Starting with the work of pioneers like Wilson and Cowan in the 1970s, mean‑field models have become a dominant tool for modeling neural activity at the level of neuronal populations.

Despite their popularity, most mean‑field models have been heuristic and not systematically derived from the underlying 'microscopic' dynamics of individual neurons.

Today's guest has made important contributions towards remedying this situation.

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.

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.