The neurophysiological signals that are recorded in EEG (electroencephalography) and MEG (magnetoencephalography) originate from current flow perpendicular to the cortical surface due to the columnar organization of pyramidal cells in the cortical gray matter. These locations and directions have been used as anatomical constraints for dipolar sources in estimations of neural activity from MEG recordings. Here we extend anatomically constrained beamforming to EEG, which requires a more sophisticated forward model than MEG due to the blurring of the electric potential at tissue boundaries, but in contrast to MEG, EEG can account for both tangential and radial sources. Using computed tomography (CT) scans we create a realistic three-layer head model consisting of tessellated surfaces representing the tissue boundaries cerebrospinal fluid-skull, skull-scalp and scalp-air. The cortical gray matter surface, the anatomical constraint for the source dipoles, is extracted from magnetic resonance imaging (MRI) scans. EEG beamforming is implemented in a set of simulated data and compared for three different head models: single sphere, multi-shell sphere and realistic geometry multi-shell model that employs a boundary element method. Beamformer performance is also analyzed and evaluated for multiple dipoles and extended sources (patches). We show that using anatomical constraints with the beamforming algorithm greatly reduces computation time while increasing the spatial accuracy of the reconstructed sources of neural activity. Using the spatial Laplacian instead of the electric potential in combination with beamforming further improves the spatial resolution and allows for the detection of highly correlated sources. / by Vyacheslav Murzin. / Thesis (Ph.D.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

Thesis (M.A.)--York University, 2004. Graduate Programme in Psychology. / Typescript. Includes bibliographical references (leaves 48-55). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss&rft%5Fval%5Ffmt=info:ofi/fmt:kev:mtx:dissertation&rft%5Fdat=xri:pqdiss:MQ99389

What does the spike of a sensory neuron mean? This is a fundamental question in computational neuroscience. Conventional approaches provide an answer based on correlation between spike pattern and the stimulus that caused it. However, these approaches do not satisfactorily explain how the brain, which does not have direct knowledge of the world or the stimuli, can achieve this task. This thesis frames the problem in terms of a task for a simulated agent and provides a solution based on an approach which regards action as necessary for acquiring the meaning of neural spikes. This approach differs from some others in that it proposes a new criterion called the sensory invariance criterion, which can be used to associate meaning to spike patterns in terms of action sequences the agent generates. This criterion forms the basis of the Sensory Invariance Driven Action (SIDA) framework presented in this thesis. This framework is implemented in a reinforcement learning agent and the results indicate that the agent can successfully learn to associate meaning to the sensor activity in terms of specific actions which reflect the properties of the stimulus. Further behavioral experiments on the agent show that this framework allows the agent to learn the meaning of complex (spatiotemporal) spike patterns. The successful learning exhibited by the agent raises hopes that SIDA can be used to build agents with natural semantics.

To learn effectively, an adaptive controller needs to know its sensitivity derivatives — the variables that quantify how system performance depends on the commands from the controller. In the case of biological sensorimotor control, no one has explained how those derivatives themselves might be learned, and some authors suggest they aren’t learned at all but are known innately. Here I show that this knowledge can’t be solely innate, given the adaptive flexibility of neural systems. And I show how it could be learned, using forms of information transport that are available in the brain, by a mechanism I call implicit supervision. I show that implicit supervision explains a wide range of otherwise-puzzling facts about motor learning. It explains how we can cope with conditions that reverse the signs of sensitivity derivatives, e.g. nerve or muscle transpositions, reversing goggles, or tasks like drilling teeth seen in a mirror. It also explains why it is harder to recover from reversals than from other alterations such as magnifying, minifying or displacing goggles. A further prediction of the theory of implicit supervision, in its simplest form, is that each control system — say for gaze stabilization, or saccades, or reaching — has one single, all-purpose estimate of its sensitivity derivatives for all parts of the motion. When that estimate is revised, it should affect all stages of the task. For instance, when you learn to move to mirror-reversed targets then your adapted estimate of e/u should reverse not only your initial aiming but also your online course adjustments: when the target jumps in mid-movement, your path adjustment should be appropriately reversed. Here I put subjects through many trials with jumping targets, and show that, given enough practice, they do learn to reverse their course adjustments, and therefore both initial aiming and later adjustments are governed by revisable estimates of sensitivity derivatives. And I argue that all the available data, from my own experiments and earlier ones, are compatible with a single, adaptable, all-purpose estimate of these derivatives, as in the simplest form of implicit supervision.

