GSoC 2024 Project Idea 8.1 Automated In-Silico Representation of Published Literature (175/350 h)

NetPyNE is a high-level graphical and python scripting interface to NEURON that describes multiscale models of brain neural circuits in a declarative, json-like syntax that can then be simulated in-silico via the NEURON simulator. NetPyNE’s toolkit allows users to declare–within a single framework–interactions between molecules (reaction diffusion modeling), synapses, channels, morphologies, and networks exemplified in our published, publicly available large-scale, biologically realistic models of rodent motor (M1), rodent somatosensory (S1), and macaque auditory (A1) thalamocortical circuits. NetPyNE is used in over 100 published models developed over 40 institutions worldwide, from smaller networks that can be run on user machines to models encompassing 50 million synaptic interactions run on high powered computing centers. NetPyNE additionally integrates with multiple popular neuroscience standards, toolkits and platforms including the NeuroML, SONATA formats, CoreNEURON, LFPykit, HNN, TVB, OSB, EBRAINS, and SciUnit.

NetPyNE’s toolkit functionality is continuously extended based on use-cases in patho/physiological modeling and signal analysis tasks. To this end, automated tools for the aggregation of in-vivo/vitro experimental datasets and conversion of these datasets to appropriate parameters for in-silico modeling represent a research and development task for 2024. This includes development of data crawlers and domain specific language models to extract relevant information from existing datasets and conversion to a common standard and declarative syntax that can then be used to generate a corresponding in-silico model. For GSoC our project involves development of a proof of concept that converts Allen Brain datasets to parameters for comparison against NetPyNE validated M1/S1/A1. Of note, these aims represent larger project aims and can be tailored to the applicant’s background expertise / time constraints/ interest.


  1. Design toolkit for automated extraction of parameters from Allen Brain datasets.
  2. Design toolkit for conversion of extracted parameters to NetPyNE syntax.
  3. Validation of extracted parameters via in-silico simulation.

Skill level: Flexible

Required skills: Python; open source software development. Other useful skills: background in computational/theoretical neuroscience and/or modeling experience; experience with NEURON/NMODL/hoc.

Time commitment: Flexible (175/350 h)

Lead mentor: Valery Bragin (, Eugenio Urdapilleta (, James Chen (, Salvador Dura-Bernal (

Project website: TBA (Discuss on Neurostars)

Backup mentors: TBD

Tech keywords: Python, open source, Data Science, Automation, NEURON, NetPyNE


Can an example Allen Brain dataset link be provided? It will be helpful to think of the steps involved and try things. There are multiple datasets on the site.

(I have been working in Information Processing Lab, IIT Kharagpur. I have worked on computational modeling (in MATLAB) and analyzed electrophysiology and 2p imaging data).


Thanks for reaching out!

Since this is part of a larger project, we’re a little more flexible in terms of datasets based on contributor interest–
For instance, anything from could be explored depending on interest. However, a particular focus is the V1 model here: Computational Modeling & Theory - You should be able to to view the paper and methodology for building the model from that link–our interest would be in automating that process.
Furthermore the cell models here: and their automated conversion w/ relevant electrophysiology would be a point of interest.



Hello James,

Thank you for the response.

I have gone through the given Visual Model paper and similar papers for Somatosensory Cortex(Borgest et al., 2022) and Motor cortex(Dura-Bernal et al., 2023). Each paper has its approach to making a model that fits the data and at places requires decisive human actions.

I meant to convey that given data and neuron model, there seems no standard procedure to fit the data to the model network. On the project title, I assumed that given a electrophysiology/2p data in a standard format(like NWB) and neuron model like ‘GLIF’ or HH, there would be some way to fit the parameters. The closest I found was found by Allen Brain’s GLIF models fitted to their single-cell electrphysiology response data(“Neuronal Models: GLIF” doc).

I wanted to ask for papers that deal with fitting neuronal parameters and synaptic weights based on the data? Something akin to this Parameter Estimation and Model Selection in Computational Biology but for network neuron models. I tried searching, but couldn’t find it. Another related paper I found was Supervised learning in spiking neural networks with FORCE training | Nature Communications , but it trains networks to do tasks, not for fitting experimental data.


You are correct, the process for fitting neuronal parameters and synaptic weights based on data still very much involves manual tuning – this is also something we are exploring in Project Idea 8.2.

Depending on the lab, the specific tuning problem, and their available software tools, different approaches have been taken to achieving parameter fits to experimental data, and these are techniques that are still very much being developed (see Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference for a recent instance) .

For this project; however, we are more interested in the preliminary stage of aggregating data and converting data to a point where fitting can be done (2 out of the 3 aims), the validation of in-silico simulation can be as straightforward as simply having the created python files run a simulation that produces something that can then be fitted if a point of concern is not achieving all aims (I posted on 8.1 regarding this, but I’ll repost here a little later).

Hello all,

Thank you for your interest regarding our NetPyNE projects,
just as a heads up, some important upcoming dates include
March 18, 2024 (next week) when Contributor Proposals Open and
April 02, 2024 when Proposals are due.

Since we are a smaller group with a heavy focus on research,
we would prefer doing a quick informal videocall or chat to
discuss your Contributor Proposal (and this will also be a
chance for you to ask any questions) before submission.

To set up a quick meeting, feel free to message me, either through
neurostars (click on my icon and select “send message”), or my specific email.

James Chen