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 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.
Simulation-based inference is a powerful emerging method for parameter estimation, which has been used successfully in a neuroscientific context, but is not currently available in Neuroptimus.
The aim of the current project is to extend the set of parameter estimation methods available in Neuroptimus (which aim to find a single best solution) by adding methods of simulation-based inference, which aim to determine the probability distribution of the parameters from the data, and enable the quantification of uncertainty and correlations in the estimated parameters. Methods would include Bayesian parameter inference based on the specification of the likelihood function and the prior distribution, solved via direct or approximate methods such as Markov-chain Monte Carlo (MCMC) or variational inference. Likelihood-free methods would also be included. Integration of these methods would rely on existing Python packages, and would leverage previous experience in the group with using these approaches.
Skills and effort: Intermediate
Required skills: At least intermediate coding skills. Comfortable with Python. Experience with probabilistic inference or basic AI/ML concepts and tools would be beneficial.
Time commitment: Full-time (350 h)
Mentors: The project would be supervised by members of the Computational Neuroscience laboratory at the Institute of Experimental Medicine (Budapest, Hungary), including Máté Mohácsi (mohacsimate91@gmail.com) (the lead developer of Neuroptimus) and Szabolcs Káli (kali@koki.hu) (head of the laboratory).
Hello I am Tamogh Nekkanti ,3rd year computer science student at Thapar Institute of engineering and technology, would love to contribute to this project. I have already set up neuroptimus in my linux system. I have 2 years of experience in python and have also worked on several machine learning projects and nlp related tasks. Would try to understand the general code structure for neuroptimus python file and would get started on the work.
Hey, I am Aryan Mishra a Junior at Indian Institute of Science Education and Research, Bhopal. I am highly interested to get to know and work to enhance Neuroptimus this is an interesting domain to work upon I am majoring in Data Science and Engineering and have comprehensive experience of working on Machine Learning Projects so would like to get started and understand code base, looking forward to learn and contribute.
Hi team, I’m Deep Nayak, a computer scientist from India. I am a working professional and have experience in the Machine Learning domain. I am currently getting familiar with the Neuroptimus codebase, would highly appreciate it if someone could point out additional resources, thanks!
Hey, I’m a final year undergraduate in Mathematics and Computing. I am looking to contribute to to this project; I am familiar with MCMC methods (I have used Gibbs and posterior sampling in my previous coursework/projects) and can easily pick up other variational inference and likelihood-free methods as well. I do not know much about simulation-based inference in context of neuroscience, so it would be great if @greg_incf or the mentors could point me to some papers/blogs/articles on the same. Some additional clarity on what will be the expected deliverables is also appreciated. Should I email the mentors personally with my questions?
My name is Federico and I am a passionate computer science student with a particular interest in the field of scientific research and artificial intelligence. I am currently pursuing a double master’s degree in Computer Science at the University of Trento in Italy and Eötvös Loránd University in Budapest, Hungary.
I am deeply motivated to participate in the proposed Google Summer of Code project, which aims to extend the capabilities of the open-source software Neuroptimus in the field of computational neuroscience. This project attracts me because it represents a unique opportunity to combine my passion for programming with my growing curiosity about neuroscience.
Participating in open-source projects has always been a goal for me, as I strongly believe in the importance of knowledge sharing and collaboration for scientific and technological progress. Contributing to a project like Neuroptimus would not only allow me to enhance my practical programming skills, but also enable me to make a tangible difference in the field of neuroscience research.
I am confident that my academic background and programming skills, along with my passion for research and desire to continuously learn, will make me a valuable contributor to this project.
Attached you will find my curriculum vitae (link), where you can find more details about my academic background and professional experiences. I am available for further clarification or to discuss any details regarding the proposed project. Thank you for the opportunity and attention.
I am Nanthakumar currently pursuing computer science in NIT Trichy. well experienced with python, LLMs, tensorflow. Previously build an classification model with tensorflow. BY combining various model from hugging face I had build an AI-video app. Looking forward to work in this project
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 this paper: https://doi.org/10.3389/fninf.2014.00063
The software has been substantially updated and extended since the publication of that paper.
You can find the current standard version (also including a link to the documentation) here:
We have also applied traditional Bayesian inference (where the prior is given and the likelihood can be explicitly calculated based on the simulation results), using both exact and approximate inference methods (MCMC and variational approximations).
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 kali@koki.hu, and make sure that you describe how you have contributed to software (preferably via links to the code on Github or other similar platform).