GSoC 2022 Project Idea 13.1: Integration of automated model testing and parameter fitting tools for neuroscience applications (350 h)

Biologically detailed models are useful tools in neuroscience, and automated methods are now routinely applied to construct and validate such models based on the relevant experimental data. The open-source parameter fitting software Neuroptimus (formerly Optimizer) was developed to enable the straightforward application of advanced parameter optimization methods (such as evolutionary algorithms and swarm intelligence) to various problems in neuronal modeling. Neuroptimus includes a graphical user interface, and works on various platforms including PCs and supercomputers. Neuroptimus currently uses various built-in cost functions and those implemented by the eFEL feature extraction library to compare the behavior of the models to the (experimental) target data. However, this approach severely limits the range of neuronal behaviors that can be targeted by the optimization. On the other hand, the popular model-testing framework SciUnit allows the implementation of tests that quantitatively evaluate arbitrary model behaviors.

The aim of the current project is to extend the open-source neural parameter optimization tool Neuroptimus so that it is able to use test scores from the SciUnit framework as the cost function during optimization. Direct applications would include the construction of detailed biophysical models of hippocampal neurons using a combination of Neuroptimus and HippoUnit, an open-source neuronal test suite based on SciUnit. All of these tools are implemented in Python.

Skills and effort: The task would probably require a full-time effort during GSoC (350h), and at least intermediate coding skills.

Mentors: The project would be supervised by members of the Computational Neuroscience laboratory at the Institute of Experimental Medicine (Budapest, Hungary), including Sára Sáray (the developer of HippoUnit) and Szabolcs Káli @szabolcs_kali (head of the laboratory), with contributions from Máté Mohácsi (the current lead developer of Neuroptimus).

Tech keywords: Python

I am interested in this project. I am a second year Physics student with experience in making Machine Learning Models, Scientific Programming and EEG Data Analysis. Are there any places I could start working, or some introdcutory resources I can delve into? @szabolcs_kali @malin

Hey, I am Harini, a second year CS undergraduate.I am interested in this project. I am well versed with python and have sufficient experience in deep learning and related fields. I have also taken a couple of math courses in college and am acquainted with the math needed for ML. How should I get started? Could we get in touch?

Dear potential Contributors,

Thank you for your interest in our work. In order to learn more about the background of the current project, I suggest that you take a look at two of our papers:

The goal of the current project is essentially to unify the approaches represented by these two articles.
The source code of both projects is also available at the respective Github repositories:
Please note that most recent development in these projects is happening outside the main branches; Optimizer, in particular, is undergoing a major update, and will be re-released soon under a different name (Neuroptimus).
If you have questions or suggestions about the papers or the code, please feel free to contact us.

If you wish to become a contributor to our project, then (in addition to becoming familiar with the resources above) please introduce yourself via email to, and make sure that you describe how you have contributed to software (preferably via links to the code on Github or other similar platform).

In addition, I recommend looking at these excellent resources on participating in GSoC: