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Theoretical Neuroscience Podcast
VetenskapLife Science

On reproducibility of modeling and 10 years with the Potjans-Diesmann network model - with Hans Ekkehard Plesser - #37

1 tim 29 min•31 januari 2026

Reproducibility is key for scientific progress. If research results cannot be reproduced and trusted, other researchers cannot build on them.

Reproducibility is a challenge also in computational neuroscience, and today's guest has worked on how this can be remedied, for example, through standardized model description and model sharing.

He also recently organised a workshop celebrating a decade with the (reproducible) Potjans-Diesmann neural network model, which has become an important community tool.

 

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On modeling neural population activity with mean-field models - with Tilo Schwalger - #39
•2 tim 19 min
On extracting spiking network models from experiments - with Richard Gao - #38
•1 tim 36 min
On low-dimensional manifolds in motor cortex - with Sara Solla - #36
•2 tim 5 min
On modeling metabolic networks in the brain – with Polina Shichkova - #35
•1 tim 32 min
On balanced neural networks - with Nicolas Brunel - #34
•1 tim 39 min
On computational neurotechnology for the clinic - with Anthony Burkitt, Nada Yousif & Esra Neufeld - #33
•1 tim 1 min
On IIT and adversarial testing of consciousness theories - with Christof Koch - #32
•2 tim 18 min
On how to cure brain diseases - with Nicole Rust - #31
•2 tim 13 min
On co-dependent excitatory and inhibitory plasticity - with Tim Vogels - #30
•1 tim 31 min
On the philosophy of simplification in computational neuroscience - with Mazviita Chirimuuta and Terrence Sejnowski - #29
•1 tim 24 min

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