Dataset Information

Image statistics underlying natural texture selectivity of neurons in macaque V4.

ABSTRACT: Our daily visual experiences are inevitably linked to recognizing the rich variety of textures. However, how the brain encodes and differentiates a plethora of natural textures remains poorly understood. Here, we show that many neurons in macaque V4 selectively encode sparse combinations of higher-order image statistics to represent natural textures. We systematically explored neural selectivity in a high-dimensional texture space by combining texture synthesis and efficient-sampling techniques. This yielded parameterized models for individual texture-selective neurons. The models provided parsimonious but powerful predictors for each neuron's preferred textures using a sparse combination of image statistics. As a whole population, the neuronal tuning was distributed in a way suitable for categorizing textures and quantitatively predicts human ability to discriminate textures. Together, we suggest that the collective representation of visual image statistics in V4 plays a key role in organizing the natural texture perception.

SUBMITTER: Okazawa G

PROVIDER: S-EPMC4313822 | biostudies-other | 2015 Jan

REPOSITORIES: biostudies-other

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Image statistics underlying natural texture selectivity of neurons in macaque V4.

Okazawa Gouki G Tajima Satohiro S Komatsu Hidehiko H

Proceedings of the National Academy of Sciences of the United States of America 20141222 4

Our daily visual experiences are inevitably linked to recognizing the rich variety of textures. However, how the brain encodes and differentiates a plethora of natural textures remains poorly understood. Here, we show that many neurons in macaque V4 selectively encode sparse combinations of higher-order image statistics to represent natural textures. We systematically explored neural selectivity in a high-dimensional texture space by combining texture synthesis and efficient-sampling techniques. ...[more]

PMID: 25535362

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Project description:Neurons in visual area V4 modulate their responses depending on the figure-ground (FG) organization in natural images containing a variety of shapes and textures. To clarify whether the responses depend on the extents of the figure and ground regions in and around the classical receptive fields (CRFs) of the neurons, we estimated the spatial extent of local figure and ground regions that evoked FG-dependent responses (RF-FGs) in natural images and their variants. Specifically, we applied the framework of spike triggered averaging (STA) to the combinations of neural responses and human-marked segmentation images (FG labels) that represent the extents of the figure and ground regions in the corresponding natural image stimuli. FG labels were weighted by the spike counts in response to the corresponding stimuli and averaged over. The bias due to the nonuniformity of FG labels was compensated by subtracting the ensemble average of FG labels from the weighted average. Approximately 50% of the neurons showed effective RF-FGs, and a large number exhibited structures that were similar to those observed in virtual neurons with ideal FG-dependent responses. The structures of the RF-FGs exhibited a subregion responsive to a preferred side (figure or ground) around the CRF center and a subregion responsive to a non-preferred side in the surroundings. The extents of the subregions responsive to figure were smaller than those responsive to ground in agreement with the Gestalt rule. We also estimated RF-FG by an adaptive filtering (AF) method, which does not require spherical symmetry (whiteness) in stimuli. RF-FGs estimated by AF and STA exhibited similar structures, supporting the veridicality of the proposed STA. To estimate the contribution of nonlinear processing in addition to linear processing, we estimated nonlinear RF-FGs based on the framework of spike triggered covariance (STC). The analyses of the models based on STA and STC did not show inconsiderable contribution of nonlinearity, suggesting spatial variance of FG regions. The results lead to an understanding of the neural responses that underlie the segregation of figures and the construction of surfaces in intermediate-level visual areas.

Project description:Extrastriate area V4 is a critical component of visual form processing in both humans and non-human primates. Previous studies have shown that the tuning properties of V4 neurons demonstrate an intermediate level of complexity that lies between the narrow band orientation and spatial frequency tuning of neurons in primary visual cortex and the highly complex object selectivity seen in inferotemporal neurons. However, the nature of feature selectivity within this cortical area is not well understood, especially in the context of natural stimuli. Specifically, little is known about how the tuning properties of V4 neurons, measured in isolation, translate to feature selectivity within natural scenes. In this study, we assessed the degree to which preferences for natural image components can readily be inferred from classical orientation and spatial frequency tuning functions. Using a psychophysically-inspired method we isolated and identified the specific visual "driving features" occurring in natural scene photographs that reliably elicited spiking activity from single V4 neurons. We then compared the measured driving features to those predicted based on the spectral receptive field (SRF), estimated from responses to narrowband sinusoidal grating stimuli. This approach provided a quantitative framework for assessing the degree to which linear feature selectivity was preserved during natural vision. First, we found evidence of both spectrally and spatially tuned suppression within the receptive field, neither of which were present in the linear SRF. Second, we found driving features that were stable during translation of the image across the receptive field (due to small fixational eye movements). The degree of translation invariance fell along a continuum, with some cells showing nearly complete invariance across the receptive field and others exhibiting little to no position invariance. This form of limited translation invariance could indicate that a subset of V4 neurons are insensitive to small fixational eye movements, supporting perceptual stability during natural vision.

Dataset Information

Image statistics underlying natural texture selectivity of neurons in macaque V4.

Publications

Image statistics underlying natural texture selectivity of neurons in macaque V4.

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