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.
All projects below can be tailored for a 12-week time window, both full-time and part-time, as the features/pages can be built incrementally.
In the TVB ecosystem there is a toolbox called TVB-Multiscale, which allows for Co-Simulation of TVB with spiking network simulators. The toolbox currently includes interfaces to NEST (via pynest interface), ANNarchy and NEURON via the NETPYNE python interface. Co-Simulation means that most of the brain is modeled by TVB using mean field dynamical models (the equation of which describe the average activity of millions of neurons), whereas a few selected brain regions are modeled at a finer scale as spiking neuronal networks. Then, both parts of the model are simulated at the same time and exchange data via necessary transformations between mean field activity and total spiking activity. TVB-Multiscale is already actively used in brain modeling studies (e.g., see the first related publication, modeling Virtual Deep Brain Stimulation to a TVB brain, where a basal ganglia network is modeled as a spiking neuronal network in ANNarchy).
Currently, Co-Simulation takes place in a sequential manner, where the integration of each model by the corresponding simulator, as well as the bidirectional transformations and transfer of data, follow one after the other, and are executed by a single process.
The objective of this project is to work towards the next major release (3.x), which will allow for Co-Simulation and data transformation and exchanges to happen in parallel, via multiprocessing. There is already ongoing work, towards a solution that will allow for interactive configuration and execution of the parallel Co-Simulation in Jupyter notebooks, with an API as similar as possible to the API currently used by the sequential Co-Simulation. This solution is using ray, a software that makes parallel computations easier. Unit and integration tests, as well as documentation, are additional aspects of the work that needs to be done.
Expected results: A set of classes, extending the existing TVB-multiscale classes, and allowing for parallel Co-Simulation, as well as, related examples, tests and documentation, to be included to the next major release of TVB-Multiscale (3.x).
Skill level: Mid/mid+
Required skills: Python, Jupyter (optionally multiprocessing, especially ray or MPI)
Time commitment: Flexible (175/350 h)
Lead mentor: Dionysios Perdikis
Project website: https://req.thevirtualbrain.org/browse/TVB-2607
Backup mentors: Lia Domide, Michael Schirner, Petra Ritter
Tech keywords: Python, Jupyter, multiprocessing, ray, MPI