The design of robots that interact autonomously with the environment and exhibit complex
behaviours is an open challenge that can benefit from understanding what makes living …

Neuromorphic computing is a brain-inspired approach to hardware and algorithm design
that efficiently realizes artificial neural networks. Neuromorphic designers apply the …

Neuromorphic computing draws inspiration from the brain to provide computing technology
and architecture with the potential to drive the next wave of computer engineering …

Spiking neural networks and neuromorphic hardware platforms that simulate neuronal
dynamics are getting wide attention and are being applied to many relevant problems using …

Despite the success of Deep Neural Networks—a type of Artificial Neural Network (ANN)—in
problem domains such as image recognition and speech processing, the energy and …

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 …

For many robotics applications, it is desirable to have relatively low-power and efficient
onboard solutions. We took inspiration from insects, such as ants, that are capable of …

Neuromorphic computing shows promise for advancing computing efficiency and
capabilities of AI applications using brain-inspired principles. However, the neuromorphic …

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

Spiking Neural Networks (SNNs) is a promising paradigm for efficient event-driven
processing of spatio-temporally sparse data streams. Spiking Neural Networks (SNNs) have …