The Experts below are selected from a list of 5515392 Experts worldwide ranked by ideXlab platform

Maxim Bazhenov - One of the best experts on this subject based on the ideXlab platform.

  • thalamocortical and intracortical laminar connectivity determines sleep spindle properties
    PLOS Computational Biology, 2018
    Co-Authors: Giri P Krishnan, Burke Q Rosen, Lyle E Muller, Sydney S Cash, Jenyung Chen, Eric Halgren, Terrence J. Sejnowski, Maxim Bazhenov
    Abstract:

    Sleep spindles are brief oscillatory events during non-rapid eye movement (NREM) sleep. Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance, suggesting spindle involvement in memory consolidation. Here, using computational models, we identified network mechanisms that may explain differences in spindle properties across cortical structures. First, we report that differences in spindle occurrence between MEG and EEG data may arise from the contrasting properties of the core and matrix thalamocortical systems. The matrix system, projecting superficially, has wider thalamocortical fanout compared to the core system, which projects to middle layers, and requires the recruitment of a larger population of neurons to initiate a spindle. This property was sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers, as observed in the EEG signal. In contrast, spindles in the core system occurred more frequently but less synchronously, as observed in the MEG recordings. Furthermore, consistent with human recordings, in the model, spindles occurred independently in the core system but the matrix system spindles commonly co-occurred with core spindles. We also found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system, leading to widespread spindle activity. Our study predicts that plasticity of intra- and inter-cortical connectivity can potentially be a mechanism for increased spindle density as has been observed during learning.

  • thalamocortical and intracortical laminar connectivity determines sleep spindle properties
    bioRxiv, 2017
    Co-Authors: Giri P Krishnan, Burke Q Rosen, Lyle E Muller, Sydney S Cash, Jenyung Chen, Eric Halgren, Terrence J. Sejnowski, Maxim Bazhenov
    Abstract:

    Spindle oscillations are brief oscillatory activity during non-rapid eye movement (NREM) sleep. Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance, suggesting spindle involvement in memory consolidation. Using computational models, we identified network mechanisms that may explain differences in spindle properties across cortical structures. First, we report that differences in spindle occurrence between MEG and EEG data may arise from the properties of the core vs. matrix thalamocortical systems. The matrix system, projecting superficially, has wider thalamocortical fanout compared to the core system, projecting to the middle layers, and requires the recruitment of a larger population of neurons to initiate a spindle. Our model demonstrates that this property is sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers, as observed in the EEG signal. In contrast, spindles in the core system occurred more frequently but less synchronously, as observed in the MEG recordings. Futhermore, consistent with human recordings, in the model, spindles occurred independently in the core system but matrix system spindles commonly co-occurred with core one. We found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system, leading to widespread spindle activity. Our study predicts that plasticity of the intra and inter cortical connectivity can potentially be a mechanism for increasing in spindle density as observed during learning.

Alexander Düster - One of the best experts on this subject based on the ideXlab platform.

  • The spectral cell method for wave propagation in heterogeneous materials simulated on multiple GPUs and CPUs
    Computational Mechanics, 2019
    Co-Authors: Farshid Mossaiby, Meysam Joulaian, Alexander Düster
    Abstract:

    Efficient simulation of wave propagation in heterogeneous materials is still a challenging task. The spectral cell method, representing a combination of spectral elements with the fictitious domain concept, has proven to be an efficient approach for wave propagation analysis in materials with complicated microstructure. In this paper, we report details of parallel implementation of the spectral cell method using multi-core CPUs as well as GPUs. In our CPU implementation, we employ the OpenMP directives to parallelize the loops. On GPUs, however, we use the OpenCL framework to develop single- and multi-GPU versions of the code. In all of our implementations, the core operation is a sparse matrix-vector multiplication (SpMV) kernel. We analyze each implementation to determine its features and bottlenecks. The results show that speedups of up to 128 relative to serial CPU code can be achieved using multi-GPU code.

