Including Gap Junctions into Distributed Neuronal Network Simulations

Hahne, Jan; Wylie, Brian; Frommer, Andreas; Bolten, Matthias; Igarashi, Jun; Diesmann, Markus; Helias, Moritz; Kitayama, Itaru; Kunkel, Susanne

doi:10.1007/978-3-319-50862-7_4

Contribution to a conference proceedings/Contribution to a book

FZJ-2017-00070

Including Gap Junctions into Distributed Neuronal Network Simulations

Hahne, J. (Corresponding author) ; Helias, M.FZJ* ; Kunkel, S.FZJ* ; Igarashi, J. ; Kitayama, I. ; Wylie, B.FZJ* ; Bolten, M. ; Frommer, A. ; Diesmann, M.FZJ*

2016
Springer International Publishing Cham
ISBN: 978-3-319-50861-0 (print), 978-3-319-50862-7 (electronic)

Brain-Inspired Computing / Amunts, Katrin (Editor) ; Cham : Springer International Publishing, 2016, Chapter 4 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-319-50861-0=978-3-319-50862-7 ; doi:10.1007/978-3-319-50862-7
International Workshop on Brain-Inspired Computing, BrainComp 2015, Cetraro, Italy, 6 Jul 2015 - 10 Jul 2015 Cham : Springer International Publishing, Lecture Notes in Computer Science 10087, 43 - 57 (2016) [10.1007/978-3-319-50862-7_4]

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Please use a persistent id in citations: doi:10.1007/978-3-319-50862-7_4

Abstract: Contemporary simulation technology for neuronal networks enables the simulation of brain-scale networks using neuron models with a single or a few compartments. However, distributed simulations at full cell density are still lacking the electrical coupling between cells via so called gap junctions. This is due to the absence of efficient algorithms to simulate gap junctions on large parallel computers. The difficulty is that gap junctions require an instantaneous interaction between the coupled neurons, whereas the efficiency of simulation codes for spiking neurons relies on delayed communication. In a recent paper [15] we describe a technology to overcome this obstacle. Here, we give an overview of the challenges to include gap junctions into a distributed simulation scheme for neuronal networks and present an implementation of the new technology available in the NEural Simulation Tool (NEST 2.10.0). Subsequently we introduce the usage of gap junctions in model scripts as well as benchmarks assessing the performance and overhead of the technology on the supercomputers JUQUEEN and K computer.

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