Neurobagel is a federated data ecosystem that allows researchers and other data users to find and consume research data that has to remain at their original institute for data governance reasons. It helps researchers connect a local neuroimaging dataset with others in a decentralized framework using linked data principles. Currently, the researcher or the data user has to answer a number of queries to get the desired results and it often requires iteration. My aim would be to make this search process more user-friendly by adding an LLM style chatbot interface. This is to be done by utilizing large language models that will be able to interpret the user prompts and initiate the API calls accurately giving the user the desired results.

TheVirtualBrain (TVB) is the first integrative neuroinformatics platform for the modeling of full brain network dynamics. TVB simulator, written in Python, enables the systematic, model-based inference of neurophysiological mechanisms on different brain scales that underlie the generation of macroscopic, commonly used neuroimaging signals (EEG, MEG, fMRI). In the tvb-ecosystem, there is a new repository called tvb-widgets, which consists of UI widgets for Jupyter notebooks. Offering TVB-web interface's functionality as a JupyterLab widget will enhance integration, workflow efficiency, and interactivity making it a valuable addition to the TVB ecosystem for research and educational purposes. The project focuses on enhancing the tvb-widgets repository and developing new widgets. The proposal is for the implementation of more features from the Connectivity cockpit of TVB's web interface as interactive widgets primarily the Space-time Visualizer widget and the Connectivity Matrix Editor widget. This can be achieved by leveraging different libraries like Ipywidgets, PythreeJs, etc. Essential deliverables include classes, functions, tests, etc., for these widgets.

DevoGraph is an open-source GNN framework designed specifically for analyzing embryogenetic data. It utilizes Graph Neural Networks (GNNs) as a powerful method for revealing connectivity patterns within complex geometric datasets. My contribution to this project involves (i) implementing the Mapper algorithm for topological data analysis, (ii) implementing an Evolutionary-based Neural Developmental Program to mirror the embryogenetic growth of C. elegans, and (iii) code integration in DevoLearn. Through this project, I aim to create informative and interactive visualizations for better interpretation and analysis of the data.

The project aims to enhance the LORIS web platform, a widely used tool in neuroimaging research with databases utilized by research teams in 22 countries, by improving its data ingestion, storage, and processing capabilities for neural information. The approach includes familiarizing with the LORIS platform, exploring its source code, assessing and enhancing existing modules, and introducing new modules to augment and enhance functionality. The project will involve rigorous implementation and testing of code updates, documentation of changes, and integration of feedback from the LORIS community. Deliverables include improvements to the existing code, development of features for EEG modules, development of visualization and data pipelines, and comprehensive documentation and tutorials for new features. This enhancement will streamline research efforts for the global user base of LORIS, making the platform more efficient and user-friendly.

This project studies the development of a tiny worm called Caenorhabditis elegans, focusing on how its cells grow and interact from its early stages. It uses two special tools called Spatial Hypergraphs and Lineage Hypergraphs to map out and understand these interactions. Spatial Hypergraphs look at how cells are arranged and interact in space, which helps us understand how they communicate and influence each other. Lineage Hypergraphs track the family tree of each cell, showing how cells evolve and form different parts of the worm's body. By combining these tools, the project aims to get a complete picture of the worm's development, from a single cell to a fully formed organism. This work could help us learn more about basic life processes and improve medical and tissue engineering fields. The project is supported by international organizations interested in neuroscience and the use of advanced computer models to study brain and nervous system development.

Gesture-based music generation has existed for some years now, thanks to software such as Wekinator. However, they use machine learning methods that have been overtaken by recent Deep Learning innovations, and therefore limit the studies that can be carried out on musical creativity and sound/movement interaction. The aim of this project is to use these modern Deep Learning methods to generate music driven by motion capture. The framework consists of 3 distinct modules. Firstly, user gestures are detected using landmarks generated by MediaPipe by Google. Secondly, these landmarks are used to recognize the gesture performed by the user, and generate Open Sound Control (OSC) signals. Finally, the OSC signals are converted into music using Max/MSP. In addition, new gestures can be dynamically added to the framework, with just a few examples, and mapped to new sounds. Deliverables: - Experiment to estimate the appropriate delay between a gesture and the resulting sound/effect. - Framework based on LivePose or Pose2Art using only the gestures predifined in MediaPipe, able to interface the chosen camera and the sound-card - Model based on MediaPipe holistic model to classifiy dynamic gestures - Famework to map gestures to sounds through OSC signals. - Few-Shot Learning-based model to dynamically add new gestures.

This project aims to build HarmonyHub, a web platform for personalized and engaging learning experiences with variable-pitch instruments like wind instruments (e.g., Trumpet, Flute, Clarinet) and bowed string instruments (e.g., Violin, Viola). Most wind and string instruments are among the most difficult instruments to master. This project seeks to bridge the gap between traditional music education and modern technological capabilities. The website offers customized exercises based on a student's skill level and desired progression speed, with real-time feedback for improvement. Personalized features, such as adapting exercises to master different rhythms with precise control over intonation, loudness, pitch, etc., will be implemented. Students and teachers will be provided with features to input any score, and the website will analyze the score, play the sound, and demonstrate how to play it on the instrument with pictures for the students to understand and replicate. When the students play it, real-time feedback of their performance will be provided. All of this is done through proper API integrations of APIs available for this process.

We aim to further enhance model development with NetPyNE’s “batch” subpackage by refactoring the code base for ease of use and scalability, then use the improved batch subpackage to explore and typify the effectiveness of various search algorithms (random, population based, various posterior based…) on a diverse model repository including: rodent motor(M1), rodent somatosensory(S1), and macaque auditory (A1) thalamocortical circuits to make NetPyNE’s capabilities more efficient for computational neuroscience research.

The project is about incorporating new features into an existing 3D viewer which allows for visualisation of cells and networks using the NeuroML standard. Right now the viewer is limited to only viewing the model, whether it is a single cell or a complete network, but at the end of the project it should be a fully equipped 3D viewer with GUI to interface with the user. The user should be able to get as many information as he can about the model using an intuitive GUI with a short learning curve, using “on click” or “on selection” methods to provide according information and providing easy access to views of cells showing ion channel distributions, segment groups, etc. A critical aspect of the project is to ensure high performance with the Vispy viewer to allow visualization of large scale networks. Being able to interact with a model, significantly boosts the understanding of its structure and dynamics. Furthermore, the additional information that will be added can make this viewer an invaluable tool both for an experienced scientist and a beginner in the field, as long as they have some knowledge of python, by providing the capability of exploring new models in a fast, efficient and intuitive manner. Additionally, the Vispy viewer should be embedded into NeuroMLLite-GUI. Options for using the Vispy viewer inside Jupyter notebooks/JupyterLab should also be investigated, as Jupyter notebooks are gaining popularity rapidly.

This project entails the upgrade of the existing physiopy packages, peakdet and phys2denoise, for optimized interoperability and the development of semi-automated unified physiological signal processing routines using the updated toolboxes which will largely benefit users that frequently engage with these workflows.