Dynamical Systems and Brain Inspired Computing
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".
Deadline for manuscript submissions: closed (20 October 2021) | Viewed by 6038
Special Issue Editors
Interests: quantum information theory; experimental quantum and non linear optics; machine learning
Interests: modeling and nonlinear dynamics of semiconductor lasers; synchronization phenomena and bio-inspired information processing
Special Issue Information
The brain is one of the greatest enigmas of our time. It can deal with novelty and ambiguity and solve hard tasks such as visual scenery analysis and locomotion control. This is carried out with very low energy consumption, using a huge number of neurons operating in parallel – the complete opposite of the Von Neumann architecture used in digital computers. Recent spectacular advances in artificial intelligence show that brain-inspired algorithms can solve problems that appear unfeasible with traditional approaches. In parallel, Moore’s law is reaching its limit, due to the physical barrier to further miniaturize integrated electrical circuits. These facts encourage scientists to develop novel brain-inspired paradigms for machine computing. The challenge is to develop powerful new algorithms for solving hard tasks; to gain deeper insights into how dynamical systems, including the brain, process information; and to develop novel unconventional hardware for fast efficient information processing.
Recent progress, both theoretical and experimental, has invigorated the field. Topics such as reservoir computing, Ising machines, accelerators for artificial neural networks, are addressing the above challenge. These are often based on high-dimensional dynamical systems that exhibit a transition from stability to deterministic chaos – the kind of systems studied by physicists interested in complex systems and also the kind of structures that may exist in the brain. These paradigms intimately connect the fields of physics, information theory, computer science, and engineering. They raise new questions, such as the following: How to measure the computational power of such unconventional computing systems? How to train them to execute useful tasks? What applications are best suited for these novel computing paradigms? What kinds of hardware are best suited for implementations?
This Special Issue focuses on the above questions and the challenges in the development of novel computing architectures inspired by artificial intelligence algorithms and by how the brain processes information. The topics include, but are not limited to the following:
- Reservoir computing, Ising machines, and accelerators for artificial neural networks
- Novel methods for using dynamical systems for information processing
- Theoretical analysis of information processing capability of dynamical systems, including methods based on information theory and entropy
- Experimental implementations of brain-inspired computing, including optical and (unconventional) electronic implementations
- Practical applications of such systems, for instance to telecommunications
- Connections to other areas such as neuroscience or soft robotics
Prof. Dr. Serge Massar
Prof. Dr. Guy Van der Sande
Dr. Piotr Antonik
Guest Editors
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