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

Abstract

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.

DNA Methylation Biomarkers-Based Human Age Prediction Using Machine Learning

“…After preprocessing, both datasets are passed to machine learning pipelines. StandardScaler is used to scale all the features; standardized values are useful for tracking the data, which are difficult to compare otherwise due to different magnitudes, metrics, or circumstances [ 30 ]. The Python sklearn package is used to create machine learning pipelines; these pipelines are an ensemble of several transformers with a final estimator [ 26 ].…”

“…After preprocessing, both datasets are passed to machine learning pipelines. StandardScaler is used to scale all the features; standardized values are useful for tracking the data, which are difficult to compare otherwise due to different magnitudes, metrics, or circumstances [30].…”

The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models

“…The NFE exploits the BluePyEfe and the eFEL Python libraries (see section “Methods”) by hiding from the user the technical details of configuration file writing and data management (as required when both tools are run in a standalone manner on a local machine) and exposing a user-friendly point-and-click interface, instead. The models available for the optimization are fetched from the EBRAINS Model Catalog (see Table 1 ), which provides details and links related to the modeling work carried out in the framework of the HBP/EBRAINS research infrastructure as well as results concerning the validation of models against experimental observations ( Sáray et al, 2021 ). A tight integration is also in place between the HHNB and the HPC systems available for job optimization: via the UNICORE Python library or dedicated APIs for the interaction with the CSCS-DAINT system and the NSG, respectively, and using an intuitive web interface, the HHNB seamlessly allows to authenticate to the remote platforms, configure the job execution files and fetch the optimization results.…”

Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework

“…This approach based on feature statistics is now supported by Neuroptimus. To provide access to a diverse array of electrophysiological features, and ensure compatibility with some common workflows [5,7,44,45], Neuroptimus utilizes the Electrophys Feature Extraction Library (eFEL; https://github.com/BlueBrain/eFEL) [27] to characterize the voltage responses of the models. The target data in this case contain the experimental mean and standard deviation values of a predefined set of eFEL features extracted from voltage responses to specific current step inputs, stored in a JSON file created from the recordings using the BluePyEfe tool (https://github.com/BlueBrain/BluePyEfe) and a custom script that converts the output of BluePyEfe to the format expected by Neuroptimus.…”