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Advancing the Boundaries of High-Connectivity Network Simulation with Distributed Computing

abigail{at}biologie.uni-freiburg.de, Computational Neurophysics, Institute of Biology III and Bernstein Center for Computational Neuroscience, Albert-Ludwigs-University, 79104 Freiburg, Germany

Carsten Mehring

Carsten.Mehring{at}biologie.uni-freiburg.de, Department of Zoology, Institute of Biology I and Berstein Center for Computational Neuroscience, Albert-Ludwigs-University, 79104 Freiburg, Germany

Theo Geisel

geisel{at}chaos.gwdg.de, Department of Nonlinear Dynamics, Max-Planck-Institute for Dynamics and Self Organization, 37018 Göttingen, Germany

Ad Aertsen

aertsen{at}biologie.uni-freiburg.de, Neurobiology and Biophysics, Institute of Biology III and Bernstein Center for Computational Neuroscience, Albert-Ludwigs-University, 79104 Freiburg, Germany

Markus Diesmann

diesmann{at}biologie.uni-freiburg.de, Computational Neurophysics, Institute of Biology III and Bernstein Center for Computational Neuroscience, Albert-Ludwigs-University, 79104 Freiburg, Germany

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.

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