Reproducibility and rigor in computational neuroscience: testing the data driven model

Sharon Crook, Arizona State University, present 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.

By INCF
Published Oct, 2018

Description

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. A flexible infrastructure for validation of models in neuroscience has developed with the goal of integrating experimental data with modeling efforts for greater efficiency, transparency, and impact of computational models.
Here, Crook 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.