The Experts below are selected from a list of 474306 Experts worldwide ranked by ideXlab platform

Michael J. Kahana - One of the best experts on this subject based on the ideXlab platform.

  • Multivariate stochastic volatility modeling of Neural Data
    eLife, 2019
    Co-Authors: Tung D. Phan, Jessica A. Wachter, Ethan A. Solomon, Michael J. Kahana
    Abstract:

    Because multivariate autoregressive models have failed to adequately account for the complexity of Neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent-variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity. We show that volatility features derived from our model can reliably decode memory states, and that this classifier performs as well as those using spectral features. Using the directional connections between brain regions during complex cognitive process provided by the model, we uncovered perirhinal-hippocampal desynchronization in the MTL regions that is associated with successful memory encoding.

  • Multivariate Stochastic Volatility Modeling of Neural Data
    2018
    Co-Authors: Tung D. Phan, Jessica A. Wachter, Michael J. Kahana
    Abstract:

    Abstract Because multivariate autoregressive models have failed to adequately account for the complexity of Neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using a Database of medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. We estimate both the parameters describing the latent variable processes and the directional correlations in volatility between brain regions using Bayesian sampling techniques. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity and is widely used to study brain function. We show that volatility features derived from our model can reliably decode good vs. poor memory states, and that this classifier performs as well as those using spectral features. Using the multivariate stochastic volatility model, we uncovered hippocampal-perirhinal bidirectional connections in the MTL regions that are associated with successful memory encoding.

Tung D. Phan - One of the best experts on this subject based on the ideXlab platform.

  • Multivariate stochastic volatility modeling of Neural Data
    eLife, 2019
    Co-Authors: Tung D. Phan, Jessica A. Wachter, Ethan A. Solomon, Michael J. Kahana
    Abstract:

    Because multivariate autoregressive models have failed to adequately account for the complexity of Neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent-variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity. We show that volatility features derived from our model can reliably decode memory states, and that this classifier performs as well as those using spectral features. Using the directional connections between brain regions during complex cognitive process provided by the model, we uncovered perirhinal-hippocampal desynchronization in the MTL regions that is associated with successful memory encoding.

  • Multivariate Stochastic Volatility Modeling of Neural Data
    2018
    Co-Authors: Tung D. Phan, Jessica A. Wachter, Michael J. Kahana
    Abstract:

    Abstract Because multivariate autoregressive models have failed to adequately account for the complexity of Neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using a Database of medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. We estimate both the parameters describing the latent variable processes and the directional correlations in volatility between brain regions using Bayesian sampling techniques. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity and is widely used to study brain function. We show that volatility features derived from our model can reliably decode good vs. poor memory states, and that this classifier performs as well as those using spectral features. Using the multivariate stochastic volatility model, we uncovered hippocampal-perirhinal bidirectional connections in the MTL regions that are associated with successful memory encoding.

Kristofer E Bouchard - One of the best experts on this subject based on the ideXlab platform.

  • deep learning as a tool for Neural Data analysis speech classification and cross frequency coupling in human sensorimotor cortex
    PLOS Computational Biology, 2019
    Co-Authors: Jesse A Livezey, Kristofer E Bouchard, Edward F Chang
    Abstract:

    A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of Neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear transformations between Neural features and features of the sensory stimuli or motor task. While successful in some early sensory processing areas, linear mappings are unlikely to be ideal tools for elucidating nonlinear, hierarchical representations of higher-order brain areas during complex tasks, such as the production of speech by humans. Here, we apply deep networks to predict produced speech syllables from a Dataset of high gamma cortical surface electric potentials recorded from human sensorimotor cortex. We find that deep networks had higher decoding prediction accuracy compared to baseline models. Having established that deep networks extract more task relevant information from Neural Data sets relative to linear models (i.e., higher predictive accuracy), we next sought to demonstrate their utility as a Data analysis tool for neuroscience. We first show that deep network’s confusions revealed hierarchical latent structure in the Neural Data, which recapitulated the underlying articulatory nature of speech motor control. We next broadened the frequency features beyond high-gamma and identified a novel high-gamma-to-beta coupling during speech production. Finally, we used deep networks to compare task-relevant information in different Neural frequency bands, and found that the high-gamma band contains the vast majority of information relevant for the speech prediction task, with little-to-no additional contribution from lower-frequency amplitudes. Together, these results demonstrate the utility of deep networks as a Data analysis tool for basic and applied neuroscience.

