Neurons in monkey visual area V2 encode combinations of orientations

Anzai, Akiyuki; Peng, Xinmiao; Essen, David C. Van

doi:10.1038/nn1975

Cited by 218 publications

(261 citation statements)

References 31 publications

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“…In addition, single-unit electrophysiology experiments in area V2 revealed that neurons in this area are sensitive to combinations of orientations [11], consistent with the AND-like tuning operation in the model [28]. Furthermore, a quantitative fit was established between H-W model units and the firing rates of V4 neurons evoked by a library of stimuli.…”

Section: Consistency With Experimental Results At Multiple Levelssupporting

confidence: 56%

Throwing Down the Visual Intelligence Gauntlet

Tan

Leibo

Poggio

2013

Machine Learning for Computer Vision

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Section: Consistency With Experimental Results At Multiple Levelssupporting

confidence: 56%

Throwing Down the Visual Intelligence Gauntlet

Tan

Leibo

Poggio

2013

Machine Learning for Computer Vision

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“…Physiologically, most V1 neurons are tuned to single orientations (44) while a significant fraction of V2 neurons are tuned to combinations of orientations (45). Computationally, successful object recognition models (including HMAX and related deep-learning networks) encode single orientations and their relationships in two successive layers of processing [e.g., the study by Riesenhuber and Poggio (46)].…”

Section: Discussionmentioning

confidence: 99%

“…6A), evoking brief visual responses along the standard encoding hierarchy (44,45). Because these orientations and their relationship are task relevant, they are represented in visual working memory after the stimuli's disappearances.…”

Section: Perception As Retrospective Bayesian Decoding In Working Memorymentioning

confidence: 99%

Visual perception as retrospective Bayesian decoding from high- to low-level features

Ding

Cueva

Tsodyks

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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When a stimulus is presented, its encoding is known to progress from low-to high-level features. How these features are decoded to produce perception is less clear, and most models assume that decoding follows the same low-to high-level hierarchy of encoding. There are also theories arguing for global precedence, reversed hierarchy, or bidirectional processing, but they are descriptive without quantitative comparison with human perception. Moreover, observers often inspect different parts of a scene sequentially to form overall perception, suggesting that perceptual decoding requires working memory, yet few models consider how working-memory properties may affect decoding hierarchy. We probed decoding hierarchy by comparing absolute judgments of single orientations and relative/ordinal judgments between two sequentially presented orientations. We found that lower-level, absolute judgments failed to account for higher-level, relative/ordinal judgments. However, when ordinal judgment was used to retrospectively decode memory representations of absolute orientations, striking aspects of absolute judgments, including the correlation and forward/backward aftereffects between two reported orientations in a trial, were explained. We propose that the brain prioritizes decoding of higher-level features because they are more behaviorally relevant, and more invariant and categorical, and thus easier to specify and maintain in noisy working memory, and that more reliable higher-level decoding constrains less reliable lower-level decoding.Bayesian prior | interreport correlation | bidirectional tilt aftereffect | efficient coding | adaptation theory V isual stimuli evoke neuronal responses (a process termed encoding), which lead to our perceptual estimation of the stimuli (decoding). Experimental studies have firmly established that encoding is hierarchical, progressing from lower-level representations of simpler and less invariant features to higher-level representations of more complex and invariant features along visual pathways (1). Researchers have also studied decoding by using models to relate neuronal responses to perceptual estimation. Most models posit, explicitly or implicitly, that decoding follows the same low-to high-level hierarchy, often in the form of what we call the absolute-to-relative assumption (2-6). For example, these models may decode V1 responses to a line into a perceived orientation of, say 51.2°(or a distribution around it). Psychophysically, this is termed an absolute judgment. To determine the relationship between two lines, the models first decode each orientation separately and the two resulting absolute orientations are then compared. For instance, the two absolute orientations may be subtracted to obtain the angle between the lines (relative orientation), or the sign of the difference may be used to determine whether the second line is clockwise or counterclockwise from the first (ordinal orientation discrimination). Absolute orientation of a single line is a simpler, less invariant, lower-...

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“…For example, activation of smaller-scale bipole cells in V1 is modulated by feedback from larger-scale bipole cells in V2, such that contextual elements at a larger spatial scale may sharpen the activity at the level of V1 bipole cells (Anzai et al 2007;Grossberg and Raizada 2000;Grossberg and Swaminathan 2004;Kisvárday et al 1997;Raizada and Grossberg 2001;Shmuel et al 2005). Although this article models neurophysiological data about V1 horizontal interactions, the similarity of V1 and V2 circuits with respect to the presence of anisotropic horizontal collaterals suggests that variants of our results may also apply to V2.…”

Section: Figmentioning

confidence: 68%

Running as fast as it can: How spiking dynamics form object groupings in the laminar circuits of visual cortex

2010

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How spiking neurons cooperate to control behavioral processes is a fundamental problem in computational neuroscience. Such cooperative dynamics are required during visual perception when spatially distributed image fragments are grouped into emergent boundary contours. Perceptual grouping is a challenge for spiking cells because its properties of collinear facilitation and analog sensitivity occur in response to binary spikes with irregular timing across many interacting cells. Some models have demonstrated spiking dynamics in recurrent laminar neocortical circuits, but not how perceptual grouping occurs. Other models have analyzed the fast speed of certain percepts in terms of a single feedforward sweep of activity, but cannot explain other percepts, such as illusory contours, wherein perceptual ambiguity can take hundreds of milliseconds to resolve by integrating multiple spikes over time. The current model reconciles fast feedforward with slower feedback processing, and binary spikes with analog network-level properties, in a laminar cortical network of spiking cells whose emergent properties quantitatively simulate parametric data from neurophysiological experiments, including the formation of illusory contours; the structure of non-classical visual receptive fields; and self-synchronizing gamma oscillations. These laminar dynamics shed new light on how the brain resolves local informational ambiguities through the use of properly designed nonlinear feedback spiking networks which run as fast as they can, given the amount of uncertainty in the data that they process.

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Neurons in monkey visual area V2 encode combinations of orientations

Cited by 218 publications

References 31 publications

Throwing Down the Visual Intelligence Gauntlet

Throwing Down the Visual Intelligence Gauntlet

Visual perception as retrospective Bayesian decoding from high- to low-level features

Running as fast as it can: How spiking dynamics form object groupings in the laminar circuits of visual cortex

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