Computational Intelligence: Spiking Neural Networks
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 11739
Special Issue Editors
Interests: adaptive machine learning and intelligent information processing; including the theories of evolving connectionist systems; neuro-fuzzy systems; brain-inspired spiking neural networks
Special Issues, Collections and Topics in MDPI journals
Interests: spiking neural networks; computational intelligence; deep learning; neuro-robotics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
It is our pleasure to announce a new Special Issue “Computational Intelligence: Spiking Neural Networks” in the journal Big Data and Cognitive Computing. Spiking neural networks have recently emerged as an energy-efficient alternative to traditional deep neural networks. The close proximity of their information processing mechanisms to biological neurons allows them to handle temporal and spatiotemporal data naturally. This proximity also allows them to exploit recently proposed theories in neuroscience to develop novel approaches that can be used to address real-world problems. Further, recent advances in development of neuromorphic chips have made it possible to realize the software version of approaches into hardware versions. These theoretical and hardware level advances in the area of spiking neural networks have made it easier for recent artificial intelligence techniques to transition from research labs to real-world applications.
This Special Issue encourages researchers to present their recent theoretical and application-oriented advances involving spiking neural networks. Topics include novel theoretical approaches for learning using SNNs, bio-inspired learning, edge-computing, interpretability in SNNs, deep spiking neural networks, research involving neuromorphic hardware and application of SNNs in areas such as robotics, autonomous systems, etc.
This Special Issue invites contributions, including, but not limited to, the following detailed topics:
- Bio-inspired learning;
- Learning algorithms for SNNs to handle temporal, spatiotemporal data, etc.;
- Edge-computing;
- Interpretability in SNNs;
- Deep spiking neural networks;
- Robotics;
- Research involving neuromorphic and other hardware platforms.
Prof. Dr. Nik Kasabov
Dr. Shirin Dora
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- bio-inspired learning
- deep spiking neural networks
- edge-computing
- interpretability
- robotics