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 spike-timing…

In this paper, we present our design and experiments on a planar biped robot under the control
of a pure sensor-driven controller. This design has some special mechanical features, for …

Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the
biomechanical design with its neuronal control. The coordination of this process is a very …

Reinforcement learning (RL) is learning by interacting with an environment. An RL agent
learns from the consequences of its actions, rather than from being explicitly taught and it …

Embodied cognition suggests that complex cognitive traits can only arise when agents have
a body situated in the world. The aspects of embodiment and situatedness are being …

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 input …

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 …

Spike-timing-dependent plasticity (STDP) is described by long-term potentiation (LTP),
when a presynaptic event precedes a postsynaptic event, and by long-term depression (LTD), …

The R peak detection of an ECG signal is the basis of virtually any further processing and
any error caused by this detection will propagate to further processing stages. Despite this, R …

To effectively study the mechanisms by which deep brain stimulation (DBS) produces its
therapeutic benefit and to evaluate new therapeutic indications, it is vital to administer DBS over …