The Hermann Grid Illusion Revisited
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Visual Physiology
Visual Physiology Perception and Attention Graham Hole Problems confronting the visual system: Retinal image contains a huge amount of information - which must be processed quickly. Retinal image is dim, blurry and distorted. Light levels vary enormously. Solutions: Enhance the important information (edges and contours). Process relative intensities, not absolute light levels. Use two systems, one for bright light and another for twilight (humans). The primary visual pathways: The right visual field is represented in the left hemisphere. The left visual field is represented in the right hemisphere The eye: 50% of light entering the eye is lost by absorption & scattering (diffraction). The rest strikes retinal photoreceptors. Photoreceptors: Rod Outer Segment Cone The retina contains two kinds of photoreceptors, rods and cones. Inner Segment Pedicle Why are two kinds of photoreceptor needed? 2 S Surface Luminance (cd/m ) c o t o White paper in starlight 0.05 p i c White paper in moonlight 0.5 White paper in artificial light 250 P ho t Computer monitor, TV 100 o p i White paper in sunlight 30,000 c Ambient light levels can vary hugely, by a factor of 10,000,000. Pupil diameter can vary retinal illumination by only a factor of 16. Rods respond at low (scotopic) light levels. Cones respond at high (photopic) light levels. Differences between rods and cones: Sensitivity Number Retinal distribution Visual Pigment Number and distribution of receptors: There are about 120 million rods, and 8 million cones. Cones are concentrated in central vision, around the fovea. Rods cover the entire retina, with the exception of the fovea. -
Visual Properties of Human Retinal Ganglion Cells
bioRxiv preprint doi: https://doi.org/10.1101/766170; this version posted August 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Visual properties of human retinal ganglion cells Katja Reinhard1,2,3* & Thomas A. Münch1,4* 1 Retinal Circuits and Optogenetics, Centre for Integrative Neuroscience and Bernstein Center for Computational Neuroscience, University of Tübingen, Otfried-Müller-Str. 25, 72076 Tübingen, Germany; 2 Neuroscience Graduate School, University of Tübingen, Österbergstraße 3, 72074 Tübingen Germany; 3 Current address: Neuro- electronics Research Flanders, Kapeldreef 75, 3001 Leuven, Belgium; 4 Institute for Ophthalmic Research, University of Tübingen, Elfriede-Aulhorn-Straße 7, 72076 Tübingen, Germany * Corresponding authors Katja Reinhard: [email protected] and Thomas A. Münch: [email protected] The retinal output is the sole source of visual information for the brain. Studies in non-primate mammals estimate that this information is carried by several dozens of retinal ganglion cell types, each informing the brain about different aspects of a visual scene. Even though morphological studies of primate retina suggest a similar diversity of ganglion cell types, research has focused on the function of only a few cell types. In human retina, recordings from individual cells are anecdotal or focus on a small subset of identified types. Here, we present the first systematic ex-vivo recording of light responses from 342 ganglion cells in human retinas obtained from donors. -
A Linear Systems View to the Concept of Strfs Mounya Elhilali(1)
A linear systems view to the concept of STRFs Mounya Elhilali (1) , Shihab Shamma (2,3) , Jonathan Z Simon (2,3,4) , Jonathan B. Fritz (3) (1) Department of Electrical & Computer Engineering, Johns Hopkins University (2) Department of Electrical & Computer Engineering, University of Maryland, College Park (3)Institute for Systems Research, University of Maryland, College Park (4)Department of Biology, University of Maryland, College Park 1. Introduction A key requirement in the study of sensory nervous systems is the ability to characterize effectively neuronal response selectivity. In the visual system, striving for this objective yielded remarkable progress in understanding the functional organization of the visual cortex and its implications to perception. For instance, the discovery of ordered feature representations of retinal space, color, ocular dominance, orientation, and motion direction in various cortical fields has catalyzed extensive studies into the anatomical bases of this organization, its developmental course, and the changes it undergoes during learning or following injury. In the primary auditory cortex (A1), response selectivity of the neurons (also called their receptive fields ) is ordered topographically according to the frequency they are most tuned to, an organization inherited from the cochlea. Beyond their tuning, however, A1 responses and receptive fields exhibit a bewildering variety of dynamics, frequency bandwidths, response thresholds, and patterns of excitatory and inhibitory regions. All this has been learned over decades of testing with a large variety of acoustic stimuli (tones, clicks, noise, and natural sounds) and response measures (tuning curves, rate-level functions, and binaural maps). A response measure that has proven particularly useful beyond the feature- specific measures enumerated earlier is the notion of a generalized spatiotemporal , or equivalently for the auditory system, the spectro-temporal receptive field (STRF). -
