TVB GSOC 2021

[liadomide]

The Virtual Brain: An open-source simulator for whole brain network modeling.

There are several modeling studies using brain network models which incorporate biologically realistic macroscopic connectivity (the so-called connectome) to understand the global dynamics observed in the healthy and diseased brain measured by different neuroimaging modalities such as fMRI, EEG and MEG.

For this particular modelling approach in Computational Neuroscience, open source frameworks enabling the collaboration between researchers with different backgrounds are not widely available. The Virtual Brain is, so far, the only neuroinformatics project filling that place.

[1] Reusable visualization tools for Jupyter

Description: TVB's web-based UI provides several very useful
visualization tools, which are setup for full screen
use. As TVB is used in wider contexts (HBP collaboratory,
Jupyter notebooks), it is important to ensure the relevent
visualization tools are present everywhere.

This project is to rewrite the widgets in TVB UI to become
reusable components which can be employed from a Jupyter notebook
for use in the HBP collaboratory, while maintaining compatibility with
the existing TVB framework. Tools are to be
refactored, choice up to the student, in order of priority

1. anatomical visualization (surface, connectivity)
2. time series viewer
3. the phase plane tool

Screenshots for inspiration:
https://github.com/the-virtual-brain/tvb-documentation/blob/master/manuals/UserGuide/screenshots/visualizer_dual_seeg.jpg
https://github.com/the-virtual-brain/tvb-documentation/blob/master/manuals/UserGuide/screenshots/simulator_phase_plane_interactive.jpg

Use of WebGL (in particular Python/notebook oriented GL tools) are
encouraged, where numerous interesting opportunities for optimization
are present, e.g. XTK for anatomy, vispy for time series.

Expected results: A set of classes usable within Jupyter notebook, for displaying common data objects via WebGL or WebGL-based libraries. Documentation and usage examples for all created classes.

Skills: Python, WebGL, IPyWidgets, Jupyter

Mentors: Lia Domide, Paula Popa

[2] Benchmark and Optimize TVB areas

Description: TVB has become a complex tool, with some generic areas and few very specific critical parts. Due to these, some parts are slow now. The team has identified some area with great potential to improve:

1. Simulation wizard
2. H5 files writing
3. loading for visualizers from files

Starting from these critical parts (identifying others from the student tests would be much appreciated), writing or executing a flow, profiling the code usage and proposing improvements would be the goal of this project. We expect the student to start by accommodating with TVB documentation, understanding the main workflows, creating a work development setup, then use benchmark and profiling tools for identifying the critically slow areas in the code, as well as propose optimization solutions.

Expected results: A set of well described slow scenarios for benchmarking, profiling files with highlighted critical code areas, suggestions to improve (vectorize code, rewrite, use buffers, etc).

Skills: Python, Numpy, Numba, profiling tools

Mentors: Lia Domide, Paula Popa