Explore Projects

<p>While running simulation on cortical surfaces we need to calculate geodesic distance as opposed to euclidean distance due to the shape of the cortical surface. The virtual brain uses geodesic_library for this calculation. The library implements the <a href="https://pdfs.semanticscholar.org/f890/2dc723ac2a49ee52efbc58947c21f8d0970b.pdf" target="_blank">original paper</a> in C++. The original source code can be found in Google Code Archive: <a href="https://code.google.com/archive/p/geodesic" target="_blank">https://code.google.com/archive/p/geodesic</a>. tvb_geodesic repository implements a cython wrapper on top of the C++ code which then is released to Pypi (<a href="https://pypi.org/project/tvb-gdist/" target="_blank">tvb-gdist</a>) and conda-forge (<a href="https://anaconda.org/conda-forge/tvb-gdist" target="_blank">Tvb Gdist</a>).</p> <p>However, the code is now outdated and users have reported various <a href="http://github.com/the-virtual-brain/tvb-geodesic/issues/" target="_blank">issues</a>. In this project, we aim to update the code and fix those issues.</p>

<p>In this proposal, I outline how I plan to implement and benchmark five learning rules - reward-modulated STDP, triplet STDP, compeititve STDP, SuperSpike, and somato-dendritic spike-dependent synaptic plasticity - in GeNN.</p>

LORIS is a comprehensive data management platform that supports diverse datasets in neuroscience research, including imaging, behavioural, clinical, and genetic data. As part of my contributions, I will focus on implementing new modules to extend the platform’s capabilities. These modules may include advanced data processing pipelines, automated anomaly detection for quality control, and enhanced user authentication mechanisms to ensure secure access to sensitive medical data. Additionally, I aim to develop tools for improved data annotation, visualisation, and integration with external analytics frameworks. By enhancing the modular architecture of LORIS, my work will help streamline research workflows, improve data integrity, and facilitate broader adoption of the platform within the scientific community.

<p>This is a project is about identifying and parameterizing the precursors of neuronal cells within early-stage embryos. We will co-register the names with the cells due to their general location, and we collect quantitative data about static location after division and vectors that describe shape changes and overall positional changes in the embryo. For this, we need to develop image segmentation tools, which is the top priority of my proposal, to assist further development of this project. My approach, in nutshell, is to try out a variety of segmentation techniques to achieve the top priority of this project. The project will conclude with a development of a proper ImageJ plugin which will facilitate application of our approach on other databases.</p>

To Remove Bootstrap from the application and substitute the design with pure SCSS.To finish implementing the CSS global variables and add component wise CSS variables to improve the reusability of code in the application.To add CSV viewing functionality on the datasets of AnalySim to view, filter, and browse through the columns of the file. To create a feature on the Jupyter notebooks to make the Notebooks downloadable.To improve the Registration page, Explore section page, and my Dashboard page design and make the User Interface consistent throughout the application.To remove the pages and the page links that are unused in the application. To improve and redesign the project creation page. To make the design of the application consistent throughout the application to create a great User experience.

<p>This project is about packaging of the the virtual brain(tvb) to the scientific community, using the most used scientific software distributors such as packaging tvb to anaconda, Develop a native launcher for tvb distributions, Packaging and distribution for neurodebian , Develop a vagrantfile script for virtual machines, Develop a script( Dockerfile ) for building a docker image, And finally if time allow develop a script for building a Amazon Web Image(AMI).</p>

The Active Segmentation platform for ImageJ (ASP/IJ) was developed in the scope of GSOC 2016 - 2021. The plugin provides a general-purpose environment that allows biologists and other domain experts to use transparently state-of-the-art techniques in machine learning to achieve excellent image segmentation and classification. ImageJ is a public-domain Java image processing program extensively used in life and material sciences. The program was designed with an open architecture that provides extensibility via plugins computing different filters and region descriptors (i.e. image features). The feature space and the classification results produced by the platform are stored in several separate files. The idea is that the types and values of image features and classification outcomes would be stored in an SQLite database for cross-comparisons between sessions. The candidate is required to use the SQLite database engine in order to integrate it with the GUI of ASP/IJ.

