Statistical Physics podcasts

Stanford researcher Surya Ganguli connects statistical physics, theoretical neuroscience and machine learning. He discusses scaling laws, data selection and the origins of diffusion models, then turns to neural experiments on perception, self-related processing and describing neuronal responses. The conversation closes with questions about human versus machine data efficiency and the different ways brains and artificial networks acquire useful algorithms. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Hosted by Ravid Shwartz Ziv, Allen Roush

Codina Cotar discusses mathematical patterns associated with stochastic systems and anomalous diffusion, using entries in a science-and-art competition to explain the ideas. The conversation connects diffusion with familiar materials, quantum phenomena, movement and visual art, and examines how mathematical models inspire creative interpretations of nature.

Woodrow Shew and Keith Hengen assess whether critical dynamics form a homeostatic target for brain activity. They discuss proposed signatures and testable predictions, controversies in earlier studies, and possible relationships among criticality, sleep and neural manifolds, drawing on their review of the experimental literature.

Hosted by Paul Middlebrooks

John Beggs reviews the critical-brain hypothesis and the proposal that neural activity operates near a transition between ordered and chaotic regimes. He discusses information processing, dynamic range, measurement methods, experimental evidence, criticisms and the question of whether criticality is a distinctive computational property of brains or part of a broader natural phenomenon.

Hosted by Paul Middlebrooks