Literature DB >> 15969917

Advancing the boundaries of high-connectivity network simulation with distributed computing.

Abigail Morrison1, Carsten Mehring, Theo Geisel, A D Aertsen, Markus Diesmann.   

Abstract

The availability of efficient and reliable simulation tools is one of the mission-critical technologies in the fast-moving field of computational neuroscience. Research indicates that higher brain functions emerge from large and complex cortical networks and their interactions. The large number of elements (neurons) combined with the high connectivity (synapses) of the biological network and the specific type of interactions impose severe constraints on the explorable system size that previously have been hard to overcome. Here we present a collection of new techniques combined to a coherent simulation tool removing the fundamental obstacle in the computational study of biological neural networks: the enormous number of synaptic contacts per neuron. Distributing an individual simulation over multiple computers enables the investigation of networks orders of magnitude larger than previously possible. The software scales excellently on a wide range of tested hardware, so it can be used in an interactive and iterative fashion for the development of ideas, and results can be produced quickly even for very large networks. In contrast to earlier approaches, a wide class of neuron models and synaptic dynamics can be represented.

Mesh:

Year:  2005        PMID: 15969917     DOI: 10.1162/0899766054026648

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  63 in total

1.  Functional consequences of correlated excitatory and inhibitory conductances in cortical networks.

Authors:  Jens Kremkow; Laurent U Perrinet; Guillaume S Masson; Ad Aertsen
Journal:  J Comput Neurosci       Date:  2010-05-19       Impact factor: 1.621

2.  Spiking neural network simulation: memory-optimal synaptic event scheduling.

Authors:  Robert D Stewart; Kevin N Gurney
Journal:  J Comput Neurosci       Date:  2010-11-03       Impact factor: 1.621

3.  Gating of signal propagation in spiking neural networks by balanced and correlated excitation and inhibition.

Authors:  Jens Kremkow; Ad Aertsen; Arvind Kumar
Journal:  J Neurosci       Date:  2010-11-24       Impact factor: 6.167

4.  Fast numerical methods for simulating large-scale integrate-and-fire neuronal networks.

Authors:  Aaditya V Rangan; David Cai
Journal:  J Comput Neurosci       Date:  2006-07-28       Impact factor: 1.621

5.  Parallel network simulations with NEURON.

Authors:  M Migliore; C Cannia; W W Lytton; Henry Markram; M L Hines
Journal:  J Comput Neurosci       Date:  2006-05-26       Impact factor: 1.621

Review 6.  Simulation of networks of spiking neurons: a review of tools and strategies.

Authors:  Romain Brette; Michelle Rudolph; Ted Carnevale; Michael Hines; David Beeman; James M Bower; Markus Diesmann; Abigail Morrison; Philip H Goodman; Frederick C Harris; Milind Zirpe; Thomas Natschläger; Dejan Pecevski; Bard Ermentrout; Mikael Djurfeldt; Anders Lansner; Olivier Rochel; Thierry Vieville; Eilif Muller; Andrew P Davison; Sami El Boustani; Alain Destexhe
Journal:  J Comput Neurosci       Date:  2007-07-12       Impact factor: 1.621

7.  Conditions for propagating synchronous spiking and asynchronous firing rates in a cortical network model.

Authors:  Arvind Kumar; Stefan Rotter; Ad Aertsen
Journal:  J Neurosci       Date:  2008-05-14       Impact factor: 6.167

8.  Fully implicit parallel simulation of single neurons.

Authors:  Michael L Hines; Henry Markram; Felix Schürmann
Journal:  J Comput Neurosci       Date:  2008-04-01       Impact factor: 1.621

9.  Correlations in spiking neuronal networks with distance dependent connections.

Authors:  Birgit Kriener; Moritz Helias; Ad Aertsen; Stefan Rotter
Journal:  J Comput Neurosci       Date:  2009-07-01       Impact factor: 1.621

10.  Accuracy evaluation of numerical methods used in state-of-the-art simulators for spiking neural networks.

Authors:  Stephan Henker; Johannes Partzsch; René Schüffny
Journal:  J Comput Neurosci       Date:  2011-08-12       Impact factor: 1.621

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