The Retina the Retina the Pigment Layer Contains Melanin That Prevents Light Reflection Throughout the Globe of the Eye
The Retina The retina The pigment layer contains melanin that prevents light reflection throughout the globe of the eye. It is also a store of Vitamin A. Rods and Cones Photoreceptors of the nervous system responsible for transforming light energy into the electrical energy of the nervous system. Bipolar cells Link between photoreceptors and ganglion cells. Horizontal cells Provide inhibition between bipolar cells. This is the mechanism of lateral inhibition which is important to edge detection and contrast enhancement. Amacrine cells Transmit excitatory signals from bipolar to ganglion cells. Thought to be important in signalling changes in light intensity. Neural circuitry About 125 rods and 5 cones converge on each optic nerve fibre. In the central portion of the fovea there are no rods. The ratio of cones to optic nerves in the fovea is one. This increases the visual acuity of the fovea. Rods and Cones Photosensitive substance in rods is called rhodopsin and in cones is called iodopsin. The Photochemistry of Vision Rhodopsin and Iodopsin Cis-retinal +Opsin Light sensitive Light Trans-retinal Opsin Light insensitive This is a reversible reaction Cis-retinal +Opsin Light sensitive Vitamin A In the presence of light, the rods and cones are hyperpolarized mv t Hyperpolarizing potential lasts for upto half a second, leading to the perception of fusion of flickering lights. Receptor potential – rod and cone potential Hyperpolarizing receptor potential caused by rhodopsin decomposition. Hyperpolarizing potential lasts for upto half a second, leading to the fusion of flickering lights. Light and dark adaptation in the visual system The eye is capable of vision in conditions of light that vary greatly in intensity The eye adapts dynamically to lighting conditions Light and Dark Adaptation We are able to see in light intensities that vary greatly. -
Informing Computer Vision with Optical Illusions
Informing Computer Vision with Optical Illusions Nasim Nematzadeh David M. W. Powers Trent Lewis College of Science and Engineering College of Science and Engineering College of Science and Engineering Flinders University Flinders University Flinders University Adelaide, Australia Adelaide, Australia Adelaide, Australia [email protected] [email protected] [email protected] Abstract— Illusions are fascinating and immediately catch the increasingly detailed biological characterization of both people's attention and interest, but they are also valuable in retinal and cortical cells over the last half a century (1960s- terms of giving us insights into human cognition and perception. 2010s), there remains considerable uncertainty, and even some A good theory of human perception should be able to explain the controversy, as to the nature and extent of the encoding of illusion, and a correct theory will actually give quantifiable visual information by the retina, and conversely of the results. We investigate here the efficiency of a computational subsequent processing and decoding in the cortex [6, 8]. filtering model utilised for modelling the lateral inhibition of retinal ganglion cells and their responses to a range of Geometric We explore the response of a simple bioplausible model of Illusions using isotropic Differences of Gaussian filters. This low-level vision on Geometric/Tile Illusions, reproducing the study explores the way in which illusions have been explained misperception of their geometry, that we reported for the Café and shows how a simple standard model of vision based on Wall and some Tile Illusions [2, 5] and here will report on a classical receptive fields can predict the existence of these range of Geometric illusions. -
A Neural Model of the Scintillating Grid Illusion: Disinhibi- Tion and Self-Inhibition in Early Vision
LETTER Communicated by Sidney Lehky A Neural Model of the Scintillating Grid Illusion: Disinhibi- tion and Self-Inhibition in Early Vision Yingwei Yu Downloaded from http://direct.mit.edu/neco/article-pdf/18/3/521/816498/neco.2006.18.3.521.pdf by guest on 24 September 2021 [email protected] Yoonsuck Choe [email protected] Department of Computer Science, Texas A&M University, College Station, Texas 77843-3112, U.S.A A stationary display of white discs positioned on intersecting gray bars on a dark background gives rise to a striking scintillating effect—the scin- tillating grid illusion. The spatial and temporal properties of the illusion are well known, but a neuronal-level explanation of the mechanism has not been fully investigated. Motivated by the neurophysiology of the Limulus retina, we propose disinhibition and self-inhibition as possible neural mechanisms that may give rise to the illusion. In this letter, a spa- tiotemporal model of the early visual pathway is derived that explicitly accounts for these two mechanisms. The model successfully predicted the change of strength in the illusion under various stimulus conditions, indicating that low-level mechanisms may well explain the scintillating effect in the illusion. 1 Introduction The scintillating grid illusion consists of bright discs superimposed on in- tersections of orthogonal gray bars on a dark background (see Figure 1A; Schrauf, Lingelbach, & Wist, 1997). In this illusion, illusory dark spots are perceived as scintillating within the white discs. Several important prop- erties of the illusion have been discovered and reported inrecent years. (1) The discs that are closer to a fixation show less scintillation (Schrauf et al., 1997), which might be due to the fact that receptive fields in the periphery are larger than those in the fovea. -
