Brain-scale simulation of the neocortex on the IBM Blue Gene/L supercomputer

Authors:
M. Djurfeldt;M. Lundqvist;C. Johansson;M. Rehn;Ö. Ekeberg;A. Lansner
Affiliations:
Computational Biology and Neurocomputing Group, Royal Institute of Technology, Stockholm, Sweden;Computational Biology and Neurocomputing Group, Royal Institute of Technology, Stockholm, Sweden;Computational Biology and Neurocomputing Group, Royal Institute of Technology, Stockholm, Sweden;Computational Biology and Neurocomputing Group, Royal Institute of Technology, Stockholm, Sweden;Computational Biology and Neurocomputing Group, Royal Institute of Technology, Stockholm, Sweden;Computational Biology and Neurocomputing Group, Royal Institute of Technology, Stockholm, Sweden
Venue:
IBM Journal of Research and Development
Year:
2008

Citing 9
Cited 8

Modeling brain function—the world of attractor neural networks

Modeling brain function—the world of attractor neural networks
The computational brain

The computational brain
The NEURON simulation environment

Neural Computation
The book of GENESIS (2nd ed.): exploring realistic neural models with the GEneral NEural SImulation System

The book of GENESIS (2nd ed.): exploring realistic neural models with the GEneral NEural SImulation System
Neural modeling and functional brain imaging: an overview

Neural Networks - Special issue on the global brain: imaging and modelling
Neural Assemblies, an Alternative Approach to Artificial Intelligence

Neural Assemblies, an Alternative Approach to Artificial Intelligence
Computing and stability in cortical networks

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

Neural Computation
Towards cortex sized artificial neural systems

Neural Networks

2008 Special Issue: The state of MIIND

Neural Networks
The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses

Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Acceleration of hierarchical Bayesian network based cortical models on multicore architectures

Parallel Computing
Artificial neural networks in hardware: A survey of two decades of progress

Neurocomputing
On two-layer brain-inspired hierarchical topologies – a rent's rule approach –

Transactions on High-Performance Embedded Architectures and Compilers IV
Compass: a scalable simulator for an architecture for cognitive computing

SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Neural associative memories and sparse coding

Neural Networks
Nanoscale electronic synapses using phase change devices

ACM Journal on Emerging Technologies in Computing Systems (JETC) - Special issue on memory technologies

Quantified Score

Hi-index	0.00

Visualization

Abstract

Biologically detailed large-scale models of the brain can now be simulated thanks to increasingly powerful massively parallel supercomputers. We present an overview, for the general technical reader, of a neuronal network model of layers II/III of the neocortex built with biophysical model neurons. These simulations, carried out on an IBM Blue Gene/L™ supercomputer, comprise up to 22 million neurons and 11 billion synapses, which makes them the largest simulations of this type ever performed. Such model sizes correspond to the cortex of a small mammal. The SPLIT library, used for these simulations, runs on single-processor as well as massively parallel machines. Performance measurements show good scaling behavior on the Blue Gene/L supercomputer up to 8,192 processors. Several key phenomena seen in the living brain appear as emergent phenomena in the simulations. We discuss the role of this kind of model in neuroscience and note that full-scale models may be necessary to preserve natural dynamics. We also discuss the need for software tools for the specification of models as well as for analysis and visualization of output data. Combining models that range from abstract connectionist type to biophysically detailed will help us unravel the basic principles underlying neocortical function.