# NeuCompI at WCCI 2026
## Neuromorphic and Computational Intelligence: Synergies, Applications, and Learnings
> [Special Session at the 2026 IEEE World Congress on Computational Intelligence (WCCI)](https://attend.ieee.org/wcci-2026/) 

```{image} ../../_static/images/wcci2026-banner.png
:alt: WCCI Banner
:width: 100%
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:target: https://attend.ieee.org/wcci-2026/
```

<div style="text-align: center">
    <a href="#aim-and-scope">Aim and Scope</a> |
    <a href="#main-topics">Main Topics</a> |
    <a href="#session-format">Session Format</a> |
    <a href="#important-dates">Important Dates</a> |
    <a href="#organisers">Organisers</a>
</div>


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    <a href="https://attend.ieee.org/wcci-2026/call-for-papers/" 
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        Submit Your Paper
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        Register
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(aim-and-scope)=
## Aim and Scope

Between signals and noise, _animata et taxidermata_, excitations and inhibitions, two communities are believed to have evolved in isolation. On the one hand, Computational Intelligence (CI) has established itself as a leading paradigm for solving complex optimisation and learning tasks, primarily through the design of metaheuristics, evolutionary algorithms, and adaptive systems. On the other hand, Neuromorphic Computing (NC) has advanced architectures and models inspired by neural computation, emphasising asynchronous, event-driven, and energy-efficient processing. Together, these perspectives define a research space in which optimisation and learning emerge from the dynamics of spiking neurons, synapses, and networks, rather than being externally orchestrated.

Nowadays, when the demand for scalability and energy efficiency is pushing the limits of classical hardware and algorithms, these two fields find themselves converging. CI provides a mature corpus of algorithmic design strategies, from population-based heuristics to reinforcement-driven adaptation, that can be reformulated in spike-based, event-driven substrates. NC, however, is not only a matter of hardware design but also a scientific effort to understand and replicate the dynamics of real brains. It integrates insights from neuroscience, physics, and circuit design to capture how neurons and synapses process information, adapt through plasticity, and operate under noise and variability.

This special session is the first to explicitly explore the synergy between Computational Intelligence and Neuromorphic Computing in the context of optimisation and learning. Its relevance lies in positioning neuromorphic optimisation as a distinct strand within computational intelligence, complementing rather than competing with mainstream approaches. Our main objective is to create a dedicated forum where contributions rethink optimisation and learning as hardware-aware processes that can be embedded directly in neuromorphic substrates. This session also aims to consolidate a community and define a roadmap for this emerging field.

(main-topics)=
## Main Topics

The session welcomes submissions in the following areas (non-exhaustive):

### A. Spike-driven and Event-based Computational Paradigms 
1. Spike-based (event-driven) optimisation algorithms and metaheuristics
2. Asynchronous, event-driven computational intelligence models
3. Population-inspired spiking/swarm systems and coordination via spikes

### B. Adaptation, Plasticity, and Learning in Optimisation
4. Plasticity, neuromodulation, and meta-plasticity for adaptive search
5. Reinforcement-driven operator selection and continual adaptation
6. Dynamic / continual neuromorphic optimisation under non-stationary environments

### C. Hybrid and Co-Design Approaches
7. Hybrid architectures combining neuromorphic and conventional CI (e.g. deep models, surrogates)
8. Hardware-algorithm co-design
9. Mapping and deployment to neuromorphic hardware (Loihi, SpiNNaker, BrainScaleS, analogue circuits)

### D. Performance, Benchmarking and Constraints
10. Energy-latency trade-off, benchmarking, and scalability
11. Constraints and combinatorial optimisation in spiking frameworks
12.	Low-power performance metrics and evaluation

### E. Applications and Domain Use Cases
13. Robotics, control, scheduling, embedded systems, quantum, edge intelligence
14. Synergies between neuromorphic, neuroAI, and computational intelligence

### F. Theory, Foundations and Emerging Trends
15. Principles linking neuromorphic dynamics, optimisation, and learning
16. Computational neuroscience insights, coding theory, convergence
17. Emerging devices (memristive, spintronic, photonic), co-optimised sensing, hybrid reasoning

(session-format)=
## Session Format

The session will consist of **five presentations of 15 minutes each**, covering algorithms, adaptation, hardware, and applications, followed by **45 minutes of structured open debate**. The discussion will revolve around spike-triggering questions, aiming to encourage exchange, define open problems, and outline a roadmap for neuromorphic optimisation and learning.


(important-dates)=
## Important Dates

* **(31 January 2026)**
  * Paper submission deadline (23h59, Anywhere On Earth, i.e., UTC-12). 
  * **No extension will be given!**
* **(15 March 2026)**
  * Paper acceptance notification
* **(15 April 2026)**
  * Camera-ready papers
* **(21-26 June 2026)**  
  * IEEE WCCI 2026 @ Maastricht, NL: Tutorials (21 June), Industry Day (24 June), and Conference (22-26 June)
 
```{tip} Further details can be found at the [Official Website](https://attend.ieee.org/wcci-2026/important-dates-deadlines/)
```

(organisers)=
## Organisers

<div class="organiser-container">

```{image} ../../_static/images/jorge_cruz.png
:alt: Jorge M. Cruz-Duarte
:width: 150px
:class: no-scaled-link
```

<div class="organiser-bio">

**Jorge M. Cruz-Duarte** is a Postdoctoral Researcher at the Equipe de Recherche Bonus, Centre Inria de l'Université de Lille. His research spans neuromorphic computing, metaheuristic optimisation, and automated algorithm design and configuration. He chairs the IEEE Computational Intelligence Society Task Force on Automated Algorithm Design, Configuration and Selection, and serves as an IEEE Computer Society Distinguished Visitor (2025-2027).

[Email](mailto:jorge.cruz-duarte@univ-lille.fr) | [Website](https://jcrvz.co) | [Google Scholar](https://scholar.google.com/citations?user=jpu4kWUAAAAJ) | [DBLP](https://dblp.org/pid/178/0401.html)

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<div class="organiser-container">

```{image} ../../_static/images/el-ghazali_talbi.png
:alt: El-Ghazali Talbi
:width: 150px
:class: no-scaled-link
```

<div class="organiser-bio">

**El-Ghazali Talbi** is a full Professor at the University of Lille. His research interests include metaheuristics, computational intelligence, parallel and distributed optimisation, learning-based optimisation, and neuromorphic computing. He has authored more than 250 international publications, including journal and conference papers, and has delivered 52 keynotes and tutorials. With a h-index of 67 and over 24,000 citations, he is globally recognised for his contributions to computational intelligence and large-scale optimisation.

[Email](mailto:el-ghazali.talbi@univ-lille.fr) | [Website](https://pro.univ-lille.fr/el-ghazali-talbi) | [Google Scholar](https://scholar.google.com/citations?user=EwopEzgAAAAJ) | [DBLP](https://dblp.org/pid/74/3045.html)

</div>
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