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Bioplausible Multiscale Filtering in Retino-Cortical Processing As A Brain Informatics (2017) 4:271–293 DOI 10.1007/s40708-017-0072-8 Bioplausible multiscale filtering in retino-cortical processing as a mechanism in perceptual grouping Nasim Nematzadeh . David M. W. Powers . Trent W. Lewis Received: 25 January 2017 / Accepted: 23 August 2017 / Published online: 8 September 2017 Ó The Author(s) 2017. This article is an open access publication Abstract Why does our visual system fail to reconstruct Differences of Gaussians and the perceptual interaction of reality, when we look at certain patterns? Where do Geo- foreground and background elements. The model is a metrical illusions start to emerge in the visual pathway? variation of classical receptive field implementation for How far should we take computational models of vision simple cells in early stages of vision with the scales tuned with the same visual ability to detect illusions as we do? to the object/texture sizes in the pattern. Our results suggest This study addresses these questions, by focusing on a that this model has a high potential in revealing the specific underlying neural mechanism involved in our underlying mechanism connecting low-level filtering visual experiences that affects our final perception. Among approaches to mid- and high-level explanations such as many types of visual illusion, ‘Geometrical’ and, in par- ‘Anchoring theory’ and ‘Perceptual grouping’. ticular, ‘Tilt Illusions’ are rather important, being charac- terized by misperception of geometric patterns involving Keywords Visual perception Á Cognitive systems Á Pattern lines and tiles in combination with contrasting orientation, recognition Á Biological neural network Á Self-organizing size or position. Over the last decade, many new neuro- systems Á Classical receptive field (CRF) models Á physiological experiments have led to new insights as to Geometrical illusions Á Tilt effects Á Difference of how, when and where retinal processing takes place, and Gaussians Á Perceptual grouping Á Gestalt grouping the encoding nature of the retinal representation that is sent principles to the cortex for further processing. Based on these neu- robiological discoveries, we provide computer simulation evidence from modelling retinal ganglion cells responses to some complex Tilt Illusions, suggesting that the emergence 1 Introduction of tilt in these illusions is partially related to the interaction of multiscale visual processing performed in the retina. The We investigate here whether computational modelling of output of our low-level filtering model is presented for vision can provide similar interpretation of visual data to several types of Tilt Illusion, predicting that the final tilt our own experiences, based on simple bioplausible mod- percept arises from multiple-scale processing of the elling of multiscale retinal cell responses to the visual scene. Our visual perception of the world is the result of N. Nematzadeh (&) Á D. M. W. Powers Á T. W. Lewis multiple levels of visual processing. This starts with mul- College of Science and Engineering, Flinders University, tiple levels of visual filtering within the retina and ends in GPO Box 2100, Adelaide, SA 5001, Australia multiple levels of processing in the visual cortex. The e-mail: nasim.nematzadeh@flinders.edu.au bottom-up visual processing gives rise to simple percept or URL: http://www.flinders.edu.au/people/nasim.nematzadeh features, but multimodal and top-down information flow D. M. W. Powers leads to more complex concepts, as well as influencing e-mail: david.powers@flinders.edu.au basic perception. In our visual system, fast and accurate T. W. Lewis visual processing functions as a parsimonious system with e-mail: trent.lewis@flinders.edu.au 123 272 N. Nematzadeh et al. minimal redundancy, with the processing in the retina an intermediate brightness between the brightness of tiles, serving different purposes and operating in different ways giving rise to appearance of mortar lines as divergent and from the later processing levels of the cortex. We regard convergent instead of parallel lines. On the other hand, in this assumption as fundamental to a biologically plausible the Bulge patterns, superimposed dots on top of a simple vision model, and human-competitive computer vision, checkerboard give rise to the impression of a bulge or tilt. reflecting our understanding of human vision. This is highly affected by the precise position of dots. The Even given the increasingly detailed biological charac- illusory perception of these patterns is connected to the terization of both retinal and cortical cells over the last half figure and ground perception, and in particular, in the a century (1960s–2010s), there remains considerable Bulge patterns, grouping of dots together creates an illu- uncertainty, and even some controversy, as to the nature sory figure shape, on top of a textured background (here a and extent of the encoding of visual information by the checkerboard can be also a grid). This produces apparent retina, and conversely of the subsequent processing and border shifts of the checkerboard edges and increases or decoding in the cortex (see e.g. the review of physiological reduces the impression of the bulges or bows in these retinal findings by Field and Chichilnisky [1] and Golish patterns. and Meister [2]). 