This project is dedicated to developing a set of automated tools and processes for extracting useful neuronal experimental data from datasets provided by the Allen Brain Institute, and converting these data into parameters that can be used for neuronal modeling on the NetPyNE platform, for comparison with cortical regions already validated by NetPyNE. The project flow is divided into the following steps: Data Collection: Automatically extracts data from the Allen Cell Type Database for specific cell types in the visual cortex, including electrophysiological properties, morphological characteristics, and gene expression patterns. The Allen Institute provides a powerful API that allows users to programmatically access and retrieve data. First through data cleaning, and then through data crawling to obtain data that is difficult to access in the API. In addition, domain-specific language models will be fine-tuned or trained using Google's Gemini model for subsequent data acquisition. Parameter Conversion: Develop tools(scripts) or design large language model prompts to automatically convert extracted data into a parameter format understood by the NetPyNE, such as .hoc or .swc format. Identify how to set reasonable default values for missing data. Model construction: Use the converted parameters to construct neural network models of relevant brain regions in NetPyNE. This includes defining neuron types, neuron connectivity, and synaptic interactions between neurons. Run Simulation: Run a simulation of the model in NetPyNE to simulate the brain's response to a specific input stimulus. Compare Results: Compare the simulation results with actual biological experimental data to verify the accuracy of the model. Intended set of deliverables: Automated Data Collection Tool. Parameter Conversion Script. Simulation Run Framework. Result Validation Report. Documentation and User Guide.

I am proposing a project for Google Summer of Code to develop an interactive SWC to NeuroML converter in Python. The converter will simplify the conversion of the widely used SWC format to the NeuroML standard for biophysically detailed neuronal models. The project will include an interactive tool for users to utilise, and it will be tested against neuronal reconstructions from NeuroMorpho.org

The Virtual Brain (TVB) is a scientific simulation platform that provides all means to generate, manipulate and visualize connectivity and network dynamics of the brain networks. Researchers in Computational Neuroscience use brain network models to understand the dynamic behavior of the healthy and diseased brain, as measured by various neuroimaging techniques such as fMRI, EEG, and MEG. TVB is currently the only neuroinformatics project providing a platform for researchers to work together on modeling studies using brain network models with realistic connectivity. The modeling of the simulations requires datasets generally hosted on data storage platforms like Zenodo, OpenSourceBrain, EBRAINS, etc. One has to download the dataset manually, unzip it and then use it inside the web GUI. Instead of manually downloading and unzipping the data, the project's goal is to have a dedicated framework to manage the downloading and unzipping the dataset from remote sources. Right now, we are focusing on the Zenodo platform, but there are other platforms like OpenSourceBrain and EBRAINS for which we would like to extend the downloading functionality.

Open Source Brain is an online resource for sharing and collaboratively developing computational models of neural systems. Learn more at: https://www.opensourcebrain.org/