The Neocognitron As a System for Handavritten Character Recognition: Limitations and Improvements
The Neocognitron as a System for HandAvritten Character Recognition: Limitations and Improvements David R. Lovell A thesis submitted for the degree of Doctor of Philosophy Department of Electrical and Computer Engineering University of Queensland March 14, 1994 THEUliW^^ This document was prepared using T^X and WT^^. Figures were prepared using tgif which is copyright © 1992 William Chia-Wei Cheng (william(Dcs .UCLA. edu). Graphs were produced with gnuplot which is copyright © 1991 Thomas Williams and Colin Kelley. T^ is a trademark of the American Mathematical Society. Statement of Originality The work presented in this thesis is, to the best of my knowledge and belief, original, except as acknowledged in the text, and the material has not been subnaitted, either in whole or in part, for a degree at this or any other university. David R. Lovell, March 14, 1994 Abstract This thesis is about the neocognitron, a neural network that was proposed by Fuku- shima in 1979. Inspired by Hubel and Wiesel's serial model of processing in the visual cortex, the neocognitron was initially intended as a self-organizing model of vision, however, we are concerned with the supervised version of the network, put forward by Fukushima in 1983. Through "training with a teacher", Fukushima hoped to obtain a character recognition system that was tolerant of shifts and deformations in input images. Until now though, it has not been clear whether Fukushima's ap- proach has resulted in a network that can rival the performance of other recognition systems. In the first three chapters of this thesis, the biological basis, operational principles and mathematical implementation of the supervised neocognitron are presented in detail. -
Different Roles for Simple-Cell and Complex-Cell Inhibition in V1
The Journal of Neuroscience, November 12, 2003 • 23(32):10201–10213 • 10201 Behavioral/Systems/Cognitive Different Roles for Simple-Cell and Complex-Cell Inhibition in V1 Thomas Z. Lauritzen1,4 and Kenneth D. Miller1,2,3,4 1Graduate Group in Biophysics, 2Departments of Physiology and Otolaryngology, 3Sloan-Swartz Center for Theoretical Neurobiology, and 4W. M. Keck Center for Integrative Neuroscience, University of California, San Francisco, California 94143-0444 Previously, we proposed a model of the circuitry underlying simple-cell responses in cat primary visual cortex (V1) layer 4. We argued that the ordered arrangement of lateral geniculate nucleus inputs to a simple cell must be supplemented by a component of feedforward inhibition that is untuned for orientation and responds to high temporal frequencies to explain the sharp contrast-invariant orientation tuning and low-pass temporal frequency tuning of simple cells. The temporal tuning also requires a significant NMDA component in geniculocortical synapses. Recent experiments have revealed cat V1 layer 4 inhibitory neurons with two distinct types of receptive fields (RFs): complex RFs with mixed ON/OFF responses lacking in orientation tuning, and simple RFs with normal, sharp-orientation tuning (although, some respond to all orientations). We show that complex inhibitory neurons can provide the inhibition needed to explain simple-cell response properties. Given this complex cell inhibition, antiphase or “push-pull” inhibition from tuned simple inhibitory neurons acts to sharpen -
Development of Spatial Coarse-To-Fine Processing in the Visual Pathway
Development of spatial coarse-to-fine processing in the visual pathway Jasmine A. Nirody ∗ July 16, 2013 Abstract The sequential analysis of information in a coarse-to-fine manner is a fundamental mode of processing in the visual pathway. Spatial frequency (SF) tuning, arguably the most fundamental feature of spatial vision, provides particular intuition within the coarse-to-fine framework: low spatial frequencies convey global information about an image (e.g., general orientation), while high spatial frequencies carry more detailed information (e.g., edges). In this paper, we study the development of cortical spatial frequency tuning. As feedforward input from the lateral geniculate nucleus (LGN) has been shown to have significant influence on cortical coarse-to-fine processing, we present a firing-rate based thalamocortical model which includes both feedforward and feedback components. We analyze the relationship between various model parameters (including cortical feedback strength) and responses. We confirm the importance of the antagonistic relationship between the center and surround responses in thalamic relay cell receptive fields (RFs), and further characterize how specific structural LGN RF parameters affect cortical coarse-to-fine processing. Our results also indicate that the effect of cortical feedback on spatial frequency tuning is age-dependent: in particular, cortical feedback more strongly affects coarse-to-fine processing in kittens than in adults. We use our results to propose an experimentally testable hypothesis for the function of the extensive feedback in the corticothalamic circuit. 1 Introduction The mode of information processing in neural sensory systems has been the subject of many experimental and computational studies. It is intuitive that visual information is processed sequentially in a coarse-to-fine manner—when looking quickly at a scene, we process the general features before focusing on individual objects. -
