Dynamical Systems podcasts

Daniel Levenstein discusses spontaneous activity in the hippocampus and cortex, especially during sleep, and its relationship to learning, memory and navigation. He explains how AI models can help investigate these dynamics, alongside questions about model systems, cognitive maps and the relationship between neuroscience experiments and theory.

Hosted by Paul Middlebrooks

Holly Krieger discusses arithmetic dynamics: how repeatedly applying simple mathematical rules connects number theory with dynamical systems. The conversation explores the patterns and difficult questions that arise when numbers and iteration meet, providing an accessible route into an active area of mathematical research.

Ehud Ahissar explains active perception through closed loops linking movement and sensation, drawing on work with rodent whiskers. He introduces perceptual dualism as a proposed distinction between communication within the brain and interactions with the world, and explores its implications for consciousness and neural dynamics.

Hosted by Paul Middlebrooks

Liset de la Prida discusses hippocampal sharp-wave ripples and their relationship to replay and memory. The conversation connects different ripple patterns and neuron types to population activity, asking how particular cells help shape the lower-dimensional dynamics used to describe neural computation.

Hosted by Paul Middlebrooks

Tilo Schwalger discusses population-level descriptions of neural dynamics and how mean-field models can be derived from underlying network mechanisms.

Nicolas Brunel discusses excitation-inhibition balance and mathematical explanations of irregular activity in cortical networks.

Tim Vogels discusses the interaction of excitatory and inhibitory plasticity and its implications for learning and stable network activity.

Viktor Jirsa discusses linking brain models across scales and using individualized whole-brain simulations in the Virtual Brain Twin project.

Henrik Lindén discusses spinal circuits that generate rhythmic movement and the models used to understand their activity.

Eric Shea-Brown joins Paul Middlebrooks to examine what dynamical systems and dimensionality reveal about neural computation. They compare task-related activity with less constrained behaviour, consider how network connection motifs shape collective dynamics, and discuss high-dimensional activity embedded within lower-dimensional descriptions. Research examples connect predictive learning, latent representations and rich versus lazy learning regimes. The conversation also examines how theoretical neuroscientists choose useful abstractions and which assumptions about network structure and function deserve closer scrutiny.

Hosted by Paul Middlebrooks