Zusammenfassung
Artificial Neural Networks (ANN) and Machine Learning (ML)
currently also known as Deep Learning (DL) became more and more
important in industrial applications during the last decade.
This is due to new possibilities by strongly increased available
computational power in connection with a renaissance of ANN in
terms of so-called Deep Learning (DL). As DL requires especially
for Big Data extreme computational power, the question of
resource preserving methods came recently into the focus. Also,
the often propagated intelligence of DL resp. ``Cognitive
Computing'' in terms of contextual information processing is
more often discussed since it is effectively missed in DL
solutions. One option to overcome both challenges might be the
third generation of ANNs: Spiking Neural Networks (SNN). But
since SNN training methods are slow compared to DL learning
algorithms, the question of the way how to learn SNNs arose. We
will discuss different aspects of learning algorithms for SNNs:
Is it useful to adopt DL learning algorithms to SNN or not,
especially if one will preserve the ``cognitive'' functions of
SNNs?
Nutzer
