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Updated: Oct 25, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Generative adversarial networks unlock new methods for cognitive science
Lore Goetschalckx1, Alex Andonian2, Johan Wagemans3
1Department of Brain and Cognition, KU Leuven, 3000 Leuven, Belgium; Carney Institute for Brain Science, Department of Cognitive Linguistic & Psychological Sciences, Brown University, Providence, RI 02912, USA.
Abstract:
Generative adversarial networks (GANs) enable computers to learn complex data distributions and sample from these distributions. When applied to the visual domain, this allows artificial, yet photorealistic images to be synthesized. Their success at this very challenging task triggered an explosion of research within the field of artificial intelligence (AI), yielding various new GAN findings and applications. After explaining the core principles behind GANs and reviewing recent GAN innovations, we illustrate how they can be applied to tackle thorny theoretical and methodological problems in cognitive science. We focus on how GANs can reveal hidden structure in internal representations and how they offer a valuable new compromise in the trade-off between experimental control and ecological validity.
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