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A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling

Roger V. Hoang, Devyani Tanna, Laurence C. Jayet Bray, Sergiu M. Dascalu, Frederick C. Harris Jr.
Dept. of Computer Science and Engineering, University of Nevada, Reno, NV, USA
Frontiers in Neuroinformatics, 2013

@article{hoang2013novel,

   title={A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling},

   author={Hoang, Roger V and Tanna, Devyani and Jayet Bray, Laurence C and Dascalu, Sergiu M and Harris, Frederick C},

   journal={Frontiers in Neuroinformatics},

   volume={7},

   pages={19},

   year={2013},

   publisher={Frontiers}

}

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Computational Neuroscience is an emerging field that provides unique opportunities to study complex brain structures through realistic neural simulations. However, as biological details are added to models, the execution time for the simulation becomes longer. Graphics Processing Units (GPUs) are now being utilized to accelerate simulations due to their ability to perform computations in parallel. As such, they have shown significant improvement in execution time compared to Central Processing Units (CPUs). Most neural simulators utilize either multiple CPUs or a single GPU for better performance, but still show limitations in execution time when biological details are not sacrificed. Therefore, we present a novel CPU/GPU simulation environment for large-scale biological networks, the NeoCortical Simulator version 6 (NCS6). NCS6 is a free, open-source, parallelizable, and scalable simulator, designed to run on clusters of multiple machines, potentially with high performance computing devices in each of them. It has built-in leaky-integrate-and-fire (LIF) and Izhikevich (IZH) neuron models, but users also have the capability to design their own plug-in interface for different neuron types as desired. NCS6 is currently able to simulate one million cells and 100 million synapses in quasi real time by distributing data across eight machines with each having two video cards.
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