HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data

PLoS Comput Biol. 2021 Jan 29;17(1):e1008114. doi: 10.1371/journal.pcbi.1008114. eCollection 2021 Jan.


Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for 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. In addition, there is currently no simple way of quantitatively comparing different models regarding how closely they match specific experimental observations. This limits the evaluation, re-use and further development of the existing models. Further, the development of new models could also be significantly facilitated by the ability to rapidly test the behavior of model candidates against the relevant collection of experimental data. We address these problems 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 multiple 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 tests to compare the behavior of several different rat hippocampal CA1 pyramidal cell models from the ModelDB database against electrophysiological data available in the literature, and evaluated how well these models match experimental observations in different domains. 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 model building in the neuroscience community.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • CA1 Region, Hippocampal* / cytology
  • CA1 Region, Hippocampal* / physiology
  • Computational Biology
  • Dendrites / physiology
  • Electrophysiological Phenomena / physiology*
  • Electrophysiology / methods*
  • Models, Neurological*
  • Pyramidal Cells / cytology
  • Pyramidal Cells / physiology
  • Rats
  • Software*

Grants and funding

This project received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 720270 and No. 785907 (Human Brain Project SGA1 and SGA2). SS has been supported by the ÚNKP-19-3-III New National Excellence Program of the Ministry For Innovation and Technology (Hungary), and the European Union, co-financed by the European Social Fund (EFOP-3.6.3-VEKOP- 16-2017-00002). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.