
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.
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On functional effects of neuronal heterogeneity - with David Dahmen - #41

On smelling your way to the fruit with ring models - with Katherine Nagel - #40

On modeling neural population activity with mean-field models - with Tilo Schwalger - #39

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