Published February 19, 2019 | Version v1
Conference paper Open

Gaussian and exponential lateral connectivity on distributed spiking neural network simulation

Description

We measured the impact of long-range exponentially decaying intra-areal lateral connectivity on the scaling and memory occupation of a distributed spiking neural network simulator compared to that of short-range Gaussian decays. While previous studies adopted short-range connectivity, recent experimental neurosciences studies are pointing out the role of longer-range intra-areal connectivity with implications on neural simulation platforms. Two-dimensional grids of cortical columns composed by up to 11 M point-like spiking neurons with spike frequency adaptation were connected by up the 30 G synapses using short- and long-range connectivity model.s The MPI processes composing the distributed simulator were run on up to 1024 hardware cores, hosted on a 64 nodes server platform. The hardware platform was a cluster of IBM NX360 M5 16-core compute nodes, each one containing two Intel Xeon Haswell 8-core E5-2630 v3 processors, with a clock of 2.40 G Hz, interconnected through an Infiniband network, equipped with 4xQDR switches. 

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Gaussian_and Exponential_Lateral_Connectivity.pdf

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Additional details

Funding

European Commission
ExaNeSt - European Exascale System Interconnect and Storage 671553