Satellite Events
Date: August 11, 2025
Location: University of Amsterdam, REC-A building (main CCN 2025 conference venue)
Computational insights into clinical disorders
11:30 am - 2:30 pm - Room A2.11
Organizers: Marcus Daghlian, Serge Dumoulin, Frans Cornelissen
This session explores cutting-edge computational approaches to capture visual properties using functional MRI in disorders of neurology (Haak) and ophthalmology (Daghlian). Next, we examine how computational approaches extend to visual behaviour (Schulte) and the auditory system, e.g., tinnitus (Moerel). Last, we examine how computational models provide insights into sight recovery using brain computer interfaces (Klink) and gene therapy (Dekker).
Recurrent neural networks making decisions
11:30 am - 2:30 pm - Room A2.09
Organizers: Manuel Molano-Mazón & Jorge Mejias
Advances in artificial intelligence and computational neuroscience are transforming our understanding of decision-making processes in the brain. This workshop will bring together experts and researchers to discuss cutting-edge methodological innovations at the intersection of neuroscience, cognitive science, and AI. We aim to explore how novel computational approaches can enhance our ability to study and model decision-making. Confirmed speakers include Chris Summerfield, Joao Barbosa, Giulia Crocioni, Manuel Molano-Mazón, and Jorge Mejias
From Child to Machine Learning: The potential and challenges of translating Developmental Principles into Neural Network Design
3:00 - 6:00 pm - Room A2.11
Organizers: Tessa Dekker, Lukas Vogelsang & H.Steven Scholte
This workshop bridges human cognitive development principles with AI visual learning systems. Starting with an overview of developmental psychology in neural network design, we'll examine curriculum design for structuring training data, architectural constraints that reflect developmental limitations, and evaluation metrics for measuring human-like learning trajectories. Through collaborative break-out sessions and a final panel discussion, participants will develop interdisciplinary research agendas aimed at creating neural networks that learn more like humans, revealing insights into efficient learning with minimal data.
Biologically Plausible Learning
3:00 - 6:00 pm - Room A2.09
Organizers: Sander Bohte & Marcel van Gerven
The workshop aims to bring together researchers working on neural learning algorithms that have increased biological plausibility, such as local approximations of error-backpropagation and/or alternative learning schemes that are compatible with what is known about learning in the brain. The objective of the workshop is to highlight current developments and state-of-the-art, and also limitations. The target audience are computational and theoretical neuroscientists working on the edge of AI, and vice versa.
Computational cognitive neuroscientists for social good
3:00 - 6:00 pm - Room A1.02
Organizers: Weiji Ma, Jessica Thompson, Anne Urai
We live in times when public trust in science is under threat, misinformation is rife, AI ethics are of increasing concern and climate change is increasingly affecting communities worldwide. How do computational social neuroscientists see ourselves and our role in this world, and can we use our skills for social good? This session will explore ways to to extend the impact of our scientific work beyond our laboratories and models, and will critically evaluate how to fulfil our responsibility as scientists during times of significant social and technological change.
Modeling the Physical Brain: Spatial Organization and Biophysical Constraints
11:30 am - 6:00 pm - Room A2.07
Organizers: Atlas Kazemian, Yash Shah, Johannes Mehrer, Dan Yamins, Martin Schrimpf
Many recent computational models of the brain address “functional” features of neuronal activity – that is, information-processing patterns of units in the system, treated as abstracted function of stimulus input or a time variable. However, the real brain is a physical device embedded in space, exhibiting reliable spatial organization, strongly constrained by biophysical requirements, and subject to substantial size, weight, and power limitations. Recent work in NeuroAI has begun to address these key facts, leading to an array of exciting theoretical modeling approaches to the brain as a biophysical system; exposing a set of new and unsolved empirical questions; and enabling a spectrum of potentially high-impact real-world neural applications. This symposium will focus on each of these components, including cutting-edge presentations on theory, experiment and application; and across a spectrum of brain areas and systems.
The Metacognitive Science Meeting
11:30 am - 6:00 pm - Room A1.03
Organizers: Megan Peters, Steve Fleming, Doby Rahnev, Lucie Charles
The Metacognitive Science Meeting is a venue for interdisciplinary discussions around all aspects of metacognition, cross-cutting psychology, neuroscience, philosophy and computer science. Our community represents an entirely new model of scientific community building: a roving satellite meeting, designed to approach and synergistically mingle with relevant scientific communities across philosophy, psychology, cognitive science, neuroscience, and artificial intelligence on an annual cycle. Registration is required.
Modelling Emotion and Morality in Brain and Machine
11:30 am - 6:00 pm - Room A2.12
Organizers: Philip Kragel & Frederic Hopp
Understanding the nature and structural organization of emotion and morality has been a central aim for social, cognitive, and affective neuroscience. Decades of research have shown that moral and affective processes are critical for learning, memory, decision-making, and social interactions. Although theorists have long argued that emotions inform moral judgments, few neuroscientific studies have modelled or demonstrated how specific emotions (e.g., anger, disgust, compassion) undergird moral judgments of specific moral domains (e.g., harm, purity, fairness). Moreover, active debates in both affective and moral science independently continue to discuss the dimensional versus categorical basis of (moral) emotions, although collaborative efforts across both fields likely have great potential to jointly advance our understanding of the human brain. Critically, the adoption of computational models and multivariate pattern analysis in these fields provides a unified framework to compare and contrast the representational structure of emotion and morality across minds, brains, and machines.
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