Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …

Artificial neural networks (ANNs) have experienced a rapid advancement for their success in
various application domains, including autonomous driving and drone vision. Researchers …

Since the beginning of information processing by electronic components, the nervous
system has served as a metaphor for the organization of computational primitives. Brain …

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 shown to be more biologically plausible and energy
efficient over their predecessors. However, there is a lack of an efficient and generalized …

The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …

Spiking neural networks combine analog computation with event-based communication
using discrete spikes. While the impressive advances of deep learning are enabled by …

In today's data-driven world, the ability to process large data volumes is crucial. Key tasks,
such as pattern recognition and image classification, are well suited for artificial neural …

For a biological agent operating under environmental pressure, energy consumption and
reaction times are of critical importance. Similarly, engineered systems are optimized for …

The critical state is assumed to be optimal for any computation in recurrent neural networks,
because criticality maximizes a number of abstract computational properties. We challenge …