<p>The project is about refactoring the current dual brain activity visualiser's 2D plots and adding new visual components to its 3D part to display more information about the signals. We will rewrite the 2D part using a modern data visualisation framework. In the 3D part, apart from the instant activity of the brain, we will add a dynamic sphere at the location of the signal. The proposed new viewer will also have the interactivity of zooming, play and pause for the users.</p>

<p>Time series analysis methods are regularly being developed and we don’t have any resource at present that compares the newly developed method with the methods that already exists which can help the user to conclude the similarity between new method and pre-existing methods. In this project we are going to develop a web-based system that takes an analysis method as python code from a user, computes it with a diverse time-series data-set and analyzes the relation of the newly developed method with the pre-existing ones.</p>

<p>The release of a new NWB version makes it necessary the reintegration of this format into the awesome open-source in-browser neuroscientific simulator, Geppetto. This integration will allow any Geppetto based application to be able to provide visualization of simulated data alongside of electrophysiology recordings.</p> <p>The purpose of this project is to make the above happen.</p>

<p>The project aims to provide a robust mechanism for cell tracking using 2D raw image object.Through the use of Viterbi Dynamic Programming based Algorithm it is aimed to implement an efficient Cell Tracking system aided by Multi Class Classifiers for Cell Event Probability definition.Trajectory estimate will be de-noised using modern filters such as IMM,Weiner and Multiple Channel Linear Correlation Filter.</p>

This project focuses on enhancing the overall workflow by improving the detection and configuration of the Emscripten toolchain. It aims to optimize data transfer between the WebAssembly simulation and JavaScript for efficient result visualization. Additionally, it provides user-friendly tools for website customization and plot display. Comprehensive documentation will be included to ensure ease of use and integration.

NiiVue is a versatile tool for visualizing and handling neuroimaging data, but insufficient documentation creates friction in using it in deploying workflows. This makes it challenging for developers to integrate, reducing the usability of NiiVue. As demand grows for secure and efficient web-based processing, this project aims to provide a demo that helps developers implement and customize cloud providers and analysis tools for scalable, secure neuroimaging workflows. This project will develop a full-stack solution for scalable neuroimaging analysis, integrating client-side preprocessing with Niimath and visualization using NiiVue. A PostgreSQL database will be designed to manage neuroimaging tasks and results. Two backend approaches will be tested: one using Flask with Nipype to interface with neuroimaging tools like FSL and FreeSurfer, supporting parallel processing, and another using Flask with containerized neuroimaging tools managed via Docker orchestration (Kubernetes/ECS) for greater flexibility. The system will be deployed on AWS with secure infrastructure, including EC2 and S3, ensuring data security through encryption and access control. A minimal demo will showcase the workflow using FSL BET or FreeSurfer, accompanied by clear documentation for easy replication and deployment by developers with their own cloud provider.

<p>Neuroscientists have begun to publicly share more and more datasets, however there are still barriers to making these datasets easily reusable by the community. One of these barriers has been the accessibility of shared data; it takes extensive time and effort to understand different data formats and determine which datasets are best suited for the scientific questions being asked. This project is an important step in addressing this challenge. The goal of this project is to convert publicly available datasets to the standardized NeuroData Without Borders (NWB) format so that they can be better interpreted and reused by other researchers. The data will then be made available for interactive analysis and visualization through the NWB explorer on the Open Source Brain repository. By using a standardized data format, researchers can more quickly work with new data and develop analysis methods to apply to a wide variety of datasets. The ability to easily explore and visualize data will also allow researchers to quickly assess the contents of the data and if they can reuse it. Thus, the results of this project will contribute to an important resource for scientists.</p>

Activity patterns of neurons have so far been examined both singly and in pairs. Higher order interactions (HOIs), or non-pairwise interactions between neurons, are being studied in an effort to improve analysis . The type and quantity of information stored in groups (pairs or multiplets) in the brain is revealed by information about organisational structure (o-info). Various methods to optimise the o-info implementations in NumPy and Jax, as well as techniques for plotting the HOIs and integrating them with Frites will be included.

