Role of Feedforward and Feedback Projections in Figure-Ground Responses

Arall, Marina; Romeo, August; Supèr, Hans

doi:10.5772/47753

Cited by 3 publications

(4 citation statements)

References 96 publications

(107 reference statements)

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“…During imagination, V1 neurons have larger receptive fields (RFs) and are tuned to lower spatial frequencies in imagined images than in seen images [8, 9]. During perception, the responses of V1 neurons to stimuli in classical RFs (mediated by bottom-up or feedforward connections [10]) are modulated by extra-classical RFs (mediated by top-down or feedback connections [11]) that surround classical RFs [12], which reflects the reconstruction of V1 activity by the higher-level cortex in in predictive coding formulations of perception [1]. These extra-classical RFs have larger sizes (i.e., lower spatial resolution) than classical RFs, and the modulation of neuronal activities by extra-classical RFs is insensitive to grating phase even for simple cells [13].…”

Section: Introductionmentioning

confidence: 99%

Top-down generation of low-resolution representations improves visual perception and imagination

2021

Preprint

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According to analysis-by-synthesis theories of perception, the primary visual cortex (V1) reconstructs visual stimuli through top-down pathway, and higher-order cortex reconstructs V1 activity. Experiments also found that neural representations are generated in a top-down cascade during visual imagination. What code does V1 provide higher-order cortex to reconstruct or simulate to improve perception or imaginative creativity? What unsupervised learning principles shape V1 for reconstructing stimuli so that V1 activity eigenspectrum is power-law with close-to-1 exponent? Using computational models, we reveal that reconstructing the activities of V1 complex cells facilitate higher-order cortex to form representations smooth to shape morphing of stimuli, improving perception and creativity. Power-law eigenspectrum with close-to-1 exponent results from the constraints of sparseness and temporal slowness when V1 is reconstructing stimuli, at a sparseness strength that best whitens V1 code and makes the exponent most insensitive to slowness strength. Our results provide fresh insights into V1 computation.

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Section: Introductionmentioning

confidence: 99%

Top-down generation of low-resolution representations improves visual perception and imagination

2021

Preprint

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“…For example, Supèr and Lamme ( 2007 ) observed that removing most of feedback connections from higher visual areas to V1 reduced but did not abolish figure-ground perception. In fact, Qiu et al ( 2007 ) reported that border-ownership signals emerge pre-attentively, and a purely feedforward model of figure-ground segmentation has been proposed by Supèr et al ( 2010 ), consistent with a limited role of feedback in figure-ground assignment process (also see Arall et al, 2012 , and Kogo and van Ee, 2014 , for a discussion).…”

Section: Intermediate Computationsmentioning

confidence: 88%

“…In order to extract finer details, representations in earlier layers can be reaccessed via feedback loops (indicated by backward arrows in Figure 6 ), as conceptualized by the Reverse Hierarchy Theory (Hochstein and Ahissar, 2002 ). Such feedback connections are abundant in the primate visual cortex and have been implicated to be important for various purposes (Felleman and Van Essen, 1991 ; Angelucci et al, 2002 ; Roelfsema et al, 2010 ; Arall et al, 2012 ). For example, intermediate representations could be used as saliency maps to direct attention to a particular part of an image or a particular feature (Walther and Koch, 2006 ; Russell et al, 2014 ).…”

Section: Intermediate Computationsmentioning

confidence: 99%

“…For example, intermediate representations could be used as saliency maps to direct attention to a particular part of an image or a particular feature (Walther and Koch, 2006 ; Russell et al, 2014 ). Then irrelevant inputs would be inhibited while the relevant ones would receive an enhanced weight (Mihalas et al, 2011 ; Arall et al, 2012 ; Wyatte et al, 2012 ), and the whole similarity and pooling computation would be repeated again. Such approach could be particularly important for resolving complicated parts of images that require high spatial resolution (Bullier, 2001 ), serial (or incremental) grouping of image features (Roelfsema, 2006 ), and could play a major role in learning features from input statistics (Roelfsema et al, 2010 ).…”

Section: Intermediate Computationsmentioning

confidence: 99%

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A conceptual framework of computations in mid-level vision

Kubilius

Wagemans

Beeck

2014

Front. Comput. Neurosci.

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If a picture is worth a thousand words, as an English idiom goes, what should those words—or, rather, descriptors—capture? What format of image representation would be sufficiently rich if we were to reconstruct the essence of images from their descriptors? In this paper, we set out to develop a conceptual framework that would be: (i) biologically plausible in order to provide a better mechanistic understanding of our visual system; (ii) sufficiently robust to apply in practice on realistic images; and (iii) able to tap into underlying structure of our visual world. We bring forward three key ideas. First, we argue that surface-based representations are constructed based on feature inference from the input in the intermediate processing layers of the visual system. Such representations are computed in a largely pre-semantic (prior to categorization) and pre-attentive manner using multiple cues (orientation, color, polarity, variation in orientation, and so on), and explicitly retain configural relations between features. The constructed surfaces may be partially overlapping to compensate for occlusions and are ordered in depth (figure-ground organization). Second, we propose that such intermediate representations could be formed by a hierarchical computation of similarity between features in local image patches and pooling of highly-similar units, and reestimated via recurrent loops according to the task demands. Finally, we suggest to use datasets composed of realistically rendered artificial objects and surfaces in order to better understand a model's behavior and its limitations.

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Dissociating representations of affect and motion in visual cortices

Kryklywy

Forys

Vieira

et al. 2023

Cogn Affect Behav Neurosci

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While a delicious dessert being presented to us may elicit strong feelings of happiness and excitement, the same treat falling slowly away can lead to sadness and disappointment. Our emotional response to the item depends on its visual motion direction. Despite this importance, it remains unclear whether (and how) cortical areas devoted to decoding motion direction represents or integrates emotion with perceived motion direction. Motion-selective visual area V5/MT+ sits, both functionally and anatomically, at the nexus of dorsal and ventral visual streams. These pathways, however, differ in how they are modulated by emotional cues. The current study was designed to disentangle how emotion and motion perception interact, as well as use emotion-dependent modulation of visual cortices to understand the relation of V5/MT+ to canonical processing streams. During functional magnetic resonance imaging (fMRI), approaching, receding, or static motion after-effects (MAEs) were induced on stationary positive, negative, and neutral stimuli. An independent localizer scan was conducted to identify the visual-motion area V5/MT+. Through univariate and multivariate analyses, we demonstrated that emotion representations in V5/MT+ share a more similar response profile to that observed in ventral visual than dorsal, visual structures. Specifically, V5/MT+ and ventral structures were sensitive to the emotional content of visual stimuli, whereas dorsal visual structures were not. Overall, this work highlights the critical role of V5/MT+ in the representation and processing of visually acquired emotional content. It further suggests a role for this region in utilizing affectively salient visual information to augment motion perception of biologically relevant stimuli.

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Role of Feedforward and Feedback Projections in Figure-Ground Responses

Cited by 3 publications

References 96 publications

Top-down generation of low-resolution representations improves visual perception and imagination

Top-down generation of low-resolution representations improves visual perception and imagination

A conceptual framework of computations in mid-level vision

Dissociating representations of affect and motion in visual cortices

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