In this review, we compare methods for temporal sequence learning (TSL) across the
disciplines machine-control, classical conditioning, neuronal models for TSL as well as …

Over the past decades, machine learning techniques, such as supervised learning,
reinforcement learning, and unsupervised learning, have been increasingly used in the …

The central resource processed by the sensorimotor system of an organism is information.
We propose an information-based quantity that allows one to characterize the efficiency of …

In this article, we present an isotropic unsupervised algorithm for temporal sequence
learning. No special reward signal is used such that all inputs are completely isotropic. All …

Currently all important, low-level, unsupervised network learning algorithms follow the
paradigm of Hebb, where input and output activity are correlated to change the connection …

Various optimality principles have been proposed to explain the characteristics of
coordinated eye and head movements during visual orienting behavior. At the same time …

It is a well-known fact that Hebbian learning is inherently unstable because of its self-
amplifying terms: the more a synapse grows, the stronger the postsynaptic activity, and …

For an animal to survive it has to excel in a twofold task: It has to perceive the world and
execute adequate actions. These skills are acquired and adapted through perceptual and …

Purpose–This work explores the consequences of Heinz von Foerster's claim in the context
of linear signal theory, embodiment and the creation of artifacts that the nervous system is …

Spike timing-dependent plasticity, related to differential Hebb-rules, has become a leading
paradigm in neuronal learning, because weights can grow or shrink depending on the …