Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial
neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …

The potential of neuromorphic computing to bring about revolutionary advancements in
multiple disciplines, such as artificial intelligence (AI), robotics, neurology, and cognitive …

Moiré quantum materials host exotic electronic phenomena through enhanced internal
Coulomb interactions in twisted two-dimensional heterostructures,,–. When combined with …

Abstract Spiking Neural Networks (SNNs) have been studied over decades to incorporate
their biological plausibility and leverage their promising energy efficiency. Throughout …

Human visual neurons rely on event-driven, energy-efficient spikes for communication, while
silicon image sensors do not. The energy-budget mismatch between biological systems and …

The demand for accurate perception of the physical world leads to a dramatic increase in
sensory nodes. However, the transmission of massive and unstructured sensory data from …

Neuroscience findings emphasize the role of dendritic branching in neocortical pyramidal
neurons for non-linear computations and signal processing. Dendritic branches facilitate …

Recent Spiking Neural Networks (SNNs) works focus on an image classification task,
therefore various coding techniques have been proposed to convert an image into temporal …

In biological neural systems, different neurons are capable of self-organizing to form
different neural circuits for achieving a variety of cognitive functions. However, the current …

Spiking Neural Networks (SNNs), known for their potential to enable low energy
consumption and computational cost, can bring significant advantages to the realm of …