Connectionists: [CFP] WCCI-IJCNN 2024 Special Session on Reservoir Computing

[Gouhei Tanaka]

Special Session on Reservoir Computing: Progress in Methods, Applications, and Implementations

IEEE World Congress on Computational Intelligence (WCCI 2024)
International Joint Conference on Neural Networks (IJCNN 2024)
30 June - 5 July 2024,  Yokohama (Japan)

More info at: https://dyn.web.nitech.ac.jp/en/wcci2024_ss_rc

=== Important dates ===
Paper submission deadline: January 15, 2024
Decision notification: March 15, 2024
Final Paper Submission & Early Registration Deadline: May 1, 2024

=== Scope ===
Reservoir Computing (RC) is a computational framework derived from efficient training methods for recurrent neural network models, which consists of a fixed hidden recurrent layer (called a reservoir) and a trainable output layer (called a readout).

The research field of RC has expanded broadly in recent years. First, many advanced RC models and learning methods have been developed to enhance computational performance in temporal pattern recognition while maintaining its low computational cost. Some RC models have been combined with other machine learning techniques, data scientific approaches, and inspirations from neuroscience. Second, the RC approaches have been applied to engineering applications with sensor data under resource/power-constrained conditions such as edge AI systems. Third, physical RC has been actively explored for realizing efficient AI hardware and device in the context of neuromorphic and unconventional computing, e.g., based on photonics, material science, and spintronics. Fourth, mathematical theory and analysis for revealing RC properties have made much progress. In this way, RC is a rapidly growing research topic, while being closely related to machine learning, dynamical systems theory, neuroscience, and bio/natural-computing, and AI hardware.

This special session is intended to be a hub for discussion and collaboration within the Neural Networks community, and therefore invites contributions on all aspects of RC, from theory to new models to emerging applications.

=== Topics ===
A list of topics relevant to this session includes, but is not limited to, the following:
* New Reservoir Computing models and architectures, including Echo State Networks and Liquid State Machines
* Hardware, physical, and neuromorphic implementations of Reservoir Computing systems
* Learning algorithms in Reservoir Computing
* Reservoir Computing in Computational Neuroscience
* Reservoir Computing on the edge systems
* Novel learning algorithms rooted in Reservoir Computing concepts
* New applications of Reservoir Computing, e.g., to images, video and structured data
* Federated and Continual Learning in Reservoir Computing
* Deep Reservoir Computing neural networks
* Theory of complex and dynamical systems in Reservoir Computing
* Extensions of the Reservoir Computing framework, such as Conceptors

=== Submission ===
 (Details in https://dyn.web.nitech.ac.jp/en/wcci2024_ss_rc)
1. Go to "https://edas.info/N31614"
2. Choose "IJCNN, Special Session Papers"
3. In the topics section, choose "Special Session: Reservoir Computing: Progress in Methods, Applications, and Implementations"

=== Organizers ===
Andrea Ceni (University of Pisa, Italy),
Claudio Gallicchio (University of Pisa, Italy),
Ryosho Nakane (The University of Tokyo, Japan),
Gouhei Tanaka (Nagoya Institute of Technology, Japan)

Sincerely,
Organizing team