  • deep learning as a tool for Neural Data analysis speech classification and cross frequency coupling in human sensorimotor cortex
    arXiv: Neural and Evolutionary Computing, 2018
    Co-Authors: Jesse A Livezey, Kristofer E Bouchard, Edward F Chang
    Abstract:

    A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of Neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear transformations between Neural features and features of the sensory stimuli or motor task. While successful in some early sensory processing areas, linear mappings are unlikely to be ideal tools for elucidating nonlinear, hierarchical representations of higher-order brain areas during complex tasks, such as the production of speech by humans. Here, we apply deep networks to predict produced speech syllables from cortical surface electric potentials recorded from human sensorimotor cortex. We found that deep networks had higher decoding prediction accuracy compared to baseline models, and also exhibited greater improvements in accuracy with increasing Dataset size. We further demonstrate that deep network's confusions revealed hierarchical latent structure in the Neural Data, which recapitulated the underlying articulatory nature of speech motor control. Finally, we used deep networks to compare task-relevant information in different Neural frequency bands, and found that the high-gamma band contains the vast majority of information relevant for the speech prediction task, with little-to-no additional contribution from lower-frequencies. Together, these results demonstrate the utility of deep networks as a Data analysis tool for neuroscience.

Jesse A Livezey - One of the best experts on this subject based on the ideXlab platform.

  • deep learning as a tool for Neural Data analysis speech classification and cross frequency coupling in human sensorimotor cortex
    PLOS Computational Biology, 2019
    Co-Authors: Jesse A Livezey, Kristofer E Bouchard, Edward F Chang
    Abstract:

    A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of Neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear transformations between Neural features and features of the sensory stimuli or motor task. While successful in some early sensory processing areas, linear mappings are unlikely to be ideal tools for elucidating nonlinear, hierarchical representations of higher-order brain areas during complex tasks, such as the production of speech by humans. Here, we apply deep networks to predict produced speech syllables from a Dataset of high gamma cortical surface electric potentials recorded from human sensorimotor cortex. We find that deep networks had higher decoding prediction accuracy compared to baseline models. Having established that deep networks extract more task relevant information from Neural Data sets relative to linear models (i.e., higher predictive accuracy), we next sought to demonstrate their utility as a Data analysis tool for neuroscience. We first show that deep network’s confusions revealed hierarchical latent structure in the Neural Data, which recapitulated the underlying articulatory nature of speech motor control. We next broadened the frequency features beyond high-gamma and identified a novel high-gamma-to-beta coupling during speech production. Finally, we used deep networks to compare task-relevant information in different Neural frequency bands, and found that the high-gamma band contains the vast majority of information relevant for the speech prediction task, with little-to-no additional contribution from lower-frequency amplitudes. Together, these results demonstrate the utility of deep networks as a Data analysis tool for basic and applied neuroscience.

  • deep learning as a tool for Neural Data analysis speech classification and cross frequency coupling in human sensorimotor cortex
    arXiv: Neural and Evolutionary Computing, 2018
    Co-Authors: Jesse A Livezey, Kristofer E Bouchard, Edward F Chang
    Abstract:

    A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of Neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear transformations between Neural features and features of the sensory stimuli or motor task. While successful in some early sensory processing areas, linear mappings are unlikely to be ideal tools for elucidating nonlinear, hierarchical representations of higher-order brain areas during complex tasks, such as the production of speech by humans. Here, we apply deep networks to predict produced speech syllables from cortical surface electric potentials recorded from human sensorimotor cortex. We found that deep networks had higher decoding prediction accuracy compared to baseline models, and also exhibited greater improvements in accuracy with increasing Dataset size. We further demonstrate that deep network's confusions revealed hierarchical latent structure in the Neural Data, which recapitulated the underlying articulatory nature of speech motor control. Finally, we used deep networks to compare task-relevant information in different Neural frequency bands, and found that the high-gamma band contains the vast majority of information relevant for the speech prediction task, with little-to-no additional contribution from lower-frequencies. Together, these results demonstrate the utility of deep networks as a Data analysis tool for neuroscience.

Jessica A. Wachter - One of the best experts on this subject based on the ideXlab platform.

  • Multivariate stochastic volatility modeling of Neural Data
    eLife, 2019
    Co-Authors: Tung D. Phan, Jessica A. Wachter, Ethan A. Solomon, Michael J. Kahana
    Abstract:

    Because multivariate autoregressive models have failed to adequately account for the complexity of Neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent-variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity. We show that volatility features derived from our model can reliably decode memory states, and that this classifier performs as well as those using spectral features. Using the directional connections between brain regions during complex cognitive process provided by the model, we uncovered perirhinal-hippocampal desynchronization in the MTL regions that is associated with successful memory encoding.

  • Multivariate Stochastic Volatility Modeling of Neural Data
    2018
    Co-Authors: Tung D. Phan, Jessica A. Wachter, Michael J. Kahana
    Abstract:

    Abstract Because multivariate autoregressive models have failed to adequately account for the complexity of Neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using a Database of medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. We estimate both the parameters describing the latent variable processes and the directional correlations in volatility between brain regions using Bayesian sampling techniques. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity and is widely used to study brain function. We show that volatility features derived from our model can reliably decode good vs. poor memory states, and that this classifier performs as well as those using spectral features. Using the multivariate stochastic volatility model, we uncovered hippocampal-perirhinal bidirectional connections in the MTL regions that are associated with successful memory encoding.