10th Annual Conference
Cognitive Computational Neuroscience
July 26-29, 2027
Edinburgh, UK
Join Us for CCN 2027 in Edinburgh!
The 10th annual conference on Cognitive Computational Neuroscience will be held Edinburgh, UK from Monday, July 26 through Thursday, July 29, 2027.
Important Dates
February 1, 2027 - 8-page Proceedings Abstract Deadline
February 4, 2027 - 8-page Proceedings Full Paper Deadline
March 18, 2027 - Event submission deadline (e.g., GACs, Community Events)
April 1, 2027 - 2-page Extended Abstract Submission Deadline
May 20, 2027 - Early Registration Deadline
July 26-29, 2027 - CCN 2027, Edinburgh International Conference Center
Additional details and a more comprehensive Dates and Deadlines schedule will be available soon. Please check back for updates.
Please note: all deadlines are end of day AoE (Anywhere on Earth, UTC-12)
About Edinburgh
Scotland's capital is a city where centuries of history meet a thriving modern culture. Set between dramatic volcanic hills and the sea, Edinburgh is one of Europe's most walkable and visually striking cities. Home to world-class museums, a renowned food and drink scene, and the Fringe — the largest arts festival on the planet and conveniently starting shortly after CCN 2027 concludes. Edinburgh is well-connected by international flights and rail, making it an easy destination for attendees to travel from abroad.
Edinburgh has been a centre for research into minds, brains, and intelligent systems for more than sixty years. From early foundational work in AI and machine learning at the University of Edinburgh — one of the first universities in the world to establish a department of artificial intelligence — to its continued strength in AI, robotics, cognitive science, and neuroscience, the city is a fitting home for CCN. Within commuting distance of Edinburgh, Scotland harbors a large community of Cognitive Computational Neuroscience researchers at Heriot-Watt University, Edinburgh Napier University, the University of Glasgow, University of St Andrews, and the University of Stirling.
About the Conference
CCN is an annual forum for discussion among researchers in cognitive science, neuroscience, and artificial intelligence, dedicated to understanding the computations that underlie complex behavior. The conference began in 2017, with a goal to deepen interactions between these disciplines and to discover ways that the communities can benefit one another and leverage each other’s successes, articulated in this TICS commentary paper.
The conference is primarily single-track featuring keynote speakers and oral presentations. Paper submissions are presented as posters with a few additionally selected for oral presentations. Community-proposed programming happens in single-track and parallel sessions, including "GACs", "K&Ts", and other community events. Generative Adversarial Collaborations (GACs), are symposia designed to clarify theoretical debates and scaffold forward progress. Keynote-and-Tutorial presentations (K&Ts) foster science and skill-building, presenting cutting-edge science as a talk, followed by the code and a tutorial of how to execute those methods. Open events are designed to welcome all creative ideas for community building, skill building, science exchange, mentorship and career development. We aspire to have an active, open, and responsive culture to meet the needs of this dynamic growing field.
We encourage participation from experimentalists and theoreticians investigating complex brain computations in humans and animals. CCN will draw researchers that address challenges including (and not limited to):
- Understanding brain information processing underlying real-world tasks that involve natural stimuli, rich knowledge, complex inferences, and behavior
- Measuring and expanding the representational competencies of modern AI systems
- Understanding commonalities and differences between biological and artificial intelligent systems
- Using techniques from machine learning and artificial intelligence to model brain information processing, and, conversely, incorporating neurobiological principles in machine learning and artificial intelligence
- Mechanistic interpretability of deep neural network models and the science of deep learning
- Revealing principles of brain connectivity and dynamics at multiple scales
- Using psychophysical techniques to relate sensory inputs to behavioral responses
- Developing cognitive- or neural-level models of perception, cognition, emotion, and action
- Representation learning and representational alignment