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Poster

From manifolds to thoughts

MPG-Autoren
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Beylier,  Charlotte
Center for Scalable Data Analytics and Artificial Intelligence, Leipzig;
Method and Development Group Neural Data Science and Statistical Computing, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Scherf,  Nico
Method and Development Group Neural Data Science and Statistical Computing, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Doeller,  Christian F.
Department Psychology (Doeller), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Menghi,  Nicholas
Department Psychology (Doeller), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Zitation

Beylier, C., Scherf, N., Doeller, C. F., & Menghi, N. (2022). From manifolds to thoughts. Poster presented at 11th IMPRS NeuroCom Summer School, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.


Zitierlink: https://hdl.handle.net/21.11116/0000-000B-1B49-6
Zusammenfassung
Advances in Machine Learning (ML) models have surpassed our ability to understand the inner “thinking process” that led to their results, leaving us with the same interrogations we ask to understand their biological counterpart, the brain. Here, the geometry of collective neural representations seems to be a crucial characteristic of information processing in both artificial and natural neural networks (NN). However, the dynamical aspect of these neural maps and the cognitive processes that operate on them are still largely unexplored.