Sub-realtime simulation of a neuronal network of natural density

TL;DR

This paper demonstrates that a full-scale neuronal network model of the brain's microcircuit can be simulated in less than real-time on conventional hardware, enabling advanced neuroscience and robotics applications.

Contribution

It presents a method for sub-realtime simulation of dense neuronal networks, a significant advancement over previous slower models.

Findings

Achieved run times shorter than biological time for a full-scale cortical microcircuit

Enabled sub-realtime simulation on conventional compute hardware

Supports applications in robotics and brain learning studies

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.