Author : Ioannis Daras
Mentors : Ankur Sinha , Padraig Gleeson
Organization : INCF
Project Source Repository : pyNeuroML
Project Title : Incorporate new features into an advanced, cross-platform 3D viewer for NeuroML cells and networks
Visualization is crucial in computational neuroscience as it allows researchers to interpret complex data, identify patterns, and understand the intricate workings of neural networks.
This project aims to improve and facilitate the usage of an already existing 3D cell/network viewer by adding features that enhance the current state of visualization, allow for more interactivity and provide users with greater flexibility. Specifically, in this present work, some utilities for improving viewing of the cell were added, while also adding shading, making the cell appear much more realistic. A very important milestone was also hit with the achievment of integrating the viewer with jupyter notebooks which are gaining popularity rapidly. Finally, the ability to click on a part of the cell and return information for that specific part was implemented, improving the interactivity of the viewer and its versatility.
Due to a refactoring that happened late in the project, these pull requests where not used as is
- Add lighting
Most of the code from here is used in this pr
This is used on this vispy pr and will also be tweaked to match the refactored version
- It was an incredible opportunity to dive into the exciting world of computational neuroscience, explore cutting-edge research, and gain a deep understanding of the field's complexities. This experience not only broadened my knowledge but also ignited a passion for further exploration and innovation in this rapidly evolving discipline.
- I really got to hone my skills in studying documentation and debugging since this project involved a deep dive with both pyNeuroML and Vispy libraries, which is used to accomplish the 3d visualization
- I got to delve deeper into the world of computer graphics with topics such as shading, transformations and meshes/instanced meshes
- Last, but not least, I got to interact with experts in the field, learn and apply clean coding practices and get more accustomed to github and its utilities
I would like to sincerely thank my mentors for their exceptional communication, patience, and support, not only in guiding me through the project but also in helping me embark on my journey into computational neuroscience. Their willingness to assist with every aspect of my learning, including the opportunity to attend lab meetings, has been invaluable.