Neuroscience is witnessing increasing knowledge about the anatomy and electrophysiological properties of neurons and their connectivity, leading to an ever increasing computational complexity of neural simulations. At the same time, a rather radical change in personal computer technology emerges with the establishment of multi-cores: high-density, explicitly parallel processor architectures for both high performance as well as standard desktop computers. This work introduces strategies for the parallelization of biophysically realistic neural simulations based on the compartmental modeling technique and results of such an implementation, with a strong focus on multi-core architectures and automation, i.e. user-transparent load balancing
NEST is a widely used tool to simulate biological spiking neural networks [1]. The simulator is subj...
Computer simulations allow researchers to explore processes underlying neural functions. Biologicall...
Current brain simulators do no scale linearly to realistic problem sizes (e.g. >100,000 neurons),...
Neuroscience is witnessing increasing knowledge about the anatomy and electrophysiological propertie...
Parallelism and distribution have been considered the key features of neural processing. The term pa...
Supercomputing is increasingly available in neuroscience and boosts the ability to create models wit...
Over the last couple of years, supercomputers such as the Blue Gene/Q system JUQUEEN in Jülich and t...
The simulation of brain areas (e.g. the visual cortex), comprising huge networks of integrate & ...
Large-scale simulations of neuronal networks provide a unique view onto brain dynamics, complementin...
Abstract-We describe a neural simulator designed for simulating very large scale models of cortical ...
[Abstract] Background: The human brain is the most complex system in the known universe, it is there...
The need for reproducible, credible, multiscale biological modeling has led to the development of st...
Biological neuronal networks models can be investigated with the NEST simulator (Gewaltig and Diesma...
Multicore neuromorphic platforms come with a custom library for efficient development of neural netw...
Despite the great strides neuroscience has made in recent decades, the underlying principles of brai...
NEST is a widely used tool to simulate biological spiking neural networks [1]. The simulator is subj...
Computer simulations allow researchers to explore processes underlying neural functions. Biologicall...
Current brain simulators do no scale linearly to realistic problem sizes (e.g. >100,000 neurons),...
Neuroscience is witnessing increasing knowledge about the anatomy and electrophysiological propertie...
Parallelism and distribution have been considered the key features of neural processing. The term pa...
Supercomputing is increasingly available in neuroscience and boosts the ability to create models wit...
Over the last couple of years, supercomputers such as the Blue Gene/Q system JUQUEEN in Jülich and t...
The simulation of brain areas (e.g. the visual cortex), comprising huge networks of integrate & ...
Large-scale simulations of neuronal networks provide a unique view onto brain dynamics, complementin...
Abstract-We describe a neural simulator designed for simulating very large scale models of cortical ...
[Abstract] Background: The human brain is the most complex system in the known universe, it is there...
The need for reproducible, credible, multiscale biological modeling has led to the development of st...
Biological neuronal networks models can be investigated with the NEST simulator (Gewaltig and Diesma...
Multicore neuromorphic platforms come with a custom library for efficient development of neural netw...
Despite the great strides neuroscience has made in recent decades, the underlying principles of brai...
NEST is a widely used tool to simulate biological spiking neural networks [1]. The simulator is subj...
Computer simulations allow researchers to explore processes underlying neural functions. Biologicall...
Current brain simulators do no scale linearly to realistic problem sizes (e.g. >100,000 neurons),...