1.2 Simultaneous Brightness/Lightness Illusions 1.1 History of Geometrical illusions To be able to differentiate between competing explanations The visual distortion experiences we encounter in visual for Geometrical illusions, consider the existing techniques illusions give clues as to some of the biological charac- for explaining the simultaneous Brightness/Lightness Illu- teristics of our visual processing that result in some erro- sions such as variations of White’s effect [15–17]. When neous perception. The explanations of optical illusions rely investigating Brightness/Lightness Illusions, high-level on our interpretation of the world, and the ambiguities explanatory models are sometimes seen as a result of against our visual experiences result in the illusory percept. lightness shift of the same luminance (thus brightness), There are various types of optical illusions, and in which decodes as different lightness for example, in [18]. ‘‘Appendix’’ (Table 1) we illustrate important representa- High-level explanations need higher cortical processing of tives of the various families including impossible 3D visual clues such as lightness/transparency as well as past arrangements such as Penrose Triangle and Penrose stair- experiences and inferences [19–21]. At the same time, to case [3–5], stimuli with multistable perception, flipping address their final percept, they might involve ideas of back and forth between different perception such as Necker interpolation (1D) or filling-in (2D) [22–25] as well. More cube [6]; also Herring’s and Orbison’s illusions [7–9] recently, the ‘Anchoring theory’ of Gilchrist et al. [26]is consist of horizontal and vertical lines located on a part of a based on ‘grouping factors’ that signal depth information, radial display, inducing tilt/bow/bulge as a result of the without any consideration of the spatial frequency of the three-dimensional percept and the perspective clues. Her- pattern. Further explanations for these illusions rely on mann Grid and Mach Bands have commonly accepted ‘Junction analysis’ like T-junctions [27] and ‘Scission explanations involving the low-level visual retinal/cortical theory’ [28, 29] which triggers the parsing of targets into processing by simple cells [10, 11] and the Lateral Inhi- multiple layers of reflectance, transparency and illumina- bition (LI) mechanism, which some of them need high- tion in which erroneous decomposition leads to Brightness/ level explanations. Table 1 in ‘‘Appendix’’ reflects simi- Lightness Illusions. larity of illusions, but due to the shortage of space and Modern ‘low-level theories’ on Brightness/Lightness table arrangement, it may not exactly match other illusion Illusions, for example Kingdom and Moulden [30], and classifications [12] based on other explanations. A com- Blakeslee and McCourt [17, 31], suggest that a set of plete reference list to the source and original illusory pat- spatial frequency filters at early stages of visual processing, terns given in Table 1 is also provided in ‘‘Appendix’’ mainly preprocessing in the retina are responsible for some (Table 2). A neurobiological explanation for a variety of Brightness/Lightness Illusions. Recent investigations on Geometrical illusion can be found in [13]. diverse range of lightness/brightness/transparency (LBT) The patterns explored in this paper are ‘second-order illusions by Kingdom [32] have shown different origins for Tilt’ Illusions [14] (Tile Illusions) involving the enhance- some of these effects due to whether illusion arises from ment of contrast between textural elements of a back- encoding of brightness or the lightness of the pattern. He ground such as a checkerboard, for example Cafe´ Wall and concluded that the most promising developments in LBT is Bulging checkerboard illusions. In the Cafe´ Wall illusion, a model of brightness coding based on ‘multiscale filtering’ the illusory tilt percept is the result of mortar lines between (models such as [17]) in conjunction with ‘contrast shifted rows of black and white tiles. The mortar lines have normalization’. 123 Bioplausible multiscale filtering in retino-cortical processing as a mechanism in perceptual… 273 1.3 Tilt illusions vision, is able to highlight the emergence of tilt in these patterns
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