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 …

The rapid growth of deep learning, spurred by its successes in various fields ranging from
face recognition [1] to game playing [2], has also triggered a growing interest in the design of …

In individuals with sensory-motor impairments, missing limb functions can be restored using
neuroprosthetic devices that directly interface with the nervous system. However, restoring …

Though neuromorphic computers have typically targeted applications in machine learning
and neuroscience ('cognitive'applications), they have many computational characteristics …

While Moore's law has driven exponential computing power expectations, its nearing end
calls for new avenues for improving the overall system performance. One of these avenues …

Spiking neural networks (SNNs) are subjects of a topic that is gaining more and more
interest nowadays. They more closely resemble actual neural networks in the brain than …

Introduction In recent years, the application of deep learning models at the edge has gained
attention. Typically, artificial neural networks (ANNs) are trained on graphics processing …

To navigate in new environments, an animal must be able to keep track of its position while
simultaneously creating and updating an internal map of features in the environment, a …

The standard nature of computing is currently being challenged by a range of problems that
start to hinder technological progress. One of the strategies being proposed to address …

Frequency-modulated continuous wave radar sensors play an essential role for assisted
and autonomous driving as they are robust under all weather and light conditions. However …