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.
Links:
- Potjans & Diesmann: “The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model”, Cerebral Cortex (2014)
- Plesser et al.: “Building on models—a perspective for computational neuroscience”, Cerebral Cortex (2025)
- Crook, Davison & Plesser: “Learning from the past: Approaches for reproducibility in computational neuroscience”, Springer Series in Computational Neuroscience (2013)
- Plesser: “Reproducibility vs. Replicability: A Brief History of a Confused Terminology”, Frontiers in Neuroinformatics (2018)
- Crook, Davison, McDougal, Plesser: “Editorial: Reproducibility and Rigour in Computational Neuroscience”, Frontiers in Neuroinformatics (2020)
- Senk et al.: “Constructive community race: full-density spiking neural network model drives neuromorphic computing”, arXiv (2025)
- Einevoll et al.: “The Scientific Case for Brain Simulations”, Neuron (2019)
- OpenSourceBrain
- ModelDB
- EBRAINS
- Homepage of Hans Ekkehard Plesser
The podcast was recorded on December 12th, 2025 and lasts 1 hour and 28 minutes.
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