Ponente: Nina Miolane
Institución: Universidad de California, Santa Barbara
01/04/2025 de 12:00 a 13:00
Dónde Auditorio "Alfonso Nápoles Gándara"
Neural representations in both biological and artificial neural networks reveal rich and intricate geometric structures. From the toroidal neural manifolds that shape navigation in animals and recurrent neural networks to the complex topological interactions among neurons in brains and machines, intelligence emerges within a landscape of geometries we are only beginning to explore. Even the higher layers of neural networks in computer vision—and the visual cortex itself—exhibit striking symmetries that challenge our understanding of learning and perception. In this talk, we delve into the mathematical foundations of neural representations and showcase ongoing research at the Geometric Intelligence Lab. By bridging geometry, neuroscience, and machine learning, we aim to expand the frontiers of how we understand and model intelligence—both artificial and biological—advancing fundamental science and transforming brain research and care.