Dynamic load balancing for distributed memory multiprocessors
Journal of Parallel and Distributed Computing
Analysis of a graph coloring based distributed load balancing algorithm
Journal of Parallel and Distributed Computing
Synchrony in excitatory neural networks
Neural Computation
The book of GENESIS (2nd ed.): exploring realistic neural models with the GEneral NEural SImulation System
Neuronal Networks of the Hippocampus
Neuronal Networks of the Hippocampus
Programming a Hypercube Multicomputer
IEEE Software
Projective Methods for Stiff Differential Equations: Problems with Gaps in Their Eigenvalue Spectrum
SIAM Journal on Scientific Computing
Telescopic projective methods for parabolic differential equations
Journal of Computational Physics
Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons
Biological Cybernetics - Special Issue: Quantitative Neuron Modeling
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
Brainstorm: a user-friendly application for MEG/EEG analysis
Computational Intelligence and Neuroscience - Special issue on academic software applications for electromagnetic brain mapping using MEG and EEG
Which model to use for cortical spiking neurons?
IEEE Transactions on Neural Networks
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
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To understand brain behaviors, it is important to directly associate the network level activities to the underlying biophysical mechanisms, which require large-scale simulations with biophysically realistic neural models like Hodgkin-Huxley models. However, when simulations are conducted on models with sufficient biophysical details, great challenges arise from limited computer power, thereby restricting most existing computational works with biophysical models only to small-scale networks. On the other hand, with the emergence of powerful computing platforms, many recent works are geared to performing large-scale simulations with simple spiking models. However, the applicability of those works is limited by the nature of the underlying phenomenological model. To bridge the gap, an intermediate step is taken to construct a scalable brain model with sufficient biophysical details. In this work, great efforts are devoted to taking into account not only local cortical microcircuits but also the global brain architecture, and efficient techniques are proposed and adopted to address the associated computational challenges in simulation of networks of such complexity. With the customized simulator developed, we are able to simulate the brain model to generate not only sleep spindle and delta waves but also the spike-and-wave pattern of absence seizures, and directly link those behaviors to underlying biophysical mechanism. Those initial results are interesting because they show the possibility to determine underlying causes of diseases by simulating the biologically realistic brain model. With further development, the work is geared to assisting the clinicians in selecting the optimal treatment on an individual basis in the future.