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Self-organizing feature maps (SOFMs) represent a dimensionality-reduction algorithm that has been used to replicate feature topographies observed experimentally in primary visual cortex (V1). We used the SOFM algorithm to model possible topographies of generic sensory cortical areas containing up to five arbitrary physiological features. This study explored(More)
Functional properties of neurons are often distributed nonrandomly within a cortical area and form topographic maps that reveal insights into neuronal organization and interconnection. Some functional maps, such as in visual cortex, are fairly straightforward to discern with a variety of techniques, while other maps, such as in auditory cortex, have(More)
Functional imaging can reveal detailed organizational structure in cerebral cortical areas, but neuronal response features and local neural interconnectivity can influence the resulting images, possibly limiting the inferences that can be drawn about neural function. Discerning the fundamental principles of organizational structure in the auditory cortex of(More)
A comparison of the performance of the tripolar and bipolar concentric as well as spline Laplacian electrocardiograms (LECGs) and body surface Laplacian mappings (BSLMs) for localizing and imaging the cardiac electrical activation has been investigated based on computer simulation. In the simulation a simplified eccentric heart-torso sphere-cylinder(More)
Laplacian Electrocardiogram (LECG) is a non-invasive approach providing high spatiotemporal distributed information of cardiac electrical activity. Recently researchers have recorded surface potentials from monopolar disc electrodes to estimate the Laplacian using finite difference algorithms or spline surface Laplacian estimators. Bipolar and quasi-bipolar(More)
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