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.
