GSoC 2023 Project Idea 1.3 tvb-multiscale (175/350 h)

GSoC
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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

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Respected Mentor!
My self Debaditya Das from India . I am a 19 years old Computer science and Cybersecurity student.

My skills and knowledge: cybersecurity , machine learning ,deep learning and web development,
android ,game development and many more.

My Previous projects:
1.HOUSE PRICE PREDICTION MACHINE LEARNING MODEL.
2.TEXT TO SPEECH AND SPEECH TO TEXT CONVERTER.
3.MAIL_SPAM DETECTOR PROJECT
4.BOOK RECOMMENDER.
5.FACE MASK DETECTION.
6.PORT SCANNER USING PYTHON FOR PEN-TESTING
ETC.

** NOW I AM WORKING IN PROJECT NAME INTRUSION DETECTION SYSTEM

Programming language: PYTHON ,C, JAVA ,flask , j.s, html, c++ etc.

MACHINE LEARNING TOOLS:JUPYTER NOTEBOOK,ANACONDA,GOGGLE COLAB etc.

CERTIFICATION: CEH(CERTIFIED ETHICAL HACKER) FROM Oak Academy(Udemy).

Machine learning libraries in python :I know skearn,numpy,pandas,tensorflow,cv2,pytorchand others important python libraries
like os , socket, datetime etc.

Machine learning knowledge : I know important ML topics like supervised-unsupervised learning
, Logistic regression, linear regression, multi regression , classification etc.

ML Algorithms: Linear regression ,Ridge Regression ,Lasso Regression ,Random Forest etc.(i know very well)

Native Language : Bengali
Proficient Language : English

Dear sir,

    I was looking for such an organization for a long time. I have had a keen interest in both machine learning since my school days. I am very interested in this project. The subject matter is very dear to me and it has motivated me a lot..I noticed that my skill set is something is match your project.so I really want to contribute.

INCF is an organization where I can learn a lot by joining and keep an important presence in the world of cybersecurity.

Really excited to collaborate and contribute to this great organization.

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Dear mentor,

I am Marta Arbizu, from Spain. I am 23 years old, soon to be 24 and graduated in Physics. I am currently doing a master´s degree in Biomedical Engineering, in which I am at the point of completing my thesis. I am working at the Biocruces Research Institute, in the Computational Neuroimaging group led by Jesús Cortés. I will be modelling large-scale brain activity, brain lesions, and brain connectivity from neuroimaging and electroencephalography. In order to do this, I am making use of The Virtual Brain. My objectives are: processing of neuroimaging by magnetic resonance and electroencephalography to infer brain connectivity, Machine Learning modelling for prediction and classification from neuroimaging descriptors and whole brain activity modelling using standardized open access tools (TVB).

Right now, I am focused on TVB for my thesis, and I would like to go deeper into it rather than choosing another project offered that doesn’t fit so well. This is the reason why I don’t think I will apply to any other project.

I am very interested in this project you are offering, as I think it could help me to broaden my horizons and feed my knowledge. Also, it would be a great help for me to work for the development of TVB and to learn more about how it works. I have been taking a look at the requirements you ask for (python and jupyter) and I definitely meet them.

Thank you for your time. Looking forward to hearing from you and starting to work.

Best regards,
Marta

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Dear @all,
It is great to hear of your interest in this project.
Please start working on a GSOC proposal, if you decide to apply for this project.
When we read a proposal (apart for respecting google rules) we also look for proof that the current project was well understood, and that some ideas on how this will be solved are highlighted in the proposal.
Feel free to ask us questions about the project, after you read the above listed documentation.
We recommend that you share your proposal draft early, for us to be able to give feedback.
For sharing, my email address is ldomide@gmail.com

Best,
Lia.

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Hello Lia,

First of all, thank you for you quick response.

Also, what are the steps to follow in order to contribute? At the moment I am having a look at the tvb - multiscale repository found on GitHub. You may be interested in something in particular, so that I can focus on that.

Best regards,
Marta

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Hi @Marta_Arbizu_Gomez,

When you look at tvb-multiscale repo, maybe try to configure an env where you are able to run notebooks, and then start reading few of those example notebooks from tvb-multiscale, to understand the project flow.

Best,
Lia.