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 …

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

Event-based vision sensors achieve up to three orders of magnitude better speed vs. power
consumption trade off in high-speed control of UAVs compared to conventional image …

In this work, an operant conditioning (OC) model and memristive circuit implementation
based on emotion generation and modulation is proposed, which is inspired by neural and …

The rapid increase of wearable sensor devices poses new challenges for implementing
continuous real-time processing of physiological data. Neuromorphic sensory-processing …

Spiking Neuronal Networks (SNNs) realized in neuromorphic hardware lead to low-power
and low-latency neuronal computing architectures. Neuromorphic computing systems are …

Neuromorphic computing offers the opportunity to implement extremely low power artificial
intelligence at the edge. Control applications, such as autonomous vehicles and robotics …

Neuromorphic engineering promises the deployment of low latency, adaptive and low power
systems that can lead to the design of truly autonomous artificial agents. However, many …

Spiking neural networks are able to control with high precision the rotation and force of
single-joint robotic arms when shape memory alloy wires are used for actuation. Bio …

Beyond providing accurate movements, achieving smooth motion trajectories is a long-
standing goal of robotics control theory for arms aiming to replicate natural human …