GSoC Projects 2015

We participated as a mentoring organization in 2015, for our 5th consecutive year, with 12 projects proposed and mentored by our Nodes and international community.

Brian2's interoperability with simulator independent model descriptions
Automate translation to and from simulator independent formats. Improve Brian2 simulator's interoperability with simulator independent model description languages.

Student: Snigdha Dagar
Mentor: Marcel Stimberg, Brian

ConnectiviPy - python module for brain connectivity analysis based on MVAR
Connectivity estimation is one of the most important problem in EEG/MEG studies. Many estimators work correctly in different applications, so the python tool which allows to have them in one place could be very useful. Main goal of the project is to design and write light and extendable module open for many data formats.

Student: Dominik Krzemiński
Mentor: Maciej Kaminski, Poland

Convert several large-scale thalamocortical models to NeuroML & PyNN
A motivated ASU Neuroscience PhD student, advised by Dr. Sharon Crook, who has extensive professional full-time software development experience, proposes to convert three, or more, if time permits, large-scale models of thalamocortical systems to PyNN and NeuroML. Such conversions to open, simulator-independent formats would increase the re-use of model components and make models available to greater number of simulation platforms.

Student: Justas Birgiolas
Mentors: Andrew Davison, France and Padraig Gleeson, UK

Ease of Deployment and Standalone Installation – BioStars
BioStars is a QnA software adaptable to any audience. Such a software should ideally have a one-step installation with easy maintenance. This project aims to ease the deployment of BioStars in 3 steps: 1. Using Ansible scripts to automate the installation and configuration of BioStars on web servers in Docker containers. 2. Writing ansible package to host BioStar on GCE 3. Making BioStars into a standalone executable to run locally on Windows with a simple and intuitive installation.

Student: Lohitaksh Parmar
Mentors: Roman Valls and Albert Istvan, BioStars

GPU implementations for MOOSE
Two solvers, one for single neurone calculations and one for chemical reaction- diffusion calculations, are both computationally demanding. They are heavily used in MOOSE project and they affect the overall computational efficiency. Previously a basic GPU implementation has been done but its calculation doesn’t give any speedup compared to serial calculation on CPU. A scalable and optimised GPU parallelisation algorithm needs to be implemented based on the previous work.

Student: Hu Wenyan
Mentors: Upinder S. Bhalla and Aviral Goel, India

Improving the graphical model exploration environment for PyDSTool
PyDSTool is a Python library for simulating and exploring the activity of dynamical systems. We present a new graphical user interface and interactive toolkit aimed at allowing neuroscientists to develop data-driven models of brain activity. The project includes a new UI, coherent redesign of supporting classes, and a suite of relevant neural dynamical systems test cases. We also include documentation and tutorials for new users of PyDSTool.

Student: Alex Kuefler
Mentor: Rob Clewley, PyDSTool

iRODS client for ImageJ
ImageJ is a public domain Java image processing program designed with an open architecture that provides extensibility via Java plugins. The aim of this project is to develop an ImageJ plugin which can be used to download/upload datasets from/to iRODS (Integrated Rule-Oriented Data System), an open source data management software used by the research community in order to take control of their data, regardless of where and on what device the data is stored.

Student: Doru Gucea
Mentors: Dimiter Prodanov, Belgium and Visakh Muraleedharan, INCF Secretariat

Language bindings for the NIX file format
NIX project is implemented in C++ and scientists/developers might face the limitation of benefiting from these efficient scientific data-set processing modules due to difference in their computational environment. This project focuses on developing Java bindings for NIX by interfacing the C++ library using SWIG and hence bridging the gap between NIX modules and Java users as Java is popularly in scientific computing.

Student: Sujith Vadakkepat
Mentors: Adrian Stoewer, Andrey Sobolev, and Christian Kellner, Germany

Modelling Biophysically Accurate Ion Channels for OpenWorm
The OpenWorm project is an attempt to build the first digital life form using open source code. The fidelity of OpenWorm to its biological counterpart, C. elegans, depends on the realism of its constituent parts, such as computationally-modelled cells. The internal dynamics of these cells are largely controlled by ion channels, so a biophysically-informed ion channel model will, in-turn, support a realistic model of the entire organism.

Student: Travis Jacobs
Mentors: Padraig Gleeson and Stephen Larson, OpenWorm

Network complexity for connectomes: New code for big samples in C-PAC
This projects aims for extending the capabilities of the existing workflow system of C-PAC with state of the art methods. The focus is set on the implementation of methods for the analysis of non linear time series (Lizier, Heinzle, Horstmann, Haynes, & Prokopenko, 2011; Razak & Jensen, 2013; Vicente, Wibral, Lindner, & Pipa, 2011) and graph theoretic approaches (Bullmore & Sporns, 2009; Lohmann et al., 2009; Ralaivola, 2013) for the analysis of network metrics and network complexity measures.

Student: Florian Gesser
Mentors: Ivan Roijals-Miras, Sweden and Cameron Craddock, US

Off-line mobile client for EEGBase
INCF has a web portal and Andoid client app to gather experimental data. Mobile app connects with the server to sync data.Currently mobile app cannot work offline.Inconvenient in environments without internet connection Task1: Implement an embedded database into the mobile client allowing the users to work offline and store experimental data in the local db, app syncs data with server when online Task2: enable client app to work with different templates where user can edit them to store data.
Student: Isuru Chamara
Mentor: Petr Ježek, Czech Republic

Power/ARM support for the Cyme library
The Cyme library provides wrapper functions for math operations, while performing vectorization to maximize hardware utilization. It is currently only supported on the PowerPC A2 processor. My main goal is to extend the back-end support for the Cyme library, so that it can be run on Power7, Power8, and ARM processors.

Student: Kai Langen
Mentor: Timothee Evart, Blue Brain project

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