Local to global normalization dynamic by nonlinear local interactions

Keil, Matthias

doi:10.1016/j.physd.2007.10.011

Cited by 5 publications

(3 citation statements)

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“…26 At first sight it seems that contrast effects, such as simultaneous brightness contrast 27 (SBC), can be explained by lateral inhibition between a target (center) and its context 28 (surround). However, activity related to brightness contrast does possibly not occur 29 before V1, albeit the receptive fields of retinal ganglion cells are consistent with lateral 30 inhibition [1]. 31 Unlike contrast, brightness assimilation pulls a target's brightness towards to that of 32 its immediate context, and therefore cannot be explained by mechanisms based on plain 33 lateral inhibition.…”

Section: Introduction 17mentioning

confidence: 76%

“…To this end, he relies heavily on the max-operator, which he justified 139 with a dendritic circuit proposal. In comparison, [29] used a modified diffusion operator, 140 which could be efficiently implemented (both physiologically and computationally) with 141 rectifying gap-junctions or rectifying dendro-dendritic connections [17,29]. For filling-in, 142 Domijan's used a luminance sensitive pathway.…”

Section: Introduction 17mentioning

confidence: 99%

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Predictive coding as a unifying principle for explaining a broad range of brightness phenomena

Lerer

Supèr

Keil

2020

Preprint

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The visual system is highly sensitive to spatial context for encoding luminance patterns. Context sensitivity inspired the proposal of many neural mechanisms for explaining the perception of luminance (brightness). Here we propose a novel computational model for estimating the brightness of many visual illusions. We hypothesize that many aspects of brightness can be explained by a predictive coding mechanism, which reduces the redundancy in edge representations on the one hand, while non-redundant activity is enhanced on the other (response equalization). Response equalization is implemented with a dynamic filtering process, which (dynamically) adapts to each input image. Dynamic filtering is applied to the responses of complex cells in order to build a gain control map. The gain control map then acts on simple cell responses before they are used to create a brightness map via activity propagation. Our approach is successful in predicting many challenging visual illusions, including contrast effects, assimilation, and reverse contrast. Author summaryWe hardly notice that what we see is often different from the physical world "outside" 1 of the brain. This means that the visual experience that the brain actively constructs 2 may be different from the actual physical properties of objects in the world. In this 3 work, we propose a hypothesis about how the visual system of the brain may construct 4 a representation for achromatic images. Since this process is not unambiguous, 5 sometimes we notice "errors" in our perception, which cause visual illusions. The 6 challenge for theorists, therefore, is to propose computational principles that recreate a 7 large number of visual illusions and to explain why they occur. Notably, our proposed 8 mechanism explains a broader set of visual illusions than any previously published 9 proposal. We achieved this by trying to suppress predictable information. For example, 10 if an image contained repetitive structures, then these structures are predictable and 11 would be suppressed. In this way, non-predictable structures stand out. Predictive 12 coding mechanisms act as early as in the retina (which enhances luminance changes but 13 suppresses uniform regions of luminance), and our computational model holds that this 14 principle also acts at the next stage in the visual system, where representations of 15 perceived luminance (brightness) are created. 16April 18, 2020 1/30 42 suggested a pattern-specific inhibition mechanism acting in the visual cortex, which 43 inhibits regularly arranged patterns of a visual stimulus. 44Anchoring theory is a rule-based mechanism for segregating the contributions of 45 illumination and reflectance in brightness, and thus to map luminance to perceived gray 46 levels [4]. Accordingly, a visual image is first divided into one or more perceptual 47 frameworks (a framework is a set of surfaces that are grouped together). Within each 48 framework, the highest luminance value is anchored at perceived white, and smaller 49 luminance values are mapped to...

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Section: Introduction 17mentioning

confidence: 76%

Section: Introduction 17mentioning

confidence: 99%

Predictive coding as a unifying principle for explaining a broad range of brightness phenomena

Lerer

Supèr

Keil

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…A surface response (say DL) will not survive either, because D-and L-responses have equal amplitudes. The local spatial WTA-competition is established with a nonlinear diffusion paradigm [52]. The final output of the texture system (i.e., a texture representation) is computed according to equation 6, where texture brightness acts excitatory, and texture darkness inhibitory.…”

Section: Application 1: a Dynamical Retinal Modelmentioning

confidence: 99%