The proposal involves contributing to the LORIS codebase, a pivotal resource for neuroscience research. LORIS plays a crucial role in facilitating Open Science and advancing global research by offering an open-stack platform for the management of diverse medical data. My work will include but will not be limited to enhancing the system's functionality through new modules, increasing test coverage, and developing new API endpoints.

This project addresses Blender’s edit-mode mirror limitations by enhancing its functionality to support multiple axes and operators. It will enable artists to edit symmetrically without relying on workarounds like deleting half the model to apply a mirror modifier which is a method impractical for nearly finished models and incompatible with shape keys, which require actual vertex positions rather than modifier-driven symmetry. I’ll analyze the transformation system, refine the mirroring utilities, and extend support to operators that do not support mirroring. The deliverables include these enhancements, detailed documentation for users and developers, and a set of tests to ensure consistent operation. This will make symmetrical modeling more efficient for artists.

<p>Automatic speech recognition systems are traditionally based on Hidden Markov models(HMMs) as it had given the best results in the past. However due to recent advances in neural networks, recurrent neural network(RNN) models have became competitive and surpassed HMMs in speech recognition task. RNN models also have the advantage of being used in an end-to-end model and don't require extensive hand engineering of features like HMM. In this project I aim to implement a LSTM(Long Short Term Memory) based model as based on state of the art research and make it easily available using PocketSphinx like API.</p>

<p>As mention in the title, it is a 3D animation model of the motor control of speech production. Our long range goal is to implement the model completely and test it exhaustively, in the belief that doing so will significantly advance our understanding of speech motor control. The model is designed to have properties that are as faithful as possible to data from speakers– including measures of brain function, speech motor control mechanisms,physiolog, an atom, biomechanics, and acoustics.</p>

<p>Veritesting, [1] is a promising technique that speeds up dynamic symbolic execution by multiple of factors [2]. In veritesting, expensive forking is minimized by statically analyzing and summarizing regions of code, which are then used during dynamic symbolic execution. During summer, I plan to a) extend veritesting to support test case generation for static re- gions, b) optimize instantiation of high-order regions based on benchmark results and c) work on a prototype to support static regions for 2-threaded programs.</p>

<p>The main idea of the project is to define program repairs, which make the program closest to its oracle.</p> <p>Initially, we have a program, which is implemented in an incorrect way. We apply some fix options in the form of simple syntactic changes to it. Then, we run the same set of inputs on the oracle program and the repaired versions of the program. Using SPF and Green we calculate the percentage of the inputs for which the oracle program and the repaired program produce the same outputs. This percentage is the indicator of the quality of each fix.</p>

<p>Symbolic PathFinder (SPF) has been very successful in testing and verifying Java bytecode programs with numeric inputs. However, its capability is very limited when coping with programs that have heap data structures. The underlying lazy initialization algorithm exhaustively enumerates all heap objects that can bind to the structured inputs accessed by the program. This enumeration may identify many invalid heap configurations that violate properties of the data structures in the heap, which leads to a huge amount of false alarms.</p> <p>We aim to tackle this problem by using separation logic, a well-known assertion language designed for reasoning about heap-manipulating programs. We will build a system, Java StarFinder (JSF), that enables users to describe properties of the data structures in the heap using separation logic. JSF is a symbolic execution engine, built on top of SPF, that generates path conditions (PCs) in the form of separation logic. These PCs are checked by a solver for satisfiability and test input generation. In addition, JSF can verify program correctness by collecting PCs satisfying some given preconditions and verifying if these PCs satisfy user-provided assertions.</p>

<p>JPF-CORE currently builds and runs on Java 8. The main objective of this project is to get it up and running with Java 9, leveraging its new features like the modularity, strong encapsulation, while also handling the deprecates and removes. There are also other new enhancements like multi-release JAR files which is useful in supporting one or two major versions, in addition to just Java 9.</p>