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Another rich set of recent results concerns scattering by "thin" barriers modeled by delta function potentials (possibly energy dependent) supported on hypersurfaces in ( {mathbb {R}}^n ).
In the first stage, blood-oxygen level-dependent (BOLD) responses were modeled by delta functions at the stimulus onsets for the five event types of interest (i.e., centrifugal left, centrifugal right, centripetal left, centripetal right, maintain fixation), which were then convolved with a standard hemodynamic response function (HRF) to form covariates of a general linear model [GLM, 48].
Transient activation associated with switch and stay events was modeled by delta functions (coding for stimulus onsets).
Correct and incorrect responses to all experimental trials were modeled by delta functions convolved with a canonical hemodynamic response function (HRF).
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In the first stage, neural activity was modeled by a delta function at stimulus onset.
In the first stage, neural activity was modeled by a delta function at stimulus onset and the ensuing BOLD response was modeled by convolving these with a canonical hemodynamic response function (HRF) and its temporal and dispersion partial derivatives.
Each trial was modeled by a delta function defined using the event onset, and this was convolved with the canonical hemodynamic response function to create a regressor for each stimulus type.
The AFM tip mass is modeled by the Dirac delta function and the coupling effects are analyzed via the Galerkin method.
The blood oxygen level-dependent (BOLD) event-related responses were modeled by convolving these delta functions with a canonical hemodynamic response function (HRF).
Trial-specific effects were modeled by trains of delta functions convolved with 3 hemodynamic basis functions (a canonical hemodynamic response function, and its temporal and dispersion derivatives).
At the first level, for each subject, trial-specific responses were modeled by convolving a delta function that indicated each event onset with the canonical hemodynamic response function (HRF) to create regressors of interest, one regressor for each of the three event types (neutral, fearful, and happy faces).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com