Most current Artificial Neural Network (ANN) models are based on highly simplified brain
dynamics. They have been used as powerful computational tools to solve complex pattern …

Recently it has been shown that a repeating arbitrary spatiotemporal spike pattern hidden in
equally dense distracter spike trains can be robustly detected and learned by a single …

The main contribution of this letter is the derivation of a steepest gradient descent learning
rule for a multilayer network of theta neurons, a one-dimensional nonlinear neuron model …

Modeling networks of spiking neurons is a common scientific method that helps to
understand how biological neural systems represent, process and store information. But the …

Independent component analysis (or blind source separation) is assumed to be an essential
component of sensory processing in the brain and could provide a less redundant …

Several strands of research in the fields of linguistics, speech perception, and neuroethology
suggest that modelling the temporal dynamics of an acoustic event landmark-based …

The basal ganglia are a group of subcortical nuclei that contain about 100 million neurons in
humans. Different modes of basal ganglia dysfunction lead to Parkinson's disease and …

Predictive learning rules, where synaptic changes are driven by the difference between a
random input and its reconstruction derived from internal variables, have proven to be very …

Electrophysiological connectivity patterns in cortex often show a few strong connections in a
sea of weak connections. In some brain areas a large fraction of strong connections are …

When entering a synapse, presynaptic pulse trains are filtered according to the recent pulse
history at the synapse and also with respect to their own pulse time course. Various …