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 …

Spiking neural networks (SNNs) are promising in ascertaining brain-like behaviors since
spikes are capable of encoding spatio-temporal information. Recent schemes, eg, pre …

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 …

In order to understand how the mammalian neocortex is performing computations, two
things are necessary; we need to have a good understanding of the available neuronal …

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 …

The field of Deep Learning (DL) has seen a remarkable series of developments with
increasingly accurate and robust algorithms. However, the increase in performance has …

Neuromorphic computing is henceforth a major research field for both academic and
industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at …

Spiking neural networks (SNNs) are a promising energy-efficient alternative to artificial
neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal …

Spiking neural networks (SNNs) incorporating biologically plausible neurons hold great
promise because of their unique temporal dynamics and energy efficiency. However, SNNs …