SYSTEMATIC TESTING AND VALIDATION OF MODELS OF HIPPOCAMPAL NEURONS AGAINST ELECTROPHYSIOLOGICAL DATA
Sára Sáray1, 2, Christian Rössert3, Shailesh Appukuttan4, Andrew Davison4, Eilif Muller3, Tamás Freund1, 2, Szabolcs Káli1, 2
1 Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary; 2Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary; 3Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland; 4Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique/Université Paris-Sud, Gif-sur-Yvette, France.
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 growing number of computational and software tools that enable the building of models, and the increasing body of experimental data from electrophysiological measurements that describe the behavior of real neurons and thus constrain the parameters of detailed neuronal models, there are now a large number of different models of many cell types available in the literature that were developed using different methods and for different purposes. These models were usually built to capture some important or interesting properties of the given neuron type, i.e., to reproduce the results of a few selected experiments and it is often unknown, how they would behave outside their original context. Nevertheless, for data-driven models to be predictive, it is important that they are able to generalize beyond their original scope. To make it possible to automatically and systematically test the generalization properties of models of hippocampal neurons and make quantitative comparisons between the models and electrophysiological data we are developing an open-source Python test suite, HippoUnit (https://github.com/KaliLab/hippounit) that has been integrated into the Brain Simulation Platform and the Validation Framework of the Human Brain Project. Acknowledgments: Supported by the ÚNKP-19-3-III New National Excellence Program of the Ministry for Innovation and Technology; European Social Fund (EFOP-3.6.3-VEKOP-16-2017-00002); the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 720270, 785907 (Human Brain Project SGA1, SGA2).