Wearable devices are a fast-growing technology with impact on personal healthcare for both
society and economy. Due to the widespread of sensors in pervasive and distributed …

Spiking Neural Networks (SNNs) have recently emerged as a prominent neural computing
paradigm. However, the typical shallow SNN architectures have limited capacity for …

In recent times, the usage of modern neuromorphic hardware for brain-inspired SNNs has
grown exponentially. In the context of sparse input data, they are undertaking low power …

This paper gives an overview of recent progress on 1) online learning algorithms with
spiking neurons 2) neuromorphic platforms that efficiently run these algorithms with a focus …

Neuromorphic engineering combines the architectural and computational principles of
systems neuroscience with semiconductor electronics, with the aim of building efficient and …

We explore classifier training for data sets with very few labels. We investigate this task
using a neural network for nonnegative data. The network is derived from a hierarchical …

Biological and artificial neural networks (ANNs) represent input signals as patterns of neural
activity. In biology, neuromodulators can trigger important reorganizations of these neural …

Artificial neural networks (ANNs) trained using backpropagation are powerful learning
architectures that have achieved state-of-the-art performance in various benchmarks …

Using a neural sampling approach, networks of stochastic spiking neurons, interconnected
with plastic synapses, have been used to construct computational machines such as …

An extensive reliance on technology, an abundance of data, and increasing processing
requirements have imposed severe challenges on computing and data processing …