Due to a fire at the Almere datacenter, digital access to UU buildings will not be possible tomorrow. As a result, Utrecht University will be closed.
The talk by Elena Agliari will be moved to Leiden University. The talk by Edan Lerner (UvA) will be postponed to next season.
Time: 14:00–16:00
Location: Leiden University, Gorlaeus Building, BM.2.26
The Hopfield Model: From Disordered Systems to Biological Memory and Machine Learning.
Introduced as a model of associative memory, the Hopfield model has become a paradigmatic framework at the interface of statistical physics, theoretical neuroscience, and machine learning. Its simple definition already captures key features of high-dimensional systems with quenched disorder, frustration, and emergent collective behavior, making it a natural benchmark for both physical and computational questions. From the neuroscience perspective, biologically motivated mechanisms of learning and memory have long inspired algorithmic developments, and the Hopfield model provides a natural setting in which such ideas can be analyzed in a mathematically controlled way. At the same time, its formal equivalence with Boltzmann machines connects it to several themes in machine learning, including representation learning, regularization strategies, and energy-based optimization. From the viewpoint of statistical mechanics, the Hopfield model remains a fundamental laboratory for rigorous and heuristic methods developed for spin glasses and random systems. These methods can also be extended beyond the classical setting to structured or correlated data, where new phenomena arise.