Color Appearance Models Second Edition
Color Appearance Models Second Edition Mark D. Fairchild Munsell Color Science Laboratory Rochester Institute of Technology, USA Color Appearance Models Wiley–IS&T Series in Imaging Science and Technology Series Editor: Michael A. Kriss Formerly of the Eastman Kodak Research Laboratories and the University of Rochester The Reproduction of Colour (6th Edition) R. W. G. Hunt Color Appearance Models (2nd Edition) Mark D. Fairchild Published in Association with the Society for Imaging Science and Technology Color Appearance Models Second Edition Mark D. Fairchild Munsell Color Science Laboratory Rochester Institute of Technology, USA Copyright © 2005 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England Telephone (+44) 1243 779777 This book was previously publisher by Pearson Education, Inc Email (for orders and customer service enquiries): [email protected] Visit our Home Page on www.wileyeurope.com or www.wiley.com All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher. Requests to the Publisher should be addressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or emailed to [email protected], or faxed to (+44) 1243 770571. This publication is designed to offer Authors the opportunity to publish accurate and authoritative information in regard to the subject matter covered. -
Spatial Properties and Functional Organization of Small Bistratified Ganglion Cells in Primate Retina
The Journal of Neuroscience, November 28, 2007 • 27(48):13261–13272 • 13261 Behavioral/Systems/Cognitive Spatial Properties and Functional Organization of Small Bistratified Ganglion Cells in Primate Retina Greg D. Field,1* Alexander Sher,2* Jeffrey L. Gauthier,1* Martin Greschner,1 Jonathon Shlens,1 Alan M. Litke,2 and E. J. Chichilnisky1 1Salk Institute for Biological Studies, La Jolla, California 92037, and 2Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California 95064 The primate visual system consists of parallel pathways initiated by distinct cell types in the retina that encode different features of the visual scene. Small bistratified cells (SBCs), which form a major projection to the thalamus, exhibit blue-ON/yellow-OFF [S-ON/(LϩM)- OFF] light responses thought to be important for high-acuity color vision. However, the spatial processing properties of individual SBCs and their spatial arrangement across the visual field are poorly understood. The present study of peripheral primate retina reveals that contrary to previous suggestions, SBCs exhibit center-surround spatial structure, with the (LϩM)-OFF component of the receptive field ϳ50% larger in diameter than the S-ON component. Analysis of response kinetics shows that the (LϩM)-OFF response in SBCs is slower thantheS-ONresponseandsignificantlylesstransientthanthatofsimultaneouslyrecordedOFF-parasolcells.The(LϩM)-OFFresponse in SBCs was eliminated by bath application of the metabotropic glutamate receptor agonist L-APB. These observations indicate that the (LϩM)-OFF response of SBCs is not formed by OFF-bipolar cell input as has been suspected and suggest that it arises from horizontal cell feedback. Finally, the receptive fields of SBCs form orderly mosaics, with overlap and regularity similar to those of ON-parasol cells. -
Arxiv:1907.09019V2 [Cs.CV] 5 Aug 2019
Preprint IMAGENET-TRAINED DEEP NEURAL NETWORK EX- HIBITS ILLUSION-LIKE RESPONSE TO THE SCINTILLAT- ING GRID Eric Sun Ron Dekel Department of Physics Department of Neurobiology Department of Chemistry and Chemical Biology Weizmann Institute of Science Harvard University Rehovot, PA 7610001, Israel Cambridge, MA 02138, USA [email protected] eric [email protected] ABSTRACT Deep neural network (DNN) models for computer vision are now capable of human-level object recognition. Consequently, similarities in the performance and vulnerabilities of DNN and human vision are of great interest. Here we charac- terize the response of the VGG-19 DNN to images of the Scintillating Grid visual illusion, in which white dots are perceived to be partially black. We observed a significant deviation from the expected monotonic relation between VGG-19 rep- resentational dissimilarity and dot whiteness in the Scintillating Grid. That is, a linear increase in dot whiteness leads to a non-linear increase and then, remark- ably, a decrease (non-monotonicity) in representational dissimilarity. In control images, mostly monotonic relations between representational dissimilarity and dot whiteness were observed. Furthermore, the dot whiteness level correspond- ing to the maximal representational dissimilarity (i.e. onset of non-monotonic dissimilarity) matched closely with that corresponding to the onset of illusion per- ception in human observers. As such, the non-monotonic response in the DNN is a potential model correlate for human illusion perception. 1 INTRODUCTION Deep neural network (DNN) models are capable of besting human champions in chess [18] and Go [20] and reaching superhuman levels of accuracy in image classification and object recognition tasks [3, 11, 5, 18].