Design patterns: elements of reusable object-oriented software
Design patterns: elements of reusable object-oriented software
The design and evolution of C++
The design and evolution of C++
Parallel programming with MPI
The NEURON simulation environment
Neural Computation
Programming with POSIX threads
Programming with POSIX threads
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The book of GENESIS (2nd ed.): exploring realistic neural models with the GEneral NEural SImulation System
On numerical simulations of integrate-and-fire neural networks
Neural Computation
Graph theory and its applications
Graph theory and its applications
Simulating, Analyzing, and Animating Dynamical Systems: A Guide Toi Xppaut for Researchers and Students
The C++ Programming Language, Third Edition
The C++ Programming Language, Third Edition
Data Structures and Algorithms
Data Structures and Algorithms
Event-driven simulation of spiking neurons with stochastic dynamics
Neural Computation
On embedding synfire chains in a balanced network
Neural Computation
Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers (2nd Edition)
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
GNU Scientific Library Reference Manual - 2nd Edition
GNU Scientific Library Reference Manual - 2nd Edition
Memory Capacity of Balanced Networks
Neural Computation
Exact simulation of integrate-and-fire models with synaptic conductances
Neural Computation
Spike-Timing-Dependent Plasticity in Balanced Random Networks
Neural Computation
The high-conductance state of cortical networks
Neural Computation
Anatomy of a cortical simulator
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Brain-scale simulation of the neocortex on the IBM Blue Gene/L supercomputer
IBM Journal of Research and Development
Correlations and population dynamics in cortical networks
Neural Computation
2008 Special Issue: The state of MIIND
Neural Networks
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
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
High-conductance states in a neuromorphic hardware system
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Efficient simulation of large-scale spiking neural networks using CUDA graphics processors
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Efficient parallelization of a dynamic global vegetation model with river routing
Environmental Modelling & Software
Efficient neural models for visual attention
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
Vectorized algorithms for spiking neural network simulation
Neural Computation
Spiking neural network simulation: memory-optimal synaptic event scheduling
Journal of Computational Neuroscience
Journal of Computational Neuroscience
A hierachical configuration system for a massively parallel neural hardware platform
Proceedings of the 9th conference on Computing Frontiers
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
Multithreaded and distributed simulation of large biological neuronal networks
PVM/MPI'07 Proceedings of the 14th European conference on Recent Advances in Parallel Virtual Machine and Message Passing Interface
High-capacity embedding of synfire chains in a cortical network model
Journal of Computational Neuroscience
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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 contrast to earlier approaches, a wide class of neuron models and synaptic dynamics can be represented.