Parallel computation provides deeper insight into brain function | 3/24/2017 | Staff
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Unlike experimental neuroscientists who deal with real-life neurons, computational neuroscientists use model simulations to investigate how the brain functions. While many computational neuroscientists use simplified mathematical models of neurons, researchers in the Computational Neuroscience Unit at the Okinawa Institute of Science and Technology Graduate University (OIST) develop software that models neurons to the detail of molecular interactions with the goal of eliciting new insights into neuronal function. Applications of the software were limited in scope up until now because of the intense computational power required for such detailed neuronal models, but recently Dr. Weiliang Chen, Dr. Iain Hepburn, and Professor Erik De Schutter published two related papers in which they outline the accuracy and scalability of their new high-speed computational software, "Parallel STEPS". The combined findings suggest that Parallel STEPS could be used to reveal new insights into how individual neurons function and communicate with each other.

The first paper, published in The Journal of Chemical Physics in August 2016, focusses on ensuring that the accuracy of Parallel STEPS is comparable with conventional methods. In conventional approaches, computations associate with neuronal chemical reactions and molecule diffusion are all calculated on one computational processing unit or 'core' sequentially. However, Dr. Iain Hepburn and colleagues introduced a new approach to perform computations of reaction and diffusion in parallel which can then be distributed over multiple computer cores, whilst maintaining simulation accuracy to a high degree. The key was to develop an original algorithm separated into two parts - one that computed chemical reaction events and the other diffusion events.

Range - Model - Simulations - Diffusion - Models

"We tested a range of model simulations from simple diffusion models to realistic biological models and found that we could achieve improved performance using a parallel approach with minimal loss of accuracy. This demonstrated the potential suitability of the method on a larger scale," says...
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