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Theoretical Neuroscience Podcast
VetenskapLife Science

On extracting spiking network models from experiments - with Richard Gao - #38

1 tim 36 min•28 februari 2026

While some models aim to explain qualitative features of brain activity, other aim to reproduce experimental data quantitatively. If so, model parameters must be adjusted to make the model predictions fit the experimental data.

A complication is that in most neurobiological applications, there is not a unique best fit: many parameter combinations give equally good model fits.

Recently, the guest, together with colleagues, made the tool AutoMIND to fit spiking network models to data.

 

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On modeling neural population activity with mean-field models - with Tilo Schwalger - #39
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On reproducibility of modeling and 10 years with the Potjans-Diesmann network model - with Hans Ekkehard Plesser - #37
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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
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On computational neurotechnology for the clinic - with Anthony Burkitt, Nada Yousif & Esra Neufeld - #33
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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
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