Journal: PLoS Computational Biology

Article Title: Asymmetries around the visual field: From retina to cortex to behavior

doi: 10.1371/journal.pcbi.1009771

Figure Lengend Snippet: A 1-ms frame of a 100% contrast Gabor stimulus is used at each computational step for illustration purposes. (1) Scene radiance. Photons emitted by the visual display, resulting in a time-varying scene spectral radiance. Gabor stimulus shows radiance summed across 400–700 nm wavelengths. (2) Retinal irradiance. Emitted photons pass through simulated human cornea, pupil, and optics, indicated by the schematic point spread function (PSF) in the top right-side box, resulting in time-varying retinal irradiance. Gabor stimulus shows irradiance with wavelengths converted to RGB values for illustration purposes. (3) Cone absorptions. Retinal irradiance is isomerized by a rectangular cone mosaic, resulting in time-varying photon absorption rates for each L-cone with Poisson noise. (4) Cone photocurrent. Absorptions are converted to photocurrent via temporal integration, gain control, followed by adding Gaussian white noise. This results in time-varying photocurrent for each cone. (5) Midget RGC responses. Time-varying cone photocurrents are convolved with a 2D Difference of Gaussians spatial filter (DoG), followed by additive Gaussian white noise and subsampling. (6) Behavioral inference. A linear support vector machine (SVM) classifier is trained on the RGC outputs to classify stimulus orientation per contrast level. With 10-fold cross-validation, left-out data are tested, and accuracy is fitted with a Weibull function to extract the contrast threshold at ~80%.

Article Snippet: We used a linear SVM classifier as implemented in MATLAB’s fitcsvm with 10-fold cross-validation and built-in z-scoring.

Techniques: Control, Plasmid Preparation, Biomarker Discovery

Journal: PLoS Computational Biology

Article Title: Asymmetries around the visual field: From retina to cortex to behavior

doi: 10.1371/journal.pcbi.1009771

Figure Lengend Snippet: Left column shows classifier accuracy as function of stimulus contrast. Data are from simulated experiments with 1,000 trials per stimulus class, using a model with a L-cone only mosaic varying in cone density. Data are fitted with a Weibull function. Contrast thresholds are plotted separately as a function of cone density in the right column. (A) Cone absorptions. Applying a linear SVM classifier to cone absorptions averaged across stimulus time points. (B) Cone photocurrent. Applying a linear SVM classifier to cone outer segment photocurrent responses, averaged across time weighted by a temporally delayed stimulus time course. This transformation of cone absorptions into photocurrent causes a ~10x increase in contrast thresholds, interacting with cone density ( i . e ., Weibull functions are spaced out compared to cone absorptions). (C) RGC responses. Applying a linear SVM classifier to spatially filtered photocurrent with added white noise. This transformation causes an additional increase in contrast thresholds for all cone densities. Data show results for a fixed subsampling ratio of 2 mRGCs per cone.

Article Snippet: We used a linear SVM classifier as implemented in MATLAB’s fitcsvm with 10-fold cross-validation and built-in z-scoring.

Techniques: Transformation Assay