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

Hahne J, Helias M, Kunkel S, Igarashi J, Bolten M, Frommer A, Diesmann M - Front Neuroinform (2015)

Bottom Line: 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.To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers.Finally, we discuss limitations of the novel technology.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Science, Bergische Universität Wuppertal Wuppertal, Germany.

ABSTRACT
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. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology.

No MeSH data available.


Related in: MedlinePlus

Effect of membrane potential interpolation on network error. (A) RMSE ϵapprox of linear (dashed curves) and cubic (solid curves) interpolation for the action potential shown in Figure 2 as a function of the computation step size h. (B) Integration error for the two-neuron network (Figure 7) as a function of the interpolation error shown in (A).
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Figure 8: Effect of membrane potential interpolation on network error. (A) RMSE ϵapprox of linear (dashed curves) and cubic (solid curves) interpolation for the action potential shown in Figure 2 as a function of the computation step size h. (B) Integration error for the two-neuron network (Figure 7) as a function of the interpolation error shown in (A).

Mentions: Figure 6A shows the functionality of the iterative method by measuring the error ϵ in the membrane potential for different numbers of iterations. The RMSE decreases with every iteration until it converges to some plateau error. The plateau error depends on the used interpolation order and is independent of the employed communication strategy. Its origin will be discussed later in Figure 8. As expected, a faster convergence is reached with the h-step communication, while the communication in intervals of the minimal delay takes a few more iterations. The lower panel (Figure 6B) shows the mean number of iterations when the same simulation is run with the iteration control described in Section 2.1.2. The number of needed iterations is mostly independent of the step size h and the used interpolation order, but differs by about four iterations for the different communication strategies.


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

Hahne J, Helias M, Kunkel S, Igarashi J, Bolten M, Frommer A, Diesmann M - Front Neuroinform (2015)

Effect of membrane potential interpolation on network error. (A) RMSE ϵapprox of linear (dashed curves) and cubic (solid curves) interpolation for the action potential shown in Figure 2 as a function of the computation step size h. (B) Integration error for the two-neuron network (Figure 7) as a function of the interpolation error shown in (A).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4563270&req=5

Figure 8: Effect of membrane potential interpolation on network error. (A) RMSE ϵapprox of linear (dashed curves) and cubic (solid curves) interpolation for the action potential shown in Figure 2 as a function of the computation step size h. (B) Integration error for the two-neuron network (Figure 7) as a function of the interpolation error shown in (A).
Mentions: Figure 6A shows the functionality of the iterative method by measuring the error ϵ in the membrane potential for different numbers of iterations. The RMSE decreases with every iteration until it converges to some plateau error. The plateau error depends on the used interpolation order and is independent of the employed communication strategy. Its origin will be discussed later in Figure 8. As expected, a faster convergence is reached with the h-step communication, while the communication in intervals of the minimal delay takes a few more iterations. The lower panel (Figure 6B) shows the mean number of iterations when the same simulation is run with the iteration control described in Section 2.1.2. The number of needed iterations is mostly independent of the step size h and the used interpolation order, but differs by about four iterations for the different communication strategies.

Bottom Line: 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.To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers.Finally, we discuss limitations of the novel technology.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Science, Bergische Universität Wuppertal Wuppertal, Germany.

ABSTRACT
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. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology.

No MeSH data available.


Related in: MedlinePlus