Computational Neuroscience podcasts

Gaute Einevoll and Mihai Petrovici discuss how neuromorphic hardware can support artificial intelligence and efficient brain simulation. They contrast high-frequency arithmetic on conventional silicon with the rich dynamics and millisecond spikes of biological neurons, and explore brain-inspired systems for emulating neural dynamics and studying computation in spiking networks. Petrovici is a senior researcher at the University of Bern. The conversation was recorded on 18 June 2026 and released on 23 September 2026; the publisher’s audio runs 1 hour, 41 minutes and 46 seconds.

Hosted by Gaute Einevoll

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

Gaute Einevoll talks with Tatjana Tchumatchenko about a mechanistic mathematical model of how neurons minimize energy use by deciding whether ion-channel proteins are produced locally in dendrites or in the soma. The model’s predictions agree with experimental findings.

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

Ad Aertsen reflects on Valentino Braitenberg's contributions to neuroscience, including anatomy, synthetic psychology and theories of neural computation.

Timothy O'Leary discusses how neurons preserve functional identity despite molecular turnover and how related processes may contribute to changing representations.

Cristopher Moore connects computational complexity to questions about cognition and artificial intelligence. The discussion considers what makes a problem difficult, how rugged landscapes shape computation, and what complexity theory can contribute to understanding learning and generalization in brains and machines.

Hosted by Paul Middlebrooks

David Dahmen examines how differences between individual neurons affect network function and when homogeneous models miss important behaviour.

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

Katherine Nagel discusses how fruit flies navigate using odour and how ring models can explain the short-term memory involved.

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

Romain Brette questions familiar computational and information-processing metaphors for the brain. Starting from behavior in organisms such as paramecia, he considers cognition as an activity of living systems, the autonomy of interacting cells, and the distinction between anticipation and prediction, with implications for artificial intelligence.

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.

Richard Gao discusses fitting spiking-network models to experimental data and dealing with different parameter combinations that explain similar observations.

Hans Ekkehard Plesser discusses reproducibility in computational research through a decade of work with the Potjans-Diesmann cortical network model.

Sara Solla discusses how low-dimensional descriptions can reveal structure in recordings from large populations of motor-cortex neurons.

Polina Shichkova discusses models of brain metabolism, including energy supply and the cellular processes that maintain neuronal function.

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

Anthony Burkitt, Nada Yousif and Esra Neufeld discuss how computational research can inform neurotechnology for clinical use.

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