2nd workshop on symmetry, invariance and neural representations


Arianna Di Bernardo | Ecole Normale Supérieure, Paris, France
Simone Azeglio | Vision Institute, Sorbonne University / Ecole Normale Supérieure, Paris, France


The second workshop on symmetry, invariance, and nneural representations at the Bernstein Conference 2023 seeks to encourage interdisciplinary research at the intersection of mathematics and neuroscience. The workshop will emphasize the significance of symmetries in the structure and function of the brain and present the latest research on neural population geometry, neural manifolds, embeddings of neural data, and invariant/equivariant neural representations in both biological and artificial networks. By incorporating geometric and topological features, along with symmetry, into the design of neural architectures, researchers can develop more interpretable and trainable models, leading to a more profound comprehension of the brain and its complexities. This ongoing research area has the potential to transform our understanding of neural computation and information processing, opening doors to more robust and efficient neural models. Building on the feedback and interaction with peers from the previous year, the second edition of this workshop will bring together researchers and students from various fields to promote cooperation and push forward this exciting research area.

Schedule (CEST)

Tuesday, Sept 26


Introduction and initial remarks
Simone Azeglio, Arianna Di Bernardo


James Whittington | Stanford University, USA & Oxford University, UK
Compositional representations for flexible behavior


Devika Narain | Erasmus University Medical Center, Netherlands
Unsupervised manifold learning through low distortion Riemannian alignment of tangent spaces


Alex Williams | New York University & Flatiron Institute, USA
Shape metrics: a framework to quantify similarity in neural manifolds


30 min coffee break


Pierre-Etienne Fiquet | New York University, USA
Neural representation for predictive processing of dynamic visual signals


Alex Cayco-Gajic | Ecole Normale Supérieure, France
The learning dynamics of neural manifolds


Kristopher Jensen | University of Cambridge, UK
Tutorial: Studying the geometry and topology of neural population recordings with probabilistic generative models


End of first day

Wednesday, Sept 27


Introduction to day 2
Simone Azeglio, Arianna Di Bernardo


Hanspeter Mallot | University of Tübingen, Germany 
The Structure of Spatial Memory: From Topological Maps to Metric Graphs


Joanna Chang | Imperial College London, UK
Structure in neural activity space driving motor learning and adaptation


Adam Gosztolai | Swiss Federal Institute of Technology in Lausanne, Switzerland
Interpretable statistical representations of neural population dynamics and geometry 


30 min coffee break


Misun Kim | Max Planck Institute for Human Cognitive and Brain Sciences, Germany 
Navigating a non-Euclidean world: Human path integration on a sphere


Benjamin Dunn | Norwegian University of Science and Technology, Norway
Developmental timeline of the grid cell torus


Round table discussion
All workshop speakers