Contextual modulation of orientation tuning contributes to efficient processing of natural stimuli

Felsen, Gidon; Touryan, Jon; Dan, Yang

doi:10.1080/09548980500463347

Cited by 40 publications

(61 citation statements)

References 34 publications

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“…3A). Similar changes in gain have been reported in macaque V1 (Müller et al, 1999;Wissig and Kohn, 2012;Patterson et al, 2013) and in cat V1 (Dragoi et al, 2000(Dragoi et al, , 2001Felsen et al, 2002Felsen et al, , 2005Benucci et al, 2013). The orientation-tuned reduction in gain largely (but incompletely) compensated for the imbalance in mean responses evoked by a biased stimulus distribution (Fig.…”

Section: Scaling and Shifting Tuning Curvessupporting

confidence: 62%

“…In primary visual cortex, prolonged viewing of an oriented stimulus (the adapter) results in response suppression for neurons tuned near the adapter and a repulsive shift of orientation tuning curves away from the adapter (Müller et al, 1999;Dragoi et al, 2000Dragoi et al, , 2001Felsen et al, 2002;Benucci et al, 2013). A standard view of adaptation is that it represents a simple fatigue-like process in highly responsive neurons (i.e., in which adaptation only adjusts the gain of individual neurons according to their recent history of responses) (Maffei et al, 1973;Carandini and Ferster, 1997).…”

Section: Introductionmentioning

confidence: 99%

“…Divisive normalization (or gain control) has also been used to explain some adaptation phenomena (Ohzawa et al, 1982;Heeger, 1992b;Wainwright et al, 2002;Wissig and Kohn, 2012;Solomon and Kohn, 2014). The model that we propose updates the normalization weights between each pair of neurons based on the product of their responses and could be implemented by a simple synaptic mechanism for coincidence detection (Levy and Steward, 1983;Yao and Dan, 2001;Yao et al, 2004). The model has no free parameters determining the shape of tuning curves following adaptation.…”

Section: Introductionmentioning

confidence: 99%

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Pattern Adaptation and Normalization Reweighting

Westrick¹,

Heeger

Landy

2016

J. Neurosci.

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Adaptation to an oriented stimulus changes both the gain and preferred orientation of neural responses in V1. Neurons tuned near the adapted orientation are suppressed, and their preferred orientations shift away from the adapter. We propose a model in which weights of divisive normalization are dynamically adjusted to homeostatically maintain response products between pairs of neurons. We demonstrate that this adjustment can be performed by a very simple learning rule. Simulations of this model closely match existing data from visual adaptation experiments. We consider several alternative models, including variants based on homeostatic maintenance of response correlations or covariance, as well as feedforward gain-control models with multiple layers, and we demonstrate that homeostatic maintenance of response products provides the best account of the physiological data.

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Section: Scaling and Shifting Tuning Curvessupporting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pattern Adaptation and Normalization Reweighting

Westrick¹,

Heeger

Landy

2016

J. Neurosci.

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“…However, this itself runs into other problems, such as being silent about the marked similarity between the effects of temporal context (as in the tilt aftereffect) and spatial context (as in the tilt illusion) (Felsen et al, 2005;Schwartz et al, 2007). Of course, the latter has yet to be shown to operate across levels.…”

Section: Computational Implicationsmentioning

confidence: 99%

Adaptation across the Cortical Hierarchy: Low-Level Curve Adaptation Affects High-Level Facial-Expression Judgments

Xu¹,

Dayan²,

Lipkin³

et al. 2008

J. Neurosci.

101

122

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Adaptation is ubiquitous in sensory processing. Although sensory processing is hierarchical, with neurons at higher levels exhibiting greater degrees of tuning complexity and invariance than those at lower levels, few experimental or theoretical studies address how adaptation at one hierarchical level affects processing at others. Nevertheless, this issue is critical for understanding cortical coding and computation. Therefore, we examined whether perception of high-level facial expressions can be affected by adaptation to low-level curves (i.e., the shape of a mouth). After adapting to a concave curve, subjects more frequently perceived faces as happy, and after adapting to a convex curve, subjects more frequently perceived faces as sad. We observed this multilevel aftereffect with both cartoon and real test faces when the adapting curve and the mouths of the test faces had the same location. However, when we placed the adapting curve 0.2°below the test faces, the effect disappeared. Surprisingly, this positional specificity held even when real faces, instead of curves, were the adapting stimuli, suggesting that it is a general property for facial-expression aftereffects. We also studied the converse question of whether face adaptation affects curvature judgments, and found such effects after adapting to a cartoon face, but not a real face. Our results suggest that there is a local component in facial-expression representation, in addition to holistic representations emphasized in previous studies. By showing that adaptation can propagate up the cortical hierarchy, our findings also challenge existing functional accounts of adaptation.

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“…However, other studies have suggested that neurons along the visual pathway encode complex properties of natural scenes (Simoncelli and Olshausen, 2001;Felsen et al, 2005b). Additional studies showed that natural stimuli elicit complex neural modulations (either facilitation or suppression) originating from within the classical receptive field or its surroundings (David et al, 2004;Felsen et al, 2005a). To better understand the neural encoding of natural stimuli we simultaneously recorded neural populations' response from thousands of points spread over V1.…”

Section: Introductionmentioning

confidence: 99%

Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing

et al. 2012

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The primary visual cortex (V1) is extensively studied with a large repertoire of stimuli, yet little is known about its encoding of natural images. Using voltage-sensitive dye imaging in behaving monkeys, we measured neural population response evoked in V1 by natural images presented during a face/scramble discrimination task. The population response showed two distinct phases of activity: an early phase that was spread over most of the imaged area, and a late phase that was spatially confined. To study the detailed relation between the stimulus and the population response, we used a simple encoding model to compute a continuous map of the expected neural response based on local attributes of the stimulus (luminance and contrast), followed by an analytical retinotopic transformation. Then, we computed the spatial correlation between the maps of the expected and observed response. We found that the early response was highly correlated with the local luminance of the stimulus and was sufficient to effectively discriminate between stimuli at the single trial level. The late response, on the other hand, showed a much lower correlation to the local luminance, was confined to central parts of the face images, and was highly correlated with the animal's perceptual report. Our study reveals a continuous spatial encoding of low-and high-level features of natural images in V1. The low level is directly linked to the stimulus basic local attributes and the high level is correlated with the perceptual outcome of the stimulus processing.

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Contextual modulation of orientation tuning contributes to efficient processing of natural stimuli

Cited by 40 publications

References 34 publications

Pattern Adaptation and Normalization Reweighting

Pattern Adaptation and Normalization Reweighting

Adaptation across the Cortical Hierarchy: Low-Level Curve Adaptation Affects High-Level Facial-Expression Judgments

Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing

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