Most elementary behaviors such as moving the arm to grasp an object or walking into the
next room to explore a museum evolve on the time scale of seconds; in contrast, neuronal …

Classical Hebbian learning puts the emphasis on joint pre-and postsynaptic activity, but
neglects the potential role of neuromodulators. Since neuromodulators convey information …

Abstract Spiking Neural Networks (SNNs) have attracted enormous research interest due to
temporal information processing capability, low power consumption, and high biological …

Humans and many other animals have an enormous capacity to learn about sensory stimuli
and to master new skills. However, many of the mechanisms that enable us to learn remain …

We study policy gradient (PG) for reinforcement learning in continuous time and space
under the regularized exploratory formulation developed by Wang et al.(2020). We …

Biological intelligence processes information using impulses or spikes, which makes those
living creatures able to perceive and act in the real world exceptionally well and outperform …

Neurobiological systems continually interact with the surrounding environment to refine their
behaviour toward the best possible reward. Achieving such learning by experience is one of …

Reinforcement learning (RL) has recently regained popularity with major achievements such
as beating the European game of Go champion. Here, for the first time, we show that RL can …

Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and
dynamics with the aim of replicating its hallmark functional capabilities in terms of …

We propose a unified framework to study policy evaluation (PE) and the associated temporal
difference (TD) methods for reinforcement learning in continuous time and space. We show …