks i...
Neuromorphic computing describes the use of electrical circuits to mimic biological architecture pre...
A linearized programming method of memristor-based neural weights is proposed. Memristor is known as...
The paper introduces a class of memristor neural networks (NNs) that are characterized by the follow...
Artificial neural networks are successfully used for classification, prediction, estimation, modelin...
The advancements in the field of Artificial Intelligence (AI) and technology has led to an evolution...
Pulse-coupled neural network (PCNN) is inspired from the visual cortex of cats. It is superior to th...
The invention of neuromorphic computing architecture is inspired by the working mechanism of human-b...
Analog switching memristive devices can be used as part of the acceleration block of Neural Network...
Data-intensive computing operations, such as training neural networks, are essential but energy-inte...
A simple neural circuit coupled by magnetic flux-controlled memristor (MFCM) can be controlled to de...
Memristors are memory resistors that promise the efficient implementation of synaptic weights in art...
Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operatio...
Memristive devices represent a promising technology for building neuromorphic electronic systems. In...
Memristive devices present a new device technology allowing for the realization of compact non-volat...