Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have
proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs …

Neuromorphic vision sensors present unique advantages over their frame based
counterparts. However, unsupervised learning of efficient visual representations from their …

Biological neural networks encode information very efficiently, and dynamically react to
sensory input on very small time scales. In contrast to most contemporary machine learning …

Dynamical systems are universal computers. They can perceive stimuli, remember, learn
from feedback, plan sequences of actions, and coordinate complex behavioural responses …

Up to now, modern machine learning (ML) has been based on approximating big data sets
with high-dimensional functions, taking advantage of huge computational resources. We …

Understanding how neurons are able to efficiently encode information is a topic with
applications ranging from more efficient neural network chips, to robot control, and also to …