To learn effectively, an adaptive controller needs to know its sensitivity derivatives — the variables that quantify how system performance depends on the commands from the controller. In the case of biological sensorimotor control, no one has explained how those derivatives themselves might be learned, and some authors suggest they aren’t learned at all but are known innately. Here I show that this knowledge can’t be solely innate, given the adaptive flexibility of neural systems. And I show how it could be learned, using forms of information transport that are available in the brain, by a mechanism I call implicit supervision. I show that implicit supervision explains a wide range of otherwise-puzzling facts about motor learning. It explains how we can cope with conditions that reverse the signs of sensitivity derivatives, e.g. nerve or muscle transpositions, reversing goggles, or tasks like drilling teeth seen in a mirror. It also explains why it is harder to recover from reversals than from other alterations such as magnifying, minifying or displacing goggles. A further prediction of the theory of implicit supervision, in its simplest form, is that each control system — say for gaze stabilization, or saccades, or reaching — has one single, all-purpose estimate of its sensitivity derivatives for all parts of the motion. When that estimate is revised, it should affect all stages of the task. For instance, when you learn to move to mirror-reversed targets then your adapted estimate of e/u should reverse not only your initial aiming but also your online course adjustments: when the target jumps in mid-movement, your path adjustment should be appropriately reversed. Here I put subjects through many trials with jumping targets, and show that, given enough practice, they do learn to reverse their course adjustments, and therefore both initial aiming and later adjustments are governed by revisable estimates of sensitivity derivatives. And I argue that all the available data, from my own experiments and earlier ones, are compatible with a single, adaptable, all-purpose estimate of these derivatives, as in the simplest form of implicit supervision.

Afferent somatosensory inputs ascend from the periphery to the cortex carrying information about touch that is critical for planning motor responses. At the cortical level, this information is subject to modulation from its earliest arrival in somatosensory cortex where factors such as task-relevance begin to shape how the sensory signals are processed. The goal of such modulation is largely to facilitate the extraction of relevant sensory information (and suppression of irrelevant signals) early in the processing stream, and these functions are in part carried out by top-down influences from cortical and sub-cortical structures. Efforts to understand the mechanisms contributing to modulation of sensory-specific cortex have revealed that crossmodal signals (i.e. simultaneously presented stimuli from a different modality) can also influence early sensory processing, but the precise nature of this modulation and what may drive it is largely unknown. It is the purpose of this thesis to investigate the modulation of somatosensory cortex, specifically how task-relevant modulation of somatosensory cortex might be influenced by crossmodal (visual) stimuli, and whether specific task requirements have any bearing on SI excitability. The studies comprising this thesis aim to address these gaps in our mechanistic understanding of the networks involved in modulating somatosensory cortex. Studies 1 and 2 employed functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to investigate how task-relevant visual and vibrotactile stimuli modulate somatosensory cortex and to probe the role of a frontoparietal network in mediating this modulation. Studies 3 and 4 also used EEG to determine how manipulating the relevance of the stimuli affects the modulation of somatosensory event-related potentials (ERPs), and to probe how task-specific sensory-motor requirements mediate excitability in somatosensory cortex as well as frontal and parietal regions. The results of this thesis provide insight into the factors that modulate somatosensory cortex and the role of a fronto-parietal network in subserving these modulations.

What does the spike of a sensory neuron mean? This is a fundamental question in computational neuroscience. Conventional approaches provide an answer based on correlation between spike pattern and the stimulus that caused it. However, these approaches do not satisfactorily explain how the brain, which does not have direct knowledge of the world or the stimuli, can achieve this task. This thesis frames the problem in terms of a task for a simulated agent and provides a solution based on an approach which regards action as necessary for acquiring the meaning of neural spikes. This approach differs from some others in that it proposes a new criterion called the sensory invariance criterion, which can be used to associate meaning to spike patterns in terms of action sequences the agent generates. This criterion forms the basis of the Sensory Invariance Driven Action (SIDA) framework presented in this thesis. This framework is implemented in a reinforcement learning agent and the results indicate that the agent can successfully learn to associate meaning to the sensor activity in terms of specific actions which reflect the properties of the stimulus. Further behavioral experiments on the agent show that this framework allows the agent to learn the meaning of complex (spatiotemporal) spike patterns. The successful learning exhibited by the agent raises hopes that SIDA can be used to build agents with natural semantics.

Thesis (Ph. D.)--University of California, San Diego, 2001. / Vita. Includes bibliographical references (leaves 115-120).

A large body of human brain-behavior research has focused upon sensorimotor processes and their relation to higher mental functioning. Semmes et al. (1960) have presented evidence to suggest that sensorimotor functions of the two cerebral hemispheres are not mirror images of each other. These

No description available.