Gaussian and exponential lateral connectivity on distributed spiking neural network simulation

TL;DR

This study investigates how different models of intra-areal lateral connectivity, specifically Gaussian versus exponential decay, affect the scalability and memory use of a distributed spiking neural network simulator, emphasizing the importance of long-range connectivity.

Contribution

It introduces a detailed comparison of short- and long-range intra-areal connectivity models in large-scale neural simulations, highlighting the impact on system performance and resource requirements.

Findings

Long-range exponential decay connectivity affects scaling.

Memory occupation varies with connectivity model.

Simulation performance depends on connectivity range.

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.