  • The spectral cell method for wave propagation in heterogeneous materials simulated on multiple GPUs and CPUs
    Computational Mechanics, 2019
    Co-Authors: Farshid Mossaiby, Meysam Joulaian, Alexander Düster
    Abstract:

    Efficient simulation of wave propagation in heterogeneous materials is still a challenging task. The spectral cell method, representing a combination of spectral elements with the fictitious domain concept, has proven to be an efficient approach for wave propagation analysis in materials with complicated microstructure. In this paper, we report details of parallel implementation of the spectral cell method using multi-core CPUs as well as GPUs. In our CPU implementation, we employ the OpenMP directives to parallelize the loops. On GPUs, however, we use the OpenCL framework to develop single- and multi-GPU versions of the code. In all of our implementations, the core operation is a sparse matrix-vector multiplication (SpMV) kernel. We analyze each implementation to determine its features and bottlenecks. The results show that speedups of up to 128 relative to serial CPU code can be achieved using multi-GPU code.

Giri P Krishnan - One of the best experts on this subject based on the ideXlab platform.

  • thalamocortical and intracortical laminar connectivity determines sleep spindle properties
    PLOS Computational Biology, 2018
    Co-Authors: Giri P Krishnan, Burke Q Rosen, Lyle E Muller, Sydney S Cash, Jenyung Chen, Eric Halgren, Terrence J. Sejnowski, Maxim Bazhenov
    Abstract:

    Sleep spindles are brief oscillatory events during non-rapid eye movement (NREM) sleep. Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance, suggesting spindle involvement in memory consolidation. Here, using computational models, we identified network mechanisms that may explain differences in spindle properties across cortical structures. First, we report that differences in spindle occurrence between MEG and EEG data may arise from the contrasting properties of the core and matrix thalamocortical systems. The matrix system, projecting superficially, has wider thalamocortical fanout compared to the core system, which projects to middle layers, and requires the recruitment of a larger population of neurons to initiate a spindle. This property was sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers, as observed in the EEG signal. In contrast, spindles in the core system occurred more frequently but less synchronously, as observed in the MEG recordings. Furthermore, consistent with human recordings, in the model, spindles occurred independently in the core system but the matrix system spindles commonly co-occurred with core spindles. We also found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system, leading to widespread spindle activity. Our study predicts that plasticity of intra- and inter-cortical connectivity can potentially be a mechanism for increased spindle density as has been observed during learning.

  • thalamocortical and intracortical laminar connectivity determines sleep spindle properties
    bioRxiv, 2017
    Co-Authors: Giri P Krishnan, Burke Q Rosen, Lyle E Muller, Sydney S Cash, Jenyung Chen, Eric Halgren, Terrence J. Sejnowski, Maxim Bazhenov
    Abstract:

    Spindle oscillations are brief oscillatory activity during non-rapid eye movement (NREM) sleep. Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance, suggesting spindle involvement in memory consolidation. Using computational models, we identified network mechanisms that may explain differences in spindle properties across cortical structures. First, we report that differences in spindle occurrence between MEG and EEG data may arise from the properties of the core vs. matrix thalamocortical systems. The matrix system, projecting superficially, has wider thalamocortical fanout compared to the core system, projecting to the middle layers, and requires the recruitment of a larger population of neurons to initiate a spindle. Our model demonstrates that this property is sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers, as observed in the EEG signal. In contrast, spindles in the core system occurred more frequently but less synchronously, as observed in the MEG recordings. Futhermore, consistent with human recordings, in the model, spindles occurred independently in the core system but matrix system spindles commonly co-occurred with core one. We found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system, leading to widespread spindle activity. Our study predicts that plasticity of the intra and inter cortical connectivity can potentially be a mechanism for increasing in spindle density as observed during learning.

Farshid Mossaiby - One of the best experts on this subject based on the ideXlab platform.

  • The spectral cell method for wave propagation in heterogeneous materials simulated on multiple GPUs and CPUs
    Computational Mechanics, 2019
    Co-Authors: Farshid Mossaiby, Meysam Joulaian, Alexander Düster
    Abstract:

    Efficient simulation of wave propagation in heterogeneous materials is still a challenging task. The spectral cell method, representing a combination of spectral elements with the fictitious domain concept, has proven to be an efficient approach for wave propagation analysis in materials with complicated microstructure. In this paper, we report details of parallel implementation of the spectral cell method using multi-core CPUs as well as GPUs. In our CPU implementation, we employ the OpenMP directives to parallelize the loops. On GPUs, however, we use the OpenCL framework to develop single- and multi-GPU versions of the code. In all of our implementations, the core operation is a sparse matrix-vector multiplication (SpMV) kernel. We analyze each implementation to determine its features and bottlenecks. The results show that speedups of up to 128 relative to serial CPU code can be achieved using multi-GPU code.

