Grid Framework for Parallel Investigations of Spiking Neural Microcircuits

Abstract

Simulation of spiking neural networks is computationally expensive and the employment of multicore processors can boost the performance of such simulations. Designing parallelization strategies that work well for different characteristics of the microcircuits entails expensive computations, leading to increased development times. To speed up the design of multicore software for computational neuroscience, we have developed a framework that exploits multicore systems available in grid computing environments. Due to the use of Grid SFEA plugins, common operations such as evaluation of parallelization strategies can be undertaken with very little effort. We evaluated the plugins for the development of a synchronous multicore spiking neural simulator. This uses the spike response model combined with the phenomenological model of spike time dependent synapse plasticity. The parallelization uses OpenMP, the microcircuits have small world topologies and count up to 104 neurons and 107 synapses with biological details. With this novel framework more complex investigations in computational neuroscience such as analysis of the dynamics of neural microcircuits could be tackled.