Description: A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations

This title appears in the Scientific Report : 2015

A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations

Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of b...

Personal Name(s):	Hahne, Jan (Corresponding author)
	Helias, Moritz / Kunkel, Susanne / Igarashi, Jun / Bolten, Matthias / Frommer, Andreas / Diesmann, Markus
Contributing Institute:	Computational and Systems Neuroscience; INM-6 John von Neumann - Institut für Computing; NIC Jülich Supercomputing Center; JSC Theoretical Neuroscience; IAS-6
Published in:	Frontiers in computational neuroscience, 9 (2015) 22, S. 00022
Imprint:	Lausanne Frontiers Research Foundation 2015
PubMed ID:	26441628
DOI:	10.3389/fninf.2015.00022
Document Type:	Journal Article
Research Program:	The Next-Generation Integrated Simulation of Living Matter Theory of multi-scale neuronal networks The Human Brain Project Computational Science and Mathematical Methods Theory, modelling and simulation SimLab Neuroscience Scalable solvers for linear systems and time-dependent problems Brain-inspired multiscale computation in neuromorphic hybrid systems
Link:	OpenAccess OpenAccess
	Publikationsportal JuSER

Please use the identifier: http://dx.doi.org/10.3389/fninf.2015.00022 in citations.
Please use the identifier: http://hdl.handle.net/2128/9338 in citations.

Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. We show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy.