Connectionists: Call for Papers: IJCNN Special session on Reservoir Computing for Scalable and Energy-Efficient AI: Theory, Dynamics, and Implementations

[Benjamin Paassen]

Dear colleagues,

we cordially invite you to submit to our IJCNN special session on 
Reservoir Computing for Scalable and Energy-Efficient AI: Theory, 
Dynamics, and Implementations.

Key Dates:
- Paper submission deadline: January, 31th 2026 (AoE)
- Decision notification: March, 15th  2026

Description and Topics

Reservoir Computing (RC) has established itself as an efficient and 
versatile approach for training and designing recurrent neural networks 
(RNNs), offering powerful capabilities in processing temporal and 
spatio-temporal data without the heavy computational demands of 
conventional deep learning models. As modern artificial intelligence 
increasingly moves toward large-scale, low-latency, and energy-efficient 
computation, the relevance of RC extends to a broad range of 
applications, including signal processing, robotics, neuroscience, and 
edge computing. The growing interest in hardware-aware and 
resource-constrained AI also reinforces the importance of RC as a 
framework for neuromorphic and embedded intelligence. However, to fully 
meet the challenges posed by scalability, robustness, and efficiency, RC 
requires new advances in theory, model design, and implementation 
strategies.

The goal of this special session is to promote progress in Reservoir 
Computing by addressing its theoretical, algorithmic, and hardware 
dimensions. The first objective is to deepen the theoretical 
understanding of the dynamical principles that govern stability, 
adaptability, and generalization in reservoir systems, particularly in 
the context of large-scale and heterogeneous data. The second objective 
is to foster the development of hybrid architectures that combine RC 
with modern deep learning approaches (such as convolutional, 
graph-based, or hierarchical models) to extend its reach to complex 
spatio-temporal domains. The third goal is to explore efficient and 
energy-aware hardware realizations of RC, leveraging advances in 
neuromorphic, photonic, and spintronic technologies for real-time, 
low-power computation.

We invite researchers to submit papers on all aspects of RC research, 
targeting contributions on theory, models, and applications.

A list of topics relevant to this session includes, but is not limited 
to, the following:

-Theoretical foundations of Reservoir Computing, including stability 
analysis, expressivity, generalization, and memory–capacity 
characterizations
-Dynamical systems theory for reservoir design, adaptation, and analysis
-Novel reservoir architectures: structured, modular, deep, hierarchical, 
multiscale, or hybrid with modern deep learning paradigms
-Learning algorithms for reservoir systems, including gradient-based, 
constrained, biologically inspired, or unsupervised/self-supervised 
approaches
-Training and adaptation of recurrent dynamics: online learning, 
continual learning, meta-learning, and federated settings in RC
-Reservoir Computing for high-dimensional spatio-temporal data, 
including images, video, physical processes, and graph-structured domains
-Hardware-oriented RC: neuromorphic, photonic, spintronic, analog, and 
mixed-signal implementations; co-design of models and substrates
-Energy-efficient and resource-aware RC for embedded, edge, or on-device 
intelligence
-Physical reservoirs and in-materio computing for machine learning
-Reservoir Computing in computational neuroscience and brain-inspired 
modeling
-Robustness, scalability, and reliability of reservoir systems in 
real-world environments
-Applications of Reservoir Computing across scientific, industrial, and 
societal domains, including robotics, system identification, 
forecasting, signal processing, geoscience, and biomedical data analysis

Submission

As in recent editions, the review process for IJCNN 2026 will be 
double-blind.

Page limit for full papers: up to 6 pages; notice that a maximum of two 
extra content pages per full paper is allowed (i.e, up to 8 pages), at 
an additional charge per extra page as specified in the registration 
page of the conference. Please note that ORCID will be required for each 
author of the paper.

Full authors' instructions can be found at the following link: 
https://attend.ieee.org/wcci-2026/information-for-authors/

Further information on the submission process can be found at the 
following link: https://attend.ieee.org/wcci-2026/submissions/

Link to submission system: https://ssl.linklings.net/conferences/WCCI/
Note that to submit to this special session, you need to create a new 
submission (Make a New Submission tab) and select the track IJCNN SS27 
Reservoir Computing for Scalable and Energy-Efficient AI: Theory, 
Dynamics, and Implementations.

Best regards,

Benjamin Paassen (Bielefeld University, Germany)
Gouhei Tanaka (Nagoya Institute of Technology, Japan)
Andrea Ceni (University of Pisa, Italy)
Claudio Gallicchio (University of Pisa, Italy)

Special Session Organizers


-- 
Jun. Prof. Benjamin Paaßen
Knowledge Representation and Machine Learning
Faculty of Technology
Bielefeld University

preferred pronouns: they/them

Office:  CITEC 1.220
Inspiration 1
33619 Bielefeld
Germany
Tel:     +49 521 106 87838
e-mail:  bpaassen at techfak.uni-bielefeld.de
website: bpaassen.gitlab.io