By Lucio Grandinetti, Thomas Lippert, Nicolai Petkov
This ebook constitutes the completely refereed convention court cases of the overseas Workshop on Brain-inspired Computing, BrainComp 2013, held in Cetraro, Italy, in July 2013. The sixteen revised complete papers have been rigorously reviewed and chosen from various submissions and canopy subject matters comparable to mind constitution and serve as as a neuroscience viewpoint, computational types and brain-inspired computing, HPC and visualization for human mind simulations.
Read or Download Brain-Inspired Computing: International Workshop, BrainComp 2013, Cetraro, Italy, July 8-11, 2013, Revised Selected Papers PDF
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Additional info for Brain-Inspired Computing: International Workshop, BrainComp 2013, Cetraro, Italy, July 8-11, 2013, Revised Selected Papers
Biol. 5, e1000433 (2009) Integrating Brain Structure and Dynamics on Supercomputers 31 10. : The NEURON simulation environment. Neural Comput. 9, 1179–1209 (1997) 11. : The Book of GENESIS: Exploring realistic neural models with the GEneral NEural SImulation System. Springer, New York (1995) 12. : Python scripting in the Nengo simulator. Front. Neuroinf. 3, 7 (2009) 13. : The Brian simulator. Front. Neurosci. 3, 192–197 (2009) 14. : Exact subthreshold integration with continuous spike times in discrete time neural network simulations.
This ensured that an instrument useful for basic science was constructed, and appropriate simulation software was available when the computer went online. It is our hope that this success story of peta-scale systems based on a continuous dialogue between hardware developers, system-level developers, and application developers can be repeated for the upcoming exa-scale era. The time spent on basic development work at an instrument that is already online should be reduced to a minimum. Scientists are eager to use the short and costly lifespan of a supercomputer to address neuroscience challenges.
5 Neuronal Network Simulations are Limited by the Available Memory Simulations of highly connected neuronal networks require large amounts of working memory, which for brain-scale networks is only provided by supercomputers. The maximum-ﬁlling NEST simulation of nearly two billion neurons on the K supercomputer is still about two orders of magnitude away from the full scale of the human brain. Although a substantial amount of time can be spent on neuronal updates especially for plastic synapses, large-scale neuronal network simulations are thus nevertheless mainly constrained by memory rather than CPU power, as opposed for instance to molecular dynamics simulations.