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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

Integration error as a function of the number of iterations. Solid curves indicate cubic interpolation, dashed curves linear interpolation. Filled circles show results for the communication interval of NEST communication, open circles show the results for communication in every time step h. Color indicates the hardware system; in this and all subsequent figures shades of green represent workstations (here) or shared memory cluster node. The RMSE ϵ of the membrane potential was measured over 1 s of biological time. The step size h was chosen as 0.05 ms leading to  time steps within one minimal delay communication interval. (A) RMSE for different numbers of iterations. (B) Mean number of iterations when using the iteration control with default settings (prelim_tol chosen as 10−4 and a maximum of 15 iterations, which was not reached for any simulation interval).
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Figure 6: Integration error as a function of the number of iterations. Solid curves indicate cubic interpolation, dashed curves linear interpolation. Filled circles show results for the communication interval of NEST communication, open circles show the results for communication in every time step h. Color indicates the hardware system; in this and all subsequent figures shades of green represent workstations (here) or shared memory cluster node. The RMSE ϵ of the membrane potential was measured over 1 s of biological time. The step size h was chosen as 0.05 ms leading to time steps within one minimal delay communication interval. (A) RMSE for different numbers of iterations. (B) Mean number of iterations when using the iteration control with default settings (prelim_tol chosen as 10−4 and a maximum of 15 iterations, which was not reached for any simulation interval).

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)

Integration error as a function of the number of iterations. Solid curves indicate cubic interpolation, dashed curves linear interpolation. Filled circles show results for the communication interval of NEST communication, open circles show the results for communication in every time step h. Color indicates the hardware system; in this and all subsequent figures shades of green represent workstations (here) or shared memory cluster node. The RMSE ϵ of the membrane potential was measured over 1 s of biological time. The step size h was chosen as 0.05 ms leading to  time steps within one minimal delay communication interval. (A) RMSE for different numbers of iterations. (B) Mean number of iterations when using the iteration control with default settings (prelim_tol chosen as 10−4 and a maximum of 15 iterations, which was not reached for any simulation interval).
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4563270&req=5

Figure 6: Integration error as a function of the number of iterations. Solid curves indicate cubic interpolation, dashed curves linear interpolation. Filled circles show results for the communication interval of NEST communication, open circles show the results for communication in every time step h. Color indicates the hardware system; in this and all subsequent figures shades of green represent workstations (here) or shared memory cluster node. The RMSE ϵ of the membrane potential was measured over 1 s of biological time. The step size h was chosen as 0.05 ms leading to time steps within one minimal delay communication interval. (A) RMSE for different numbers of iterations. (B) Mean number of iterations when using the iteration control with default settings (prelim_tol chosen as 10−4 and a maximum of 15 iterations, which was not reached for any simulation interval).
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