Open source, cross simulator, models of cortical circuits


gsoc_2013_pynn_neuroML_nineMLStudent
Vitor Chaud, University of Sao Paulo, Brasil

MentorsPadraig Gleeson (INCF UK Node) and Andrew Davison (INCF French Node) 

An increasing number of studies are using large scale network models incorporating realistic connectivity to understand information processing in cortical structures. High performance computational resources are becoming more widely available to computational neuroscientists for this type of modeling and general purpose, well tested simulation environments such as NEURON and NEST are widely used. In addition, hardware solutions (based on custom neuromorphic hardware or off the shelf FPGA/GPU hardware) are in development, promising ever larger, faster simulations. However, there is a lack of well tested, community maintained network model examples which can work across all of these simulation solutions, which both incorporate realistic cell and network properties and provide networks of sufficient complexity to test the performance and scalability of these platforms.

This work will involve converting a number of published cortical network models into open, simulator independent formats such as PyNN, NeuroML and NineML, testing them across multiple simulator implementations and making them available to the community through the Open Source Brain repository. By converting or implementing models into these simulator-independent formats the computational neuroscience community improves important aspects to the development of the field such as model transparency, accessibility and reuse.

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