Revealing Detail along the Visual Hierarchy: Neural Clustering Preserves Acuity from V1 to V4

Lu, Ying; Yin, Jiapeng; Chen, Zheyuan; Gong, Haimei; Liu, Ye; Qian, Liling; Li, Xiaohong; Li, Rui; Andolina, Ian M.; Wang, Wei

doi:10.1016/j.neuron.2018.03.009

Cited by 72 publications

(96 citation statements)

References 79 publications

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“…The range of preferred spatial frequencies was expectedly higher at near-foveal eccentricities than at extra-foveal ones (Fig. 6c ) 7 (also consistent with cortical visual areas 32 – 35 ), but the overall population curves were primarily low-pass (black curves in Fig. 6b ).…”

Section: Resultssupporting

confidence: 67%

Spatial frequency sensitivity in macaque midbrain

Chen¹,

Sonnenberg²,

Weller³

et al. 2018

Nat Commun

109

107

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Visual brain areas exhibit tuning characteristics well suited for image statistics present in our natural environment. However, visual sensation is an active process, and if there are any brain areas that ought to be particularly in tune with natural scene statistics, it would be sensory-motor areas critical for guiding behavior. Here we found that the rhesus macaque superior colliculus, a structure instrumental for rapid visual exploration with saccades, detects low spatial frequencies, which are the most prevalent in natural scenes, much more rapidly than high spatial frequencies. Importantly, this accelerated detection happens independently of whether a neuron is more or less sensitive to low spatial frequencies to begin with. At the population level, the superior colliculus additionally over-represents low spatial frequencies in neural response sensitivity, even at near-foveal eccentricities. Thus, the superior colliculus possesses both temporal and response gain mechanisms for efficient gaze realignment in low-spatial-frequency-dominated natural environments.

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Section: Resultssupporting

confidence: 67%

Spatial frequency sensitivity in macaque midbrain

Chen¹,

Sonnenberg²,

Weller³

et al. 2018

Nat Commun

109

107

View full text Add to dashboard Cite

show abstract

“…7a). Functional organization of low-order orientation and spatial frequency representation in macaque V4 had been visualized by intrinsic signal optical imaging 15,16 , consistent with our results. For more complex features, few studies have been carried out in V4 to characterize the functional organization, let alone at single cellular resolution.…”

Section: Discussionsupporting

confidence: 90%

“…The functional organization of V4 has previously been visualized by intrinsic signal optical imaging, and cortical representation of orientation, colour and spatial frequency has been systematically studied and demonstrated 15,16 , suggesting that V4 bears some similarities with V1 in representing lower-order visual information. However, how complex feature selective neurons in V4 are spatially organised and whether feature columns found in IT also exist in V4 remain unclear.…”

Section: Introductionmentioning

confidence: 99%

Clustered Functional Domains for Curves and Corners in Cortical Area V4

Jiang

Andolina

et al. 2019

Preprint

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As an intermediate stage of the ventral visual pathway, V4 plays a crucial role in transforming basic orientation information into higher-ordered object representations. However, the neural mechanisms underlying the encoding of simple, complex and intermediate features in V4 remain poorly understood.Using two-photon calcium imaging in awake macaques, we recorded the responses of large populations of V4 neurons to thousands of natural images. To understand these high-dimensional data sets, we performed dimensional reduction analysis on the neuronal population responses. We found orthogonal dimensions encoding orientation and more complex features, with curves and corners represented separately. These distinct feature dimensions were encoded by spatially clustered subpopulations of neurons organized in iso-orientation and curvature domains. Using synthetic stimuli, we confirmed that curvature and corner selective neurons in V4 were tuned to integral features rather than combinations of local orientation compartments. Our results show that curves and corners are encoded by neurons clustered into functional domains separate from orientation. This functionally specific population architecture may serve to facilitate local computations that expedite more complex shape and object representations at higher levels of the ventral pathway.

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“…A. Cohen, Alvarez, Nakayama, & Konkle, 2016;Grill-Spector et al, 1999;Haushofer, Livingstone, & Kanwisher, 2008;Op de Beeck, Torfs, & Wagemans, 2008), these findings alone are not uniquely supportive of a strict hierarchical account. Indeed, these findings are consistent with our simulations, as are recent finding demonstrating that even lower-level features are encoded, in some cases better, in latter brain regions (Ahlheim & Love, 2018;Hong, Yamins, Majaj, & DiCarlo, 2016;Lescroart & Gallant, 2019;Lu et al, 2018;Rice, Watson, Hartley, & Andrews, 2014). These findings and our results suggest that functionally there is an inverted pyramid in which more features are encoded at more advanced network (and brain) layers.…”

Section: Levels Of Representation In a Deep Learning Model Of Categorsupporting

confidence: 93%

Levels of Representation in a Deep Learning Model of Categorization

Guest

Love

2019

Preprint

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Deep convolutional neural networks (DCNNs) rival humans in object recognition. The layers (or levels of representation) in DCNNs have been successfully aligned with processing stages along the ventral stream for visual processing. Here, we propose a model of concept learning that uses visual representations from these networks to build memory representations of novel categories, which may rely on the medial temporal lobe (MTL) and medial prefrontal cortex (mPFC). Our approach opens up two possibilities: a) formal investigations can involve photographic stimuli as opposed to stimuli handcrafted and coded by the experimenter; b) model comparison can determine which level of representation within a DCNN a learner is using during categorization decisions. Pursuing the latter point, DCNNs suggest that the shape bias in children relies on representations at more advanced network layers whereas a learner that relied on lower network layers would display a color bias. These results confirm the role of natural statistics in the shape bias (i.e., shape is predictive of category membership) while highlighting that the type of statistics matter, i.e., those from lower or higher levels of representation. We use the same approach to provide evidence that pigeons performing seemingly sophisticated categorization of complex imagery may in fact be relying on representations that are very low-level (i.e., retinotopic). Although complex features, such as shape, relatively predominate at more advanced network layers, even simple features, such as spatial frequency and orientation, are better represented at the more advanced layers, contrary to a standard hierarchical view.

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Revealing Detail along the Visual Hierarchy: Neural Clustering Preserves Acuity from V1 to V4

Cited by 72 publications

References 79 publications

Spatial frequency sensitivity in macaque midbrain

Spatial frequency sensitivity in macaque midbrain

Clustered Functional Domains for Curves and Corners in Cortical Area V4

Levels of Representation in a Deep Learning Model of Categorization

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