The brain functions as a spatio-temporal information processing machine. Spatio-and
spectro-temporal brain data (STBD) are the most commonly collected data for measuring …

Hardware implementations of spiking neurons can be extremely useful for a large variety of
applications, ranging from high-speed modeling of large-scale neural systems to real-time …

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

Shifting computing architectures from von Neumann to event-based spiking neural networks
(SNNs) uncovers new opportunities for low-power processing of sensory data in …

With the remarkable progress that technology has made, the need for processing data near
the sensors at the edge has increased dramatically. The electronic systems used in these …

Several analog and digital brain-inspired electronic systems have been recently proposed
as dedicated solutions for fast simulations of spiking neural networks. While these …

Abstract We propose a Double EXponential Adaptive Threshold (DEXAT) neuron model that
improves the performance of neuromorphic Recurrent Spiking Neural Networks (RSNNs) by …

On-line learning and recognition of spatio-and spectro-temporal data (SSTD) is a very
challenging task and an important one for the future development of autonomous machine …

Inspired by the principles of the biological nervous system, neuromorphic engineering has
brought a promising alternative approach to intelligence computing with high energy …

While Moore's law has driven exponential computing power expectations, its nearing end
calls for new avenues for improving the overall system performance. One of these avenues …