Applying Super-Resolution and Tomography Concepts to Identify Receptive Field Subunits in the Retina
Fig 3
Optimal stimulus and analysis parameters.
(A) Sample model layout (left) and receptive field (right) used throughout this figure (layout outlines 1.5 σ ellipses of subunits and receptive field). (B) Illustration of the detection of hotspots in a reconstruction. Background image is FBP reconstruction with red and blue colors representing positive and negative values, respectively. Large dark-blue circle depicts area in which local maxima (yellow crosses) are identified. Local maxima are compared with 0.75 σ ellipses of the underlying subunits (black) to compute an F-score. (C) Sample sinograms (top row) and corresponding reconstructions (bottom row) of measurements with varying stimulus parameters. Surround factors s are 1, 2, and 5 from left to right, stripe width values w are 10, 5, 4. (D) Search for the optimal parameters in the parameter space of surround factor s and stripe width w. Brighter colors denote better average F-score for 1000 model instantiations with ten subunits each. Optimal parameters (s = 2.5, w = 5 pixels) are marked by a black dot. (E) Influence of smoothing the sinogram in position direction on a sample sinogram (top row) and the corresponding reconstructions (bottom row). Standard deviations σpos of the Gaussian filters are (from left to right) 0%, 1.5%, 3%, 4.5%, and 6% of the simulation area size. Smoothing in angle-direction is omitted for these plots (σang = 0°). (F) Like (D), but for search for optimal smoothing of the sinogram in the parameter space of standard deviations for stripe position smoothing σpos (optimum is 2.5%) and stripe angle smoothing σang (optimum is 5°).