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 …

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic
chips with high energy efficiency by introducing neural dynamics and spike properties. As …

Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …

Pathologic immune hyperactivation is emerging as a key feature of critical illness in COVID-
19, but the mechanisms involved remain poorly understood. We carried out proteomic …

Spiking neural networks (SNNs) that enables energy efficient implementation on emerging
neuromorphic hardware are gaining more attention. Yet now, SNNs have not shown …

Developing neuromorphic intelligence on event-based datasets with Spiking Neural
Networks (SNNs) has recently attracted much research attention. However, the limited size …

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 …

The cointegration of artificial neuronal and synaptic devices with homotypic materials and
structures can greatly simplify the fabrication of neuromorphic hardware. We demonstrate …

Spiking neural networks (SNNs) serve as a promising computational framework for
integrating insights from the brain into artificial intelligence (AI). Existing software …