Reproducibility and Rigor in Computational Neuroscience: Testing the Data Driven Model
Computational models provide a framework for integrating data across spatial scales and for exploring hypotheses about the biological mechanisms underlying neuronal and network dynamics. However, as models increase in complexity, additional barriers emerge to the creation, exchange, and re-use of models. Successful projects have created standards for describing complex models in neuroscience and provide open source tools to address these issues. In spite of this promising movement toward model sharing and reproducibility in the neuroscience community, it is extremely rare to see a specific, rigorous statement of the criteria used for evaluating models during model development, and multiple models for the same ion channels and neurons are not compared for concordance with the same suite of experimental data. We have developed a flexible infrastructure for assessing the scope and quality of computational models in neuroscience. The goal of our validation approach is to integrate experimental data with modeling efforts for more efficiency, better transparency, and greater impact of computational models in neuroscience research. 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.
About the Speaker
Sharon Crook earned her master's degree and Ph.D. in applied mathematics from the University of Maryland in 1991 and 1996, respectively. She performed her dissertation research with John Rinzel at the Mathematical Research Branch of the National Institutes of Health, where she developed coupled oscillator models for cortical dynamics in collaboration with Bard Ermentrout at the University of Pittsburgh. She then held a postdoctoral appointment at the Center for Computational Biology at Montana State University with John Miller and Gwen Jacobs, where she did joint work in neurophysiology, modeling and neuroinformatics.
Crook now holds a joint appointment as professor in the School of Mathematical and Statistical Sciences and the School of Life Sciences at Arizona State University. There, she uses computational approaches to study the dynamics of neurons and neuronal networks and the mechanisms underlying plasticity due to trauma, learning or disease. Crook also contributes to the development of NeuroML, an international effort to create a common standard for describing computational models for neuroscience research.