On reproducibility of modeling and 10 years with the Potjans-Diesmann network model - with Hans Ekkehard Plesser - #37
About this episode
The episode discusses the importance of reproducibility in scientific research, particularly in computational neuroscience, featuring guest Hans Ekkehard Plesser.
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.
People in this episode
Host: Gaute Einevoll
Guest: Hans Ekkehard Plesser
Topics covered
- reproducibility
- computational neuroscience
- modeling
- neural networks
- scientific progress
- standardized model description
Keywords
- reproducibility
- computational neuroscience
- Potjans-Diesmann
- neural network model
- scientific progress
- model sharing
- standardized model description
Mentioned in this episode
Organizations: Potjans-Diesmann, Theoretical Neuroscience Podcast
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- On extracting spiking network models from experiments - with Richard Gao - #38
- On low-dimensional manifolds in motor cortex - with Sara Solla - #36
- On modeling metabolic networks in the brain – with Polina Shichkova - #35
- On balanced neural networks - with Nicolas Brunel - #34
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