  • The spectral cell method for wave propagation in heterogeneous materials simulated on multiple GPUs and CPUs
    Computational Mechanics, 2019
    Co-Authors: Farshid Mossaiby, Meysam Joulaian, Alexander Düster
    Abstract:

    Efficient simulation of wave propagation in heterogeneous materials is still a challenging task. The spectral cell method, representing a combination of spectral elements with the fictitious domain concept, has proven to be an efficient approach for wave propagation analysis in materials with complicated microstructure. In this paper, we report details of parallel implementation of the spectral cell method using multi-core CPUs as well as GPUs. In our CPU implementation, we employ the OpenMP directives to parallelize the loops. On GPUs, however, we use the OpenCL framework to develop single- and multi-GPU versions of the code. In all of our implementations, the core operation is a sparse matrix-vector multiplication (SpMV) kernel. We analyze each implementation to determine its features and bottlenecks. The results show that speedups of up to 128 relative to serial CPU code can be achieved using multi-GPU code.

Lyle E Muller - One of the best experts on this subject based on the ideXlab platform.

  • thalamocortical and intracortical laminar connectivity determines sleep spindle properties
    PLOS Computational Biology, 2018
    Co-Authors: Giri P Krishnan, Burke Q Rosen, Lyle E Muller, Sydney S Cash, Jenyung Chen, Eric Halgren, Terrence J. Sejnowski, Maxim Bazhenov
    Abstract:

    Sleep spindles are brief oscillatory events during non-rapid eye movement (NREM) sleep. Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance, suggesting spindle involvement in memory consolidation. Here, using computational models, we identified network mechanisms that may explain differences in spindle properties across cortical structures. First, we report that differences in spindle occurrence between MEG and EEG data may arise from the contrasting properties of the core and matrix thalamocortical systems. The matrix system, projecting superficially, has wider thalamocortical fanout compared to the core system, which projects to middle layers, and requires the recruitment of a larger population of neurons to initiate a spindle. This property was sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers, as observed in the EEG signal. In contrast, spindles in the core system occurred more frequently but less synchronously, as observed in the MEG recordings. Furthermore, consistent with human recordings, in the model, spindles occurred independently in the core system but the matrix system spindles commonly co-occurred with core spindles. We also found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system, leading to widespread spindle activity. Our study predicts that plasticity of intra- and inter-cortical connectivity can potentially be a mechanism for increased spindle density as has been observed during learning.

  • thalamocortical and intracortical laminar connectivity determines sleep spindle properties
    bioRxiv, 2017
    Co-Authors: Giri P Krishnan, Burke Q Rosen, Lyle E Muller, Sydney S Cash, Jenyung Chen, Eric Halgren, Terrence J. Sejnowski, Maxim Bazhenov
    Abstract:

    Spindle oscillations are brief oscillatory activity during non-rapid eye movement (NREM) sleep. Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance, suggesting spindle involvement in memory consolidation. Using computational models, we identified network mechanisms that may explain differences in spindle properties across cortical structures. First, we report that differences in spindle occurrence between MEG and EEG data may arise from the properties of the core vs. matrix thalamocortical systems. The matrix system, projecting superficially, has wider thalamocortical fanout compared to the core system, projecting to the middle layers, and requires the recruitment of a larger population of neurons to initiate a spindle. Our model demonstrates that this property is sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers, as observed in the EEG signal. In contrast, spindles in the core system occurred more frequently but less synchronously, as observed in the MEG recordings. Futhermore, consistent with human recordings, in the model, spindles occurred independently in the core system but matrix system spindles commonly co-occurred with core one. We found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system, leading to widespread spindle activity. Our study predicts that plasticity of the intra and inter cortical connectivity can potentially be a mechanism for increasing in spindle density as observed during learning.