%0 Book Section %1 Bogdan2022-qc %A Bogdan, Martin %B XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja) %D 2022 %I Servizo de Publicacións da UDC %K algorithms learning networks neural neurological plausible spiking xack %P 201--207 %T Learning algorithms for spiking neural networks: should one use learning algorithms from ANN/DL or neurological plausible learning? - A thought-provoking impulse %X 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?

@incollection{Bogdan2022-qc, abstract = {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?}, added-at = {2025-01-07T11:29:56.000+0100}, author = {Bogdan, Martin}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2898046463dd305e66b73fcd9ee16f639/scadsfct}, booktitle = {{XLIII} Jornadas de Autom{\'a}tica: libro de actas: 7, 8 y 9 de septiembre de 2022, Logro{\~n}o (La Rioja)}, interhash = {b6e2a6f72be6b0cbb8a58b0adfd83c9e}, intrahash = {898046463dd305e66b73fcd9ee16f639}, keywords = {algorithms learning networks neural neurological plausible spiking xack}, month = sep, pages = {201--207}, publisher = {Servizo de Publicaci{\'o}ns da UDC}, timestamp = {2025-07-29T10:50:32.000+0200}, title = {Learning algorithms for spiking neural networks: should one use learning algorithms from {ANN/DL} or neurological plausible learning? - A thought-provoking impulse}, year = 2022 }