Sharon Crook

Arizona State University


Reproducibility and Rigor in Computational Neuroscience: Testing the Data Driven Model
As computational models in neuroscience increase in complexity, there are additional barriers for their creation, exchange, and re-use. Successful projects have created standards and open source tools to address these issues, but specific, rigorous criteria for evaluating models against experimental data during model development remain rare. We have developed a flexible infrastructure for validation of models in neuroscience with the goal of integrating experimental data with modeling efforts for greater efficiency, transparency, and impact of computational models.
Here I provide an overview of these projects and make a case for expanded use of resources in support of reproducibility and validation of models against experimental data.