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An efficient simulation environment for modeling large-scale cortical processing

, , , and . Frontiers in Neuroinformatics, (2011)
DOI: 10.3389/fninf.2011.00019

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Neuromorphic modeling abstractions and simulation of large-scale cortical networks., , , and . ICCAD, page 334-338. IEEE Computer Society, (2011)Novel Brain-Derived Algorithms Scale Linearly with Number of Processing Elements., , , , , , , and . PARCO, volume 15 of Advances in Parallel Computing, page 767-776. IOS Press, (2007)Design space exploration and parameter tuning for neuromorphic applications., , , and . CODES+ISSS, page 20:1-20:2. IEEE, (2013)A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors, , , , and . Neural Networks, 22 (5-6): 791--800 (July 2009)Computing Spike-based Convolutions on GPUs., , , and . ISCAS, page 1917-1920. IEEE, (2009)An efficient simulation environment for modeling large-scale cortical processing, , , and . Frontiers in Neuroinformatics, (2011)Live Demonstration: Computing Spike-based Convolutions on GPUs., , , and . ISCAS, page 1921. IEEE, (2009)Towards reverse engineering the brain: Modeling abstractions and simulation frameworks., , , and . VLSI-SoC, page 1-6. IEEE, (2010)Efficient simulation of large-scale Spiking Neural Networks using CUDA graphics processors., , , , and . IJCNN, page 2145-2152. IEEE Computer Society, (2009)