In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

Artificial neural networks have been used as a powerful processing tool in various areas
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …

Abstract Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue
for low power spike event based computation. However, the spike generation function is non …

As a new brain-inspired computational model of the artificial neural network, a spiking
neural network encodes and processes neural information through precisely timed spike …

In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

The development of spiking neural network simulation software is a critical component
enabling the modeling of neural systems and the development of biologically inspired …

Artificial intelligence (AI) is gaining strength, and materials science can both contribute to
and profit from it. In a simultaneous progress race, new materials, systems, and processes …

This article reviews recent progress in the development of the computing framework vector
symbolic architectures (VSA)(also known as hyperdimensional computing). This framework …

The computational power of formal models for networks of spiking neurons is compared with
that of other neural network models based on McCulloch Pitts neurons (ie, threshold gates) …

A key challenge for neural modeling is to explain how a continuous stream of multimodal
input from a rapidly changing environment can be processed by stereotypical recurrent …