Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …

Reservoir computing is a computational framework suited for temporal/sequential data
processing. It is derived from several recurrent neural network models, including echo state …

Understanding the fundamental relationships between physics and its information-
processing capability has been an active research topic for many years. Physical reservoir …

Deep spiking neural networks (SNNs) hold the potential for improving the latency and
energy efficiency of deep neural networks through data-driven event-based computation …

Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in
spiking neural networks (SNN) to extract visual features of low or intermediate complexity in …

Brain-computer interface (BCI) is a system empowering humans to communicate with or
control the outside world with exclusively brain intentions. Electroencephalography (EEG) …

Deep neural networks such as Convolutional Networks (ConvNets) and Deep Belief
Networks (DBNs) represent the state-of-the-art for many machine learning and computer …

A proposal for a fully post-phrenological neuroscience that details the evolutionary roots of
functional diversity in brain regions and networks. The computer analogy of the mind has …

We present a neural network-based method for solving linear and nonlinear partial
differential equations, by combining the ideas of extreme learning machines (ELM), domain …

Next-generation wireless networks must support ultra-reliable, low-latency communication
and intelligently manage a massive number of Internet of Things (IoT) devices in real-time …