Early recurrence enables figure border ownership

Mehrani, Paria; Tsotsos, John K.

doi:10.1016/j.visres.2021.04.009

Cited by 7 publications

(7 citation statements)

References 68 publications

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“…A key open question is how border ownership signals are computed. Several lines of evidence support the hypothesis that they rely on contextual information supplied by cortico-cortical feedback 9–12 . First, laminar studies found that border ownership stimuli are processed in deep cortical layers prior to granular input layers.…”

Section: Introductionmentioning

confidence: 76%

“…Despite over twenty years of debate on the nature of border ownership computations in the visual cortex, the underlying mechanisms remain unclear 4,12,19,34 . While multiple lines of evidence support a major role for contextual information fed back from a higher brain area 7,8,17,35 , it remained unclear whether grouping cells, the hypothetical source of such feedback, exist 9,20,21,33 .…”

Section: Discussionmentioning

confidence: 99%

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Grouping cells in primate visual cortex

Franken,

Reynolds

2024

Preprint

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Our perception of how objects are laid out in visual scenes is remarkably stable, despite rapid shifts in the patterns of light that fall on the retina with each saccade. One mechanism that may help establish perceptual stability is border ownership assignment. Studies in macaque area V2 have identified border ownership neurons that signal which side of a border belongs to a foreground surface. This signal persists for hundreds of milliseconds after border ownership has been rendered ambiguous by deleting the stimulus features that distinguish foreground from background. Remarkably, this signal survives eye movements: border ownership neurons also exhibit border ownership signals de novo when an eye movement places the newly ambiguous border within their receptive field. The grouping cell hypothesis proposes the existence of hypothetical grouping cells in a downstream brain area. These cells would compute persistent proto-object representations and therefore have the properties to endow cells in upstream brain areas with selectivity for border ownership. Such grouping cells have been predicted to show a centripetal and persistent pattern of preferred side of ownership for a border placed parallel to the perimeter of their classical receptive field, and such a centripetal ownership preference pattern should also occur de novo in these same cells if an ambiguous border lands in their receptive field after a saccade. It is unknown if grouping cells exist. Here we used laminar multielectrodes in area V4, the main source of feedback to V2, of behaving macaques to determine whether such grouping cells exist. Consistent with the model prediction we find a substantial population of neurons with these properties, in all laminar compartments, and they exhibit a response latency that is short enough to act as the source that endows neurons in V2 with selectivity for border ownership. While grouping cell activity provides information about the location of foreground surfaces, these neurons are, counterintuitively, not as strongly tuned for luminance contrast polarity, a feature of those surfaces, as are border ownership cells. Our data suggest a division of labor in which these newly discovered grouping cells provide spatiotemporal continuity of segmented surfaces whereas border ownership cells link this location information with surface features such as luminance contrast.

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Section: Introductionmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Grouping cells in primate visual cortex

Franken,

Reynolds

2024

Preprint

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“…Specifically, neurons in early visual areas respond differently to identical edges (borders) of objects when those objects lie at different locations outside of the CRFs(Zhou et al, 2000, Franken and Reynolds, 2021, Hesse and Tsao, 2016, Hesse and Tsao, 2023) (Figure 2). As with other forms of nCRF modulation, B own is thought to emerge either by feedback from higher areas(Craft et al, 2007, Jehee et al, 2007, Layton et al, 2012, Eguchi and Stringer, 2016, Wagatsuma et al, 2021, Mehrani and Tsotsos, 2021) or from horizontal connections within the same area(Zhaoping, 2005, Kogo et al, 2010). In V1, feedback and horizontal inputs arrive principally within supragranular and infragranular layers, i.e., they avoid layer 4(Rockland and Pandya, 1979, Rockland and Lund, 1983, Rockland and Virga, 1989, Anderson and Martin, 2009, Markov et al, 2014, Federer et al, 2021, Gilbert and Wiesel, 1983, Shmuel et al, 2005).…”

Section: Resultsmentioning

confidence: 99%

Dependence of Contextual Modulation in Macaque V1 on Interlaminar Signal Flow

Zhu,

Oh,

Trepka

et al. 2024

Preprint

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In visual cortex, neural correlates of subjective perception can be generated by modulation of activity from beyond the classical receptive field (CRF). In macaque V1, activity generated by nonclassical receptive field (nCRF) stimulation involves different intracortical circuitry than activity generated by CRF stimulation, suggesting that interactions between neurons across V1 layers differ under CRF and nCRF stimulus conditions. We measured border ownership modulation within large populations of V1 neurons. We found that neurons in single columns preferred the same side of objects located outside of the CRF. In addition, we found that interactions between pairs of neurons situated across feedback/horizontal and input layers differed between CRF and nCRF stimulation. Furthermore, the magnitude of border ownership modulation was predicted by greater information flow from feedback/horizontal to input layers. These results demonstrate that the flow of signals between layers covaries with the degree to which neurons integrate information from beyond the CRF.

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“…SparseShape, whose architecture is depicted in Figure 2a, combines and extends two previous models: RBO (Mehrani and Tsotsos, 2021) and 2DSIL (Rodríguez-Sánchez and Tsotsos, 2012). In Figure 2a, each model neuron type in the hierarchy is represented with a single box.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, neurons up to and including mBO, mEsDeg, and mEsDir are the same as those in RBO and 2DSIL, the computational details of which can be found elsewhere (Rodríguez-Sánchez and Tsotsos, 2012; Mehrani and Tsotsos, 2021). Below are outlines of the computations of new and remodeled neurons in SparseShape and the supervised learning step.…”

Section: Methodsmentioning

confidence: 99%

Learning a Model of Shape Selectivity in V4 Cells Reveals Shape Encoding Mechanisms in the Brain

Mehrani

Tsotsos

2023

J. Neurosci.

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The mechanisms involved in transforming early visual signals to curvature representations in V4 are unknown. We propose a hierarchical model that reveals V1/V2 encodings that are essential components for this transformation to the reported curvature representations in V4. Then, by relaxing the often-imposed prior of a single Gaussian, V4 shape selectivity is learned in the last layer of the hierarchy from Macaque V4 responses. We found that V4 cells integrate multiple shape parts from the full spatial extent of their receptive fields with similar excitatory and inhibitory contributions. Our results uncover new details in existing data about shape selectivity in V4 neurons that with further experiments can enhance our understanding of processing in this area. Accordingly, we propose designs for a stimulus set that allow removing shape parts awithout disturbing the curvature signal to isolate part contributions to V4 responses.Significance Statement:Selectivity to convex and concave shape parts in V4 neurons has been repeatedly reported. Nonetheless, the mechanisms that yield such selectivities in the ventral stream remain unknown. We propose a hierarchical computational model that incorporates findings of the various visual areas involved in shape processing and suggest mechanisms that transform the shape signal from low-level features to convex/concave part representations. Learning shape selectivity from Macaque V4 responses in the final processing stage in our model, we found that V4 neurons integrate shape parts from the full spatial extent of their receptive field with both facilitatory and inhibitory contributions. These results reveal hidden information in existing V4 data that with further experiments can enhance our understanding of processing in V4.

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Early recurrence enables figure border ownership

Cited by 7 publications

References 68 publications

Grouping cells in primate visual cortex

Grouping cells in primate visual cortex

Dependence of Contextual Modulation in Macaque V1 on Interlaminar Signal Flow

Learning a Model of Shape Selectivity in V4 Cells Reveals Shape Encoding Mechanisms in the Brain

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