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

Page: https://stenobird.com/podcast/theoretical-neuroscience-podcast-6657149/on-extracting-spiking-network-models-from-experiments-with-richard-gao-38
Text version: https://stenobird.com/podcast/theoretical-neuroscience-podcast-6657149/on-extracting-spiking-network-models-from-experiments-with-richard-gao-38.md
Podcast: [Theoretical Neuroscience Podcast](https://stenobird.com/podcast/theoretical-neuroscience-podcast-6657149)
Published: 2026-02-28T06:00:00+00:00
Episode link: https://431c9cd0-a1ff-4f9a-ac41-4fca68240d88.libsyn.com/on-extracting-spiking-network-models-from-experiments-with-richard-gao-38
Audio file: https://traffic.libsyn.com/secure/431c9cd0-a1ff-4f9a-ac41-4fca68240d88/ThN-038-RichardGao_mixdown.mp3?dest-id=4181327
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JSON: https://stenobird.com/v1/public/podcasts/theoretical-neuroscience-podcast-6657149/episodes/on-extracting-spiking-network-models-from-experiments-with-richard-gao-38
Duration seconds: 5734

## Resource

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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