TY  - JOUR
AU  - Hahne, Jan
AU  - Helias, Moritz
AU  - Kunkel, Susanne
AU  - Igarashi, Jun
AU  - Bolten, Matthias
AU  - Frommer, Andreas
AU  - Diesmann, Markus
TI  - A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations
JO  - Frontiers in computational neuroscience
VL  - 9
IS  - 22
SN  - 1662-5188
CY  - Lausanne
PB  - Frontiers Research Foundation
M1  - FZJ-2015-05574
SP  - 00022
PY  - 2015
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000370609700001
C6  - pmid:26441628
DO  - DOI:10.3389/fninf.2015.00022
UR  - https://juser.fz-juelich.de/record/205104
ER  -