Different learning modes and mechanisms allow faster and better acquisition of skills as
widely studied in humans and many animals. Specific neurons, called mirror neurons, are …

Spiking neural networks (SNNs) offer both compelling potential advantages, including
energy efficiency and low latencies and challenges including the non-differentiable nature of …

Spiking Neural Networks (SNNs) are at the forefront of neuromorphic computing thanks to
their potential energy-efficiency, low latencies, and capacity for continual learning. While …

This paper is devoted to developing a biological-based algorithm to simulate the control of a
human arm by means of a Spiking Neural Network (SNN) with a pre-set structure similar to …

The interaction between robots and humans is of great relevance for the field of
neurorobotics as it can provide insights on how humans perform motor control and sensor …

In robotics, spiking neural networks (SNNs) are increasingly recognized for their largely
unrealized potential energy efficiency and low latency particularly when implemented on …

Spiking neural networks have significant potential utility in robotics due to their high energy
efficiency on specialized hardware, but proof-of-concept implementations have not yet …

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 …

Evolution gave humans advanced grasping capabilities combining an adaptive hand with
efficient control. Grasping motions can quickly be adapted if the object moves or deforms …

Current low-latency neuromorphic processing systems hold great potential for developing
autonomous artificial agents. However, the variable nature and low precision of the …