A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations.
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.
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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 |
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Mentions: The discrimination between the preliminary updates and the final update is necessary, since during a preliminary update the neuron will not issue any spiking events, as shown in Algorithm 2. The incoming spiking events in each iteration are hence the same. On the other hand, only within a preliminary update a neuron will send secondary events conveying the interpolation of its membrane potential to its peers. The final, non-preliminary update conveys the extrapolation of the membrane potential to the other neurons, which will be used in the first iteration of the next time step. Figure 5 shows the realization of the iterative update process for two neurons with special focus on the communication of the interpolation coefficients. The first computation of the time step is calculated with a constant extrapolation of the membrane potential of the connected neurons. In every further iteration of the same time interval the interpolation generated with the last iteration is used. Accordingly the interpolation of the current membrane potential is computed during preliminary iterations, while for the final iteration a constant extrapolation is send to the scheduler. Thereby the interpolation coefficients are computed as described in Section 2.1.2 and saved in an array. The same applies for the receiving side (Algorithm 3), where the coefficients from the incoming connections are accumulated as described in Section 2.1.2. |
View Article: PubMed Central - PubMed
Affiliation: Department of Mathematics and Science, Bergische Universität Wuppertal Wuppertal, Germany.
No MeSH data available.