Simulation of Biological Neural Microcircuits on Multi-core Systems

Abstract

Our research focuses on the identification and quantification of the impact that multi-core parallelization strategies have on the stability of the result of spiking neural networks simulations. We investigated OpenMP-based implementations of the Spike Response Model and Spike Time-Dependent Plasticity for studying behaviors of biological neurons and synapses. The underlying neural microcircuits have small-world topologies. The simulation strategy is a synchronous one. The software development methodology we follow makes use of systematic unit testing and continuous integration, giving us a way to verify various perturbations of simulation results. We carried out investigations on systems having different multi-core processors. The processing speed (spikes/second) of our simulator scales well with the number of cores, but the parallel efficiency is moderate when all cores of the system are used in the simulation (0.57 for 12 cores e.g.). The primary outcomes of this work are twofold: One the one hand, the proposed parallel simulation strategies show a dynamic behavior unaltered by the use of multi-core specific technologies. On the other hand, we analyze issues met in our approach to multi-core simulations.

Multicore asynchronous simulation of spiking neural networks on the grid

“…Our approach to dynamics analysis is based on the work [11], which focuses on metrics for the investigation of dynamical systems. Our previous efforts in the field of neural networks using the computing power of the Grid is described in [12]. The paper describes a framework that exploits multicore systems available in grid computing environments.…”

Dynamics Analysis of Parallel Simulations of the Spike Response Model

“…The slow adoption of multi-and manycore specific technologies in computational neurosciences is a fact, especially in simulation applications where the dynamics behavior of neural microcircuits is critical. The impact of parallel simulation strategies on the almost chaotic nature of the behavior of brain microcircuits needs to be understood [1]. In our research, we focus on the identification and quantification of the impact that multi-core parallelization strategies have on the stability of the result of spiking neural networks simulations [2], [3].…”