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05:02 EDT 4th October 2015 | BioPortfolio

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New memristor technology could bring us closer to brain-like computing

SOURCE April 8, 2015 (Nanowerk News) Researchers are always searching for improved technologies, but the most efficient computer possible already exists. It can learn and adapt without needing to be p...

Training and operation of an integrated neuromorphic network based on metal-oxide memristors

Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex, with its approximately 1014 synapses, makes the hardware implementation of ne...

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Experimental Demonstration of a Second-Order Memristor and Its Ability to Biorealistically Implement Synaptic Plasticity.

Memristors have been extensively studied for data storage and low-power computation applications. In this study, we show that memristors offer more than simple resistance change. Specifically, the dyn...

Memristor Models for Machine Learning.

In the quest for alternatives to traditional complementary metal-oxide-semiconductor, it is being suggested that digital computing efficiency and power can be improved by matching the precision to the...

Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training.

Learning in multilayer neural networks (MNNs) relies on continuous updating of large matrices of synaptic weights by local rules. Such locality can be exploited for massive parallelism when implementi...

Synchronization of Memristor-Based Coupling Recurrent Neural Networks With Time-Varying Delays and Impulses.

Synchronization of an array of linearly coupled memristor-based recurrent neural networks with impulses and time-varying delays is investigated in this brief. Based on the Lyapunov function method, an...

Data Clustering using Memristor Networks.

Memristors have emerged as a promising candidate for critical applications such as non-volatile memory as well as non-Von Neumann computing architectures based on neuromorphic and machine learning sys...

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