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Working Groups

Working Groups

Working Groups are composed of users and developers from across the INCF network working collaboratively to develop, refine, and/or implement community standards. Working Groups are composed of SIG members working on short-term funded projects that aim to achieve a concrete deliverable.

Active Working Groups

Special Interest Groups are composed of users and developers from across the INCF network working collaboratively to develop, refine, and/or implement community standards. Working Groups are composed of SIG members working on short-term funded projects that aim to achieve a concrete deliverable.

 

Neuroimaging Quality Control (niQC)

Neuroimaging Quality Control (niQC) WG aims to develop standards and best practices for quality control of neuroimaging data, including standardized protocols, easy to use tools and comprehensive manuals

Standardised Representations of Network Structures

This SIG deals with the various tools and formats for creating and sharing representations of biological neuronal networks, and will work towards ensuring these are as interoperable and usable as possible for computational neuroscientists.

Reproducibility and Best Practices in Human Brain Imaging

The SIG aim is to collect, compile, synthesize and distribute information from task forces working on separate projects but with reproducibility in neuroimaging as an overarching theme.

Neuroshapes: Open SHACL schemas for FAIR neuroscience data

This SIG aims to coordinate community efforts for the development of open, use case driven and shared validatable data models (schemas, vocabularies) to enable the FAIR principles (Findable, Accessible, Interoperable and Reusable) for basic, computational and clinical neuroscience (meta)data.…

Neuroinformatics for cell types

This SIG will coordinate common efforts for defining and describing cell types across neuroscience, to reduce duplicate efforts and to improve interoperability and reuse of cell type-specific data collected across groups.

Neuroinformatics for Aging

This SIG will be a community dedicated to the creation and application of neuroinformatics technologies to address clinical and wellness challenges in aging

FAIR Metadata Working Group

The aim of this working group is the harmonization of Common Data Elements (CDEs) for data discovery and metadata annotation.

Electrophysiology Stimulation Ontology Working Group

The aim of this working group is to develop a small, well-scoped ontology for describing electrophysiology stimulation parameters. The working group is composed of representatives from the INCF network, Human Brain Project (HBP), Neurodata Without Borders (NWB) Core Development Team,…

A reduced time-series feature library to efficiently characterize neural dynamics
  • Reduce complexities by distilling a large literature on time series analysis into a small subset
  • Minimize loss of classification accuracy by using only significant features to represent the time series, with minimal loss in classification accuracy
  • Package coded features…
Automated Comparison of Scientific Methods for Time-Series Analysis
  • Develop a new web-based system to compare different time series analysis methods
  • System will take python code input, compute it with a diverse time-series dataset, and analyze the relation of the newly developed method with the pre-existing one.
Benchmarking modern SNN training algorithms on GeNN
  • Combine GeNN and other machine learning packages using Python (PyGeNN, TensorFlow, Jupyter for tutorials) and C++ (GeNN)
  • Provide easier code generation and code flexibility 
  • Compare the performance of different models
Cell Tracking Using Geometrical Features
  • Provide a robust mechanism for cell tracking using 2D raw image objects
  • Use the Mean Square Distance method to calculate the potential object displacement
  • Denoise the trajectory estimate using different modern filters such as IMM,Weiner and Multiple Channel Linear…
Conversion of large scale cortical models into PyNN/NeuroML
  • Convert published large scale network models into open, simulator independent models
  • Test models across multiple simulator implementations
Deep learning using geometric features

Use the Deeplearning4j library and modern deep learning methods for image analysis to extend active segmentation

Improved model description functionality in brian
  • Create a general, interoperable framework to coherently describe Brian models in a standard format
  • The standard format shall act as the foundation for exporting Brian models to NeuroML/LEMS format, human-readable like LaTeX typesetting, and ModelView description
Improving Personalized Models of fMRI Recordings Including Individual Region-Specific HRF in The Virtual Brain

To simulate subject- and region-specific BOLD signals by estimating the rsHRF of all the voxels from fMRI input data, then averaging these values over the regions used in TVB.

LORIS Automated Testing
  • Improve the testing database and the test datasets
  • Work out bugs by running unit and integration tests
  • Create documentation that can help future developers and users test LORIS themselves and write their own tests
LORIS Visualization of timed neuroscience data
  • Offer end users new visualizations for time-series data within LORIS
  • Allow better interpretation of data shared online through LORIS
LORIS: API development and documentation

Implement endpoints for LORIS modules not already accessible via the REST API to make data available to researchers while protecting the privacy of subjects

MRI Registration using Deep Learning and Implementation of Thin-Plate Splines
  • Develop deep learning-based methods to achieve faster image registration
  • Develop deep neural networks (DNNs) for MRI registration using thin-plate splines, free-form deformations, and affine transformations
OpenWorm - OpenDevoCell Integration
  • Solve some of the barriers existing in OpenWorm
  • Develop the OpenDevoCell portal to be globally accessible
Conversion of public neurophysiology datasets to NeuroData Without Borders format
  • Contribute to the NWB Showcase
  • Deliver multiple converted datasets
  • Integrate tutorials and analysis examples for select converted datasets
Pre-trained models for Developmental Neuroscience
  • Train a deep learning model(s) from the image dataset(s) provided
  • Develop a data augmentation pipeline which can be used on the cellular image datasets (incl. images which are not involved in this project) to help build a model robust enough for its purpose
  • Make the…
Python-based electroencephalography (EEG) and deep learning workflow system

To allow drag-and-drop creating, editing, and running workflows in JSON format from a predefined library of methods, focusing on EEG signal processing and deep learning workflows

Responsive dashboards for extensive exploration, monitoring, and reviewing of large neuroimaging datasets

Create a flexible dashboard framework which can be customized to suit user requirements

SciUnit Bifurcation analysis

Evaluate the strength of various brain data analysis models

TVB: Web GUI for Reconstruction Pipeline
  • Create a basic GUI interface for the reconstruction pipeline to allow users to provide input data, choose configurations, identify the outputs, and check logs
  • Integrate the GUI with Pegasus workflow automation
Unit Tests for Large-Scale Brain Network Dynamics

Develop tests to validate predictions from a corticothalamic neural mass model against relevant features of EEG data

Upgrade and Fix tvb-gdist C++ Library

- Improved tvb-gdist C++ code

-  Link to The Virtual Brain

The current implementation is hosted here:  
https://github.com/the-virtual-brain/…

Past Working Groups

Past Working Groups

Biophysically based Computational Models of Glial - Neuron Coupling

This SIG aims to bring together experimentalists within the glial community with computational modellers, with objective to provide a forum to foster detailed interactions and advance astro-centric brain models.

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Reproducible Research and Open Neuroscience

This SIG will coordinate interactions among researchers who are interested in reproducible research issues and open science. We will promote policies that support reproducibility, encourage better training in this area, and organize information about resources to make them more visible to the neuroscience community.

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