Trends and Advances in Bio-Inspired Image-Based Deep Learning Methodologies and Applications

Submission Deadline: 31 October 2020

IEEE Access invites manuscript submissions in the area of Trends and Advances in Bio-Inspired Image-Based Deep Learning Methodologies and Applications.

Many of the technological advances we enjoy today have been inspired by biological systems due to their ease of operation and outstanding efficiency. Designing technological solutions based on biological inspiration has become a cornerstone of research in a variety of areas ranging from control theory and optimization to computer vision, machine learning and artificial intelligence. Especially in the latter few areas, biologically relevant solutions are becoming increasingly important as we look for new ways to make artificial systems more efficient, intelligent and overall effective.

It is generally acknowledged that the human brain is a multitude of times more efficient than the best artificial intelligence algorithms and machine learning models available today. This suggests that there is still something fundamental to learn from the way the brain processes information and new (biologically-inspired) ideas are needed to devise a more effective form of computation capable of competing with the efficiency of biological systems.

One of the hottest and most active research topics in the field of machine learning and artificial intelligence right now is deep learning. Deep learning models exhibit a certain kind of biological relevance, but differ significantly from what we see in the human brain in their structure and efficiency, and the way they process information. Deep learning models, such as convolutional neural networks, consist of several processing layers that represent data at multiple levels of abstraction. Such models are able to implicitly capture the intricate structures of large-scale data and are closer in terms of information processing mechanisms to biological systems than earlier so-called shallow machine learning models.

However, despite the recent progress in deep learning methodologies and their success in various fields, such as computer vision, speech technologies, natural language processing, medicine, and the like, it is obvious that current models are still unable to compete with biological intelligence. It is, therefore, natural to believe that the state of the art in this area can be further improved if bio-inspired concepts are integrated into deep learning models.

The purpose of the Special Section is to present and discuss novel ideas, research, applications and results related to techniques of image processing and computer vision approaches based on bio-inspired intelligence and deep learning methodologies. It aims to bring together researchers from various fields to report the latest findings and developments in bio-inspired image-based intelligence, with a focus on deep learning methodologies and applications, and to explore future research directions.

The topics of interest include, but are not limited to, image-based methodologies, applications, and techniques such as:

• Bio-inspired deep model architectures
• Theoretical understanding of bio-inspired deep architectures, models and loss functions
• Novel bio-inspired training approaches for deep learning models
• Effective and scalable bio-inspired parallel algorithms to train deep models
• Bio-inspired deep learning techniques for modeling sequential (temporal) data
• Biologically relevant adaptation techniques for deep models
• End-to-end bio-inspired deep learning solutions
• Bio-inspired model design
• Bio-inspired visualizations and explanations of deep learning
• Applications of bio-inspired deep approaches in various domains

Note that “bio-inspired” is a crucial keyword in the above list. Thus, the submissions are expected to include a discussion about the bio-inspired background of the presented method. The authors must explain how their method and its novelty correlate with what we find in nature and/or organisms, brain, psychology and similar.

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor:  Peter Peer, University of Ljubljana, Slovenia

Guest Editors:

1. 1. Carlos M. Travieso-González, University of Las Palmas de Gran Canaria, Spain
   2. Vijayan K. Asari, University of Dayton Vision Lab, USA
   3. Malay K. Dutta, Dr. A.P.J. Abdul Kalam Technical University, India

Relevant IEEE Access Special Sections:

1. Deep Learning: Security and Forensics Research Advances and Challenges
2. Scalable Deep Learning for Big Data
3. Deep Learning Algorithms for Internet of Medical Things

IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: peter.peer@fri.uni-lj.si.