Functional and histological properties of caudal intraparietal area of macaque monkey

Katsuyama, Narumi; Yamashita, Akiko; Sawada, Kouji; Naganuma, Tomoka; Sakata, Hideo; Taira, Masato

doi:10.1016/j.neuroscience.2010.01.028

Cited by 30 publications

(31 citation statements)

References 23 publications

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“…Results [187] suggest selectivity for zero-order disparity (position in depth) of CIP neurons. More recently, [86] also reported selectivity for curved surfaces (second-order disparity) in one monkey. CIP neurons do not respond during saccadic eye movements.…”

Section: Caudal Intraparietal Area (Cip)mentioning

confidence: 88%

Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision?

Krüger

Janssen

Kalkan

et al. 2013

IEEE Trans. Pattern Anal. Mach. Intell.

332

199

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Abstract-Computational modeling of the primate visual system yields insights of potential relevance to some of the challenges that computer vision is facing, such as object recognition and categorization, motion detection and activity recognition or vision-based navigation and manipulation. This article reviews some functional principles and structures that are generally thought to underlie the primate visual cortex, and attempts to extract biological principles that could further advance computer vision research. Organized for a computer vision audience, we present functional principles of the processing hierarchies present in the primate visual system considering recent discoveries in neurophysiology. The hierarchal processing in the primate visual system is characterized by a sequence of different levels of processing (in the order of ten) that constitute a deep hierarchy in contrast to the flat vision architectures predominantly used in today's mainstream computer vision. We hope that the functional description of the deep hierarchies realized in the primate visual system provides valuable insights for the design of computer vision algorithms, fostering increasingly productive interaction between biological and computer vision research.

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Section: Caudal Intraparietal Area (Cip)mentioning

confidence: 88%

Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision?

Krüger

Janssen

Kalkan

et al. 2013

IEEE Trans. Pattern Anal. Mach. Intell.

332

199

View full text Add to dashboard Cite

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“…For example, CIP neurons appear to show tilt tuning for texture gradients that is quite robust, compared with disparity gradients (Tsutsui et al 2002). This suggests that texture responses in area CIP do not arise through inputs from MT, but rather depend on inputs from other areas such as V3A (Katsuyama et al 2010;Nakamura et al 2001). Thus conducting similar experiments in area V3A is likely to be of considerable interest.…”

Section: Discussionmentioning

confidence: 99%

Representation of 3-D surface orientation by velocity and disparity gradient cues in area MT

Sanada

Nguyenkim

DeAngelis

2012

Journal of Neurophysiology

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Sanada TM, Nguyenkim JD, DeAngelis GC. Representation of 3-D surface orientation by velocity and disparity gradient cues in area MT. J Neurophysiol 107: 2109 -2122, 2012. First published January 4, 2012; doi:10.1152/jn.00578.2011.-Neural coding of the threedimensional (3-D) orientation of planar surface patches may be an important intermediate step in constructing representations of complex 3-D surface structure. Spatial gradients of binocular disparity, image velocity, and texture provide potent cues to the 3-D orientation (tilt and slant) of planar surfaces. Previous studies have described neurons in both dorsal and ventral stream areas that are selective for surface tilt based on one or more of these gradient cues. However, relatively little is known about whether single neurons provide consistent information about surface orientation from multiple gradient cues. Moreover, it is unclear how neural responses to combinations of surface orientation cues are related to responses to the individual cues. We measured responses of middle temporal (MT) neurons to random dot stimuli that simulated planar surfaces at a variety of tilts and slants. Four cue conditions were tested: disparity, velocity, and texture gradients alone, as well as all three gradient cues combined. Many neurons showed robust tuning for surface tilt based on disparity and velocity gradients, with relatively little selectivity for texture gradients. Some neurons showed consistent tilt preferences for disparity and velocity cues, whereas others showed large discrepancies. Responses to the combined stimulus were generally well described as a weighted linear sum of responses to the individual cues, even when disparity and velocity preferences were discrepant. These findings suggest that area MT contains a rudimentary representation of 3-D surface orientation based on multiple cues, with single neurons implementing a simple cue integration rule. depth; slant; surface; tilt; visual cortex; middle temporal area THE VISUAL SYSTEM RECONSTRUCTS three-dimensional (3-D) scene structure from images projected onto the two retinas. Many cues, including binocular disparity, relative motion, texture, shading, and perspective, are used to perceive 3-D structure. Most complex surfaces can be approximated by combinations of locally planar surfaces. Thus understanding how planar surfaces are coded in visual cortex may help reveal how complex surface representations are constructed. The 3-D orientation of a plane (tilt and slant) can be specified by gradients of binocular disparity, motion (velocity), or texture. Human perception of 3-D surface orientation from these cues has been well studied, and the findings are often well explained by Bayesian models (Girshick and Banks 2009;Hillis et al. 2004;Jacobs 1999;Knill 2007;Knill and Saunders 2003).Physiological studies in macaques have identified neurons that signal the 3-D orientation of planar surfaces. In the ventral stream, 3-D orientation tuning has been reported in area V4 for disparity gradients (Hegde and Van Ess...

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“…In monkeys, CIP neurons can signal the 3D orientation of large planar surfaces when either disparity or texture gradients are used as depth cues (Tsutsui, Sakata, Naganuma, & Taira, 2002). CIP neurons can also be selective for concave and convex surfaces defined by disparity (Katsuyama, et al, 2010), and jointly encode the tilt and slant of large planar surfaces (Rosenberg, Cowan, & Angelaki, 2013). In addition to CIP, AIP neurons in monkeys also respond selectively to concave and convex surfaces that have identical contours (Srivastava, Orban, De Maziere, & Janssen, 2009).…”

Section: Representation Of Object Informationmentioning

confidence: 99%

A brief comparative review of primate posterior parietal cortex: A novel hypothesis on the human toolmaker

et al. 2017

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The primate visual system contains two major cortical pathways: a ventral-temporal pathway that has been associated with object processing and recognition, and a dorsal-parietal pathway that has been associated with spatial processing and action guidance. Our understanding of the role of the dorsal pathway, in particular, has greatly evolved within the framework of the two-pathway hypothesis since its original conception. Here, we present a comparative review of the primate dorsal pathway in humans and monkeys based on electrophysiological, neuroimaging, neuropsychological, and neuroanatomical studies. We consider similarities and differences across species in terms of the topographic representation of visual space; specificity for eye, reaching, or grasping movements; multi-modal response properties; and the representation of objects and tools. We also review the relative anatomical location of functionally- and topographically-defined regions of the posterior parietal cortex. An emerging theme from this comparative analysis is that non-spatial information is represented to a greater degree, and with increased complexity, in the human dorsal visual system. We propose that non-spatial information in the primate parietal cortex contributes to the perception-to-action system aimed at manipulating objects in peripersonal space. In humans, this network has expanded in multiple ways, including the development of a dorsal object vision system mirroring the complexity of the ventral stream, the integration of object information with parietal working memory systems, and the emergence of tool-specific object representations in the anterior intraparietal sulcus and regions of the inferior parietal lobe. We propose that these evolutionary changes have enabled the emergence of human-specific behaviors, such as the sophisticated use of tools.

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Functional and histological properties of caudal intraparietal area of macaque monkey

Cited by 30 publications

References 23 publications

Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision?

Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision?

Representation of 3-D surface orientation by velocity and disparity gradient cues in area MT

A brief comparative review of primate posterior parietal cortex: A novel hypothesis on the human toolmaker

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