Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions.

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 integrate

SUBMITTER: Jordan J 

PROVIDER: S-EPMC7214808 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature