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Full-Text Articles in Neuroscience and Neurobiology
Local Geometry Of Elementary Visual Computations, Peter Neri
Local Geometry Of Elementary Visual Computations, Peter Neri
MODVIS Workshop
Visual operators (e.g. edge detectors) are classically modelled using small circuits involving canonical computations, such as template-matching and gain control. Circuit models explain many aspects of the empirical descriptors that are used to characterize local visual operators, from sensitivity to classification images. Notwithstanding their utility, these models fail to provide a unified framework encompassing the variety of effects observed experimentally, such as the impact of contrast, SNR, and attention on the above descriptors. My goal is to start with a simple, plausible geometrical representation of the perceptual operation carried out by the observer, and to show that this representation is …
Evaluating And Interpreting A Convolutional Neural Net As A Model Of V4, Dean A. Pospisil, Anitha Pasupathy, Wyeth Bair
Evaluating And Interpreting A Convolutional Neural Net As A Model Of V4, Dean A. Pospisil, Anitha Pasupathy, Wyeth Bair
MODVIS Workshop
Convolutional neural nets (CNNs) are currently the highest performing image recognition computer algorithms. Of interest is whether these CNNs, following extensive supervised training, perform computations similar to those in the ventral visual stream. We investigated whether CNN units’ tuning for shape boundaries was similar to V4’s as described in the angular position and curvature (APC) model of Pasupathy and Connor 2001. From units in all layers of AlexNet (see Figure A), an object recognition CNN, we recorded responses to the original study’s set of shape stimuli (51 simple closed shapes at up to 8 rotations) presented at 51 spatial translations …