Long short-term memory (LSTM) has transformed both machine learning and
neurocomputing fields. According to several online sources, this model has improved …

Artificial neural networks (ANNs) are essential tools in machine learning that have drawn
increasing attention in neuroscience. Besides offering powerful techniques for data analysis …

Recurrently connected networks of spiking neurons underlie the astounding information
processing capabilities of the brain. Yet in spite of extensive research, how they can learn …

Abstract Spiking Neural Networks (SNNs) have recently attracted significant research
interest as the third generation of artificial neural networks that can enable low-power event …

Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes)
which can potentially lead to higher energy-efficiency in neuromorphic hardware …

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 …

Over the past decade, deep neural networks (DNNs) have demonstrated remarkable
performance in a variety of applications. As we try to solve more advanced problems …

Spiking neural networks (SNNs) are well suited for spatio-temporal learning and
implementations on energy-efficient event-driven neuromorphic processors. However …

We propose a new supervised learning rule for multilayer spiking neural networks (SNNs)
that use a form of temporal coding known as rank-order-coding. With this coding scheme, all …

Abstract Spiking Neural Networks (SNNs) are promising for enabling low-power event-
driven data analytics. The best performing SNNs for image recognition tasks are obtained by …