Brain-inspired Computing and Machine Learning (editorial)

0523724 - ÚI 2021 RIV GB eng J - Journal Article
Iliadis, L. - Kůrková, Věra - Hammer, B.
Brain-inspired Computing and Machine Learning (editorial).
Neural Computing & Applications. Roč. 32, č. 11 (2020), s. 6641-6643. ISSN 0941-0643. E-ISSN 1433-3058
Institutional support: RVO:67985807
Keywords : editorial * brain-inspired computing
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 5.606, year: 2020
Method of publishing: Open access
http://dx.doi.org/10.1007/s00521-020-04888-6

IN: Neural Computing & Applications. 2020, 32(11), 6641-6643. ISSN 0941-0643. doi: 10.1007/s00521-020-04888-6. ANNOTATION: Recent research in machine learning (ML) and neurophysiology has focused in the development of highly intelligent algorithms, utilizing information processing principles of the human brain. Deep learning is inspired by the architecture of the cerebral cortex and it has attracted the attention of many artificial intelligence (AI) scientists. It is the dominating AI approach in specific domains (e.g., image–voice classification, object detection) regardless of its requirements in high computational power and in high volume of data. This is the editorial of the “Brain Inspired Computing and Machine Learning” Special Issue (SI) of the Neural Computing and Applications Springer Journal. The response of the scientific community has been significant, as many original research papers have been submitted for consideration. Totally, 11 papers were accepted out of 20, after going through a peer-review process. All of them have significant elements of novelty and they are introducing interesting modeling approaches or algorithms, inspired by the biological processes of the human brain.
Permanent Link: http://hdl.handle.net/11104/0308028