Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.

ABSTRACT: Spiking neural networks (SNNs) represent the state-of-the-art approach to the biologically realistic modeling of nervous system function. The systematic calibration for multiple free model parameters is necessary to achieve robust network function and demands high computing power and large memory resources. Special requirements arise from closed-loop model simulation in virtual environments and from real-time simulation in robotic application. Here, we compare two complementary approaches to efficient large-scale and real-time SNN simulation. The widely used NEural Simulation Tool (NEST) parallelizes simulation across multiple CPU cores. The GPU-enhanced Neural Network (GeNN) simulator uses the highly parallel GPU-based architecture to gain simulation speed. We quantify fixed and variable

SUBMITTER: Schmitt FJ 

PROVIDER: S-EPMC9950635 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature