Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.02.184333v1?rss=1 Authors: Saray, S., Ròˆssert, C. A., Appukuttan, S., Migliore, R., Vitale, P., Lupascu, C. A., Bologna, L. L., Van Geit, W., Romani, A., Davison, A. P., Muller, E., Freund, T. F., Kali, S. Abstract: Anatomically and biophysically detailed data-driven neuronal models can be useful tools in understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context. This limits the re-use and further development of the existing models, and thus prevents the building of consensus "community models" that could capture an increasing proportion of the electrophysiological properties of the given cell type. We addressed this problem for the representative case of the CA1 pyramidal cell of the rat hippocampus by developing an open-source Python test suite, which makes it possible to automatically and systematically test the generalization properties of models by making quantitative comparisons between the models and electrophysiological data. The tests cover various aspects of somatic behavior, and signal propagation and integration in apical dendrites. To demonstrate the utility of our approach, we applied our validation tests to compare the behavior of several different hippocampal CA1 pyramidal cell models from the ModelDB database against electrophysiological data available in the literature, and concluded that all of these models perform well in some domains but badly in others. We also show how we employed the test suite to aid the development of models within the European Human Brain Project (HBP), and describe the integration of the tests into the validation framework developed in the HBP, with the aim of facilitating more reproducible and transparent community model building. Copy rights belong to original authors. Visit the link for more info