Literature DB >> 22465805

Power-efficient simulation of detailed cortical microcircuits on SpiNNaker.

Thomas Sharp1, Francesco Galluppi, Alexander Rast, Steve Furber.   

Abstract

Computer simulation of neural matter is a promising methodology for understanding the function of the brain. Recent anatomical studies have mapped the intricate structure of cortex, and these data have been exploited in numerous simulations attempting to explain its function. However, the largest of these models run inconveniently slowly and require vast amounts of electrical power, which hinders useful experimentation. SpiNNaker is a novel computer architecture designed to address these problems using low-power microprocessors and custom communication hardware. We use four SpiNNaker chips (of a planned fifty thousand) to simulate, in real-time, a cortical circuit of ten thousand spiking neurons and four million synapses. In this simulation, the hardware consumes 100 nJ per neuron per millisecond and 43 nJ per postsynaptic potential, which is the smallest quantity reported for any digital computer. We argue that this approaches fast, power-feasible and scientifically useful simulations of large cortical areas.
Copyright © 2012 Elsevier B.V. All rights reserved.

Mesh:

Year:  2012        PMID: 22465805     DOI: 10.1016/j.jneumeth.2012.03.001

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  12 in total

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6.  Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model.

Authors:  Sacha J van Albada; Andrew G Rowley; Johanna Senk; Michael Hopkins; Maximilian Schmidt; Alan B Stokes; David R Lester; Markus Diesmann; Steve B Furber
Journal:  Front Neurosci       Date:  2018-05-23       Impact factor: 4.677

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Journal:  Front Neurosci       Date:  2018-11-20       Impact factor: 4.677

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Journal:  Front Neurosci       Date:  2014-05-30       Impact factor: 4.677

9.  Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware.

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