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Recent experimental neuroscience studies are pointing out the role oflong-range intra-areal connectivity that can be modeled by a distance dependentexponential decay of the synaptic probability distribution. This short reportprovides a preliminary measure of the impact of exponentially decaying lateralconnectivity compared to that of shorter-range Gaussian decays on the scalingbehaviour and memory occupation of a distributed spiking neural networksimulator (DPSNN). Two-dimensional grids of cortical columns composed bypoint-like spiking neurons have been connected by up to 30 billion synapsesusing exponential and Gaussian connectivity models. Up to 1024 hardware cores,hosted on a 64 nodes server platform, executed the MPI processes composing thedistributed simulator. The hardware platform was a cluster of IBM NX360 M516-core compute nodes, each one containing two Intel Xeon Haswell 8-coreE5-2630 v3 processors, with a clock of 2.40GHz, interconnected through anInfiniBand network. This study is conducted in the framework of the CORTICONICFET project, also in view of the next -to-start activities foreseen as part ofthe Human Brain Project (HBP), SubProject 3 Cognitive and Systems Neuroscience,WaveScalES work-package.

Impact of exponential long range and Gaussian short range lateral  connectivity on the distributed simulation of neural networks including up to  30 billion synapses