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 hardware enables fast and power-efficient neural network–based artificial
intelligence that is well suited to solving robotic tasks. Neuromorphic algorithms can be …

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

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

The energy-efficient control of mobile robots has become crucial as the complexity of their
real-world applications increasingly involves high-dimensional observation and action …

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 …

Neuromorphic electronics have great potential in the emulation of the sensory, cognitive, self-
learning, and actuating functions of robots. While typically implemented in rigid silicon …

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 …

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

Simulating the brain-body-environment trinity in closed loop is an attractive proposal to
investigate how perception, motor activity and interactions with the environment shape brain …