Sub-realtime simulation of a neuronal network of natural density

Abstract

Full scale simulations of neuronal network models of the brain are challenging due to the high density of connections between neurons. This contribution reports run times shorter than the simulated span of biological time for a full scale model of the local cortical microcircuit with explicit representation of synapses on a recent conventional compute node. Realtime performance is relevant for robotics and closed-loop applications while sub-realtime is desirable for the study of learning and development in the brain, processes extending over hours and days of biological time.

Cited by in corpus (6)

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• A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations
• Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices
• Metadata practices for simulation workflows
• Phenomenological modeling of diverse and heterogeneous synaptic dynamics at natural density
• Exploiting network topology in brain-scale simulations of spiking neural networks