Goals and projects

Computational Worm project


The Computational Worm project seeks to model nervous system function in c. elegans. The entire circuitry of this organism has been mapped out, and many aspects of its behaviour have been analyzed through behavioural and mutant studies. This project is based in Institute of Mathematical Sciences in Chennai under the leadership of Dr. Sitabhra Sinha.

 

MOOSE project

 

MOOSE is the Multiscale Object-Oriented Simulation Environment. Its goal is to develop a multiscale simulator capable of modelling biological and especially neuronal function from molecules to large networks and systems. It is based in NCBS Bangalore with coding efforts also in other cities, and is led by Dr. U.S. Bhalla

 

Standards efforts


 

One of the major goals of the INCF and INNNI is to promote interoperability of neuroinformatics software. There is a multitude of softwares and model description languages in the field. The reuse of models is limited by the fact that models described in one language for one software cannot be simulated using another software or combined with models simulated using another software. The group of Dr. Bhalla is involved in international efforts to specify standards for exchanging models in computational neuroscience and systems biology. These include INCF NineML standard for specifying neuronal network models, NeuroML and Systems Biology Markup Language (SBML).

 

Neuroimaging analysis

 

Modern neuroimaging has yielded a flood of data that can be usefully interrogated to provide diagnostic and epidemiological data. The group of Dr. P.K. Roy is working with many clinicians to develop techniques to automatically analyze such data to provide very early diagnosis of possible neurological conditions.

 

Modelling microtubule dynamics in neuronal growth cone

 

Computational Neuroscience Group at ISI Bangalore




 

 A cognitive, computational, and systems neuroscience laboratory located at the IIIT Hyderabad India

Our lab is validating and benchmarking large scale brain network models using clinical data to develop indvidual patient and subject specific brain network identification biomarker using the open source neuroinformatics platform The Virtual Brain.




We study the role of characteristic oscillations observed in Human EEG and develop methods to identify spectral finger prints related to Visual, motor and attention related  task.




In our lab we examine structural and functional brain networks using data from non-invasive neuroimaging techniques  (fMRI, MEG, MRI, DTI, DSI). We develop network models, network connectivity and analysis methods to determine fundamental organizational principles of both underlying anatomy and specificity of functional dynamics.