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arXiv · 1803.08833

Gaussian and exponential lateral connectivity on distributed spiking neural network simulation

Elena Pastorelli·Pier Stanislao Paolucci·Francesco Simula·Andrea Biagioni·Fabrizio Capuani·Paolo Cretaro·Giulia De Bonis·Francesca Lo Cicero·Alessandro Lonardo·Michele Martinelli·Luca Pontisso·Piero Vicini·Roberto Ammendola
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Abstract

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 adaption were connected by up to 30 G synapses using short- and long-range connectivity models. 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 4x QDR switches.

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Elena Pastorelli, Pier Stanislao Paolucci, Francesco Simula, Andrea Biagioni, Fabrizio Capuani, Paolo Cretaro, Giulia De Bonis, Francesca Lo Cicero, Alessandro Lonardo, Michele Martinelli, Luca Pontisso, Piero Vicini, Roberto Ammendola. 2019-02-19. Gaussian and exponential lateral connectivity on distributed spiking neural network simulation. https://doi.org/10.1109/pdp2018.2018.00110

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