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

About this episode

The episode discusses the extraction of spiking network models from experimental data with Richard Gao.

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.

People in this episode

Host: Gaute Einevoll

Guest: Richard Gao

Topics covered

• spiking network models
• brain activity
• experimental data
• model fitting
• neurobiology

Keywords

• spiking network models
• brain activity
• model parameters
• experimental data
• AutoMIND

Mentioned in this episode

Organizations: AutoMIND

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