Advancing the Boundaries of High-Connectivity Network Simulation with Distributed Computing

TLDR

Computational neuroscience relies on efficient simulation tools, yet the vast number of neurons and synapses in large cortical networks imposes severe constraints on the size of systems that can be explored. The study introduces new techniques that eliminate the fundamental obstacle of the enormous number of synaptic contacts per neuron in biological neural network simulations. These techniques are integrated into a coherent simulation tool that distributes individual simulations across multiple computers, enabling investigation of networks orders of magnitude larger than previously possible. The resulting software scales excellently across diverse hardware, supports interactive iterative development, produces rapid results for very large networks, and accommodates a wide class of neuron models and synaptic dynamics unlike earlier approaches.

Abstract

The availability of efficient and reliable simulation tools is one of the mission-critical technologies in the fast-moving field of computational neuroscience. Research indicates that higher brain functions emerge from large and complex cortical networks and their interactions. The large number of elements (neurons) combined with the high connectivity (synapses) of the biological network and the specific type of interactions impose severe constraints on the explorable system size that previously have been hard to overcome. Here we present a collection of new techniques combined to a coherent simulation tool removing the fundamental obstacle in the computational study of biological neural networks: the enormous number of synaptic contacts per neuron. Distributing an individual simulation over multiple computers enables the investigation of networks orders of magnitude larger than previously possible. The software scales excellently on a wide range of tested hardware, so it can be used in an interactive and iterative fashion for the development of ideas, and results can be produced quickly even for very large networks. In con-trast to earlier approaches, a wide class of neuron models and synaptic dynamics can be represented.