Dynamics-informed priors (DIP) for neural mass modelling
Alessia Caccamo, Dominic M. Dunstan, Mark P. Richardson, Alexander D. Shaw, Marc Goodfellow · 2026
Read Dynamics-informed priors (DIP) for neural mass modelling by Alessia Caccamo, Dominic M. Dunstan, Mark P. Richardson, Alexander D. Shaw, Marc Goodfellow (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13224311.txt.
Description
Alessia Caccamo and colleagues propose a 2026 improved method for neural mass models, which give a simple account of how large groups of brain cells behave. Fitting such models to brain recordings depends heavily on prior assumptions about its settings, which are hard to measure directly. Poor priors can bias the whole inference.
Their method first uses a genetic algorithm, a search process modelled on evolution, to find value regions that produce realistic model behaviour. Those regions then become informed priors for standard Bayesian fitting. Tested on two independent brain-recording datasets, the combined approach predicted data better than either method alone. It also recovered known signatures of psychiatric illness and drug effects.
Catalog record
| CREATOR | Alessia Caccamo, Dominic M. Dunstan, Mark P. Richardson, Alexander D. Shaw, Marc Goodfellow |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 61 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |