Neuromorphic computing technologies will be important for the future of computing, but
much of the work in neuromorphic computing has focused on hardware development. Here …

The past decade has witnessed the great success of deep neural networks in various
domains. However, deep neural networks are very resource-intensive in terms of energy …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

Abstract We present Recurrent Vision Transformers (RVTs), a novel backbone for object
detection with event cameras. Event cameras provide visual information with sub …

Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …

Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …

Spiking neural networks (SNNs) are nature's versatile solution to fault-tolerant, energy-
efficient signal processing. To translate these benefits into hardware, a growing number of …

The human brain is a sophisticated, high‐performance biocomputer that processes multiple
complex tasks in parallel with high efficiency and remarkably low power consumption …

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 …

Inspired by detailed modelling of biological neurons, spiking neural networks (SNNs) are
investigated as biologically plausible and high-performance models of neural computation …