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 modelling and general purpose, well tested simulation environments such as NEURON and NEST are widely used. New, well annotated experimental data and detailed compartmental models are becoming available from the large scale brain initiatives.
However, the majority of neuronal models which have been published over the past number of years are only available in simulator specific formats, illustrating a subset of features associates with the original studies.
This work will involve converting a number of published large scale network models into open, simulator independent formats such as NeuroML and PyNN and testing them across multiple simulator implementations. They will be made freely available to the community through the Open Source Brain repository (http://www.opensourcebrain.org) for reuse, modification and extension.
Required skills: Python; XML; open source development; computational modelling experience.
Desired skills: Java experience; a background in theoretical neuroscience and/or large scale modelling.
Select a number of large scale cortical network models for the conversion & testing process.
Convert network structure and cell/synaptic properties to NeuroML and/or PyNN. Where appropriate use the simulator independent specification in LEMS to specify cell/synapse dynamics & to allow mapping to simulators. Implementing extensions to PyNN, NeuroML or other tools may be required.
Make models available on the Open Source Brain repository, along with documentation and references.
Hello, I am interested in this project. I have considerable modelling experience and am comfortable using Python. I have been trained as a computer scientist and neuroscientist, and hence have a background in theoretical neuroscience as well. I do not have much Java experience however, though I can learn about it.
Could you give me some examples of, say, a large scale cortical model you have converted and any documentation or examples of previous work in this direction?
My name is Oksana, I’m a student in Biomedial Electrical Enngineering. Because of my passion to BCI development I’m very excited about the projects like this. Before the applying for the project I’d like to know if any kind of neural network is appropriate. For example for now I’ve found pretty good materials on a neural basis of color vision and a cognitive model of writing, but do I can work on it, or should I use some among predefined ones?
Thanks for your interest in the project. I suspect that some of the network types you describe might be out of the scope of this project, simply because they may not be easy to implement in the existing form of NeuroML and/or PyNN (i…e using spiking neurons or neural mass models).
Please see the link to the Google doc I’ve just posted with previous projects, some of the NeuroML/PyNN papers, and see if there is existing code for such models on ModelDB.
I am a first-year PhD student in Computational Neurosciences and did my MSc in Biomedical Engineering, which gave me computational modelling experience, specifically in Python and MatLab. This project looks very interesting from my point of view as it seems to focus on simulating larger brain systems instead of at the cellular level. Is this correct? What are the network models that this project will focus on converting into the OBS?
NeuroML and PyNN mainly cover spiking neural networks so will have individual cells represented, but last year’s GSoC extended the language to allow neural masses to be represented in larger scale networks.
See the Google doc above for examples of the previous years’ models which have been converted as part of this. As mentioned there, it would be good for applicants to look at these, at the specification formats and select some models from ModelDB they feel would be useful to translate for this process.