ArticleFrontiers in neuroinformatics2020
Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions.
Article in Frontiers in neuroinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units.Brain sciences · 2022Review
- Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda.Neuroinformatics · 2021Article
- Event-Based Update of Synapses in Voltage-Based Learning Rules.Frontiers in neuroinformatics · 2021Article
- Routing Brain Traffic Through the Von Neumann Bottleneck: Parallel Sorting and Refactoring.Frontiers in neuroinformatics · 2021Article
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4 authors.
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Abstract
Investigating the dynamics and function of large-scale spiking neuronal networks with realistic numbers of synapses is made possible today by state-of-the-art simulation code that scales to the largest contemporary supercomputers. However, simulations that involve electrical interactions, also called gap junctions, besides chemical synapses scale only poorly due to a communication scheme that collects global data on each compute node. In comparison to chemical synapses, gap junctions are far less abundant. To improve scalability we exploit this sparsity by integrating an existing framework for continuous interactions with a recently proposed directed communication scheme for spikes. Using a reference implementation in the NEST simulator we demonstrate excellent scalability of the integrated framework, accelerating large-scale simulations with gap junctions by more than an order of magnitude. This allows, for the first time, the efficient exploration of the interactions of chemical and electrical coupling in large-scale neuronal networks models with natural synapse density distributed across thousands of compute nodes.
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