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

Area of Science:

• Computational Neuroscience
• Systems Neuroscience
• Neuroscience Research Tools

Background:

• Brain modeling is crucial for understanding neural mechanisms and information processing.
• Building data-driven models typically requires integrated experimental data, analysis tools, simulation environments, and high-performance computing.
• Existing workflows are often fragmented, necessitating custom programming and hindering reproducibility.

Purpose of the Study:

• To develop a comprehensive framework for building data-driven single-cell neural models.
• To address the fragmentation and complexity in current brain modeling workflows.
• To enhance the reproducibility and collaborative nature of computational neuroscience.

Main Methods:

• Developed the EBRAINS Hodgkin-Huxley Neuron Builder (HHNB), a web resource.
• Implemented automated extraction of activity features from electrophysiological traces.
• Utilized a genetic algorithm executed on high-performance computing for model parameter optimization.
• Integrated an interactive framework for simulating the optimized neural models.

Main Results:

• The HHNB facilitates the data-driven model building workflow.
• The tool enables efficient parameter optimization and model simulation.
• The platform enhances the reproducibility of neural model construction.

Conclusions:

• The HHNB provides a streamlined and accessible approach to creating single-cell neural models.
• This resource fosters a more collaborative and reproducible environment for computational neuroscience.
• The HHNB simplifies the integration of experimental data into computational brain models.