A neural code for three-dimensional object shape in macaque inferotemporal cortex

By Y. Yamane, Eric T. Carlson, Katherine C. Bowman, Zhihong Wang, C. Connor. 2008

313 citations recorded by the source corpus.

Previous investigations of the neural code for complex object shape have focused on two-dimensional pattern representation. This may be the primary mode for object vision given its simplicity and direct relation to the retinal image. In contrast, three-dimensional shape representation requires higher-dimensional coding derived from extensive computation. We found evidence for an explicit neural code for complex three-dimensional object shape. We used an evolutionary stimulus strategy and linear/nonlinear response models to characterize three-dimensional shape responses in macaque monkey inferotemporal cortex (IT). We found widespread tuning for three-dimensional spatial configurations of surface fragments characterized by their three-dimensional orientations and joint principal curvatures. Configural representation of three-dimensional shape could provide specific knowledge of object structure to support guidance of complex physical interactions and evaluation of object functionality and utility.

Subfields: Cognitive Neuroscience, Molecular Biology

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Citations in the corpus

Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “tion through leveraging findings in the area of static shape encoding in the neural pathway of the primate cortex [217].”
How does the brain solve visual object recognition? (2012, neutral): “Within the IT complex, crude retinotopy exists over the more posterior portion (pIT; Boussaoud et al., 1991; Yasuda et al., 2010), but retinotopy is not reported in the central and anterior regions (Felleman and Van Essen, 1991).”
Selectivity and tolerance (“invariance”) both increase as visual information propagates from cortical area V4 to IT (2010, background): “…elegant series of studies has explored tuning for parametrically defined contour shapes (Pasupathy and Connor, 1999; Brincat and Connor, 2004; Yamane et al., 2008) and found indications of increasingly complex contour encoding properties across different stages of the ventral visual stream…”
Deep Nets: What have They Ever Done for Vision? (2018, background): “The ventral stream of primates is very complex and there is evidence that it estimates the three-dimensional structure of objects and parts (Yamane et al., 2008), and relates to the classic theory of object recognition by component (Biederman, 1987) which diﬀers in many respects from standard Deep…”
Artificial Neural Networks for Neuroscientists: A Primer (2020, methodology): “Gradientfree methods such as genetic algorithms have been used to study complex tuning of biological neurons (Yamane et al., 2008).”
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence (2016, background): “A major impediment in creating such a model is the highly nonlinear and sparse nature of neural tuning properties in mid-and high-level visual areas 11–13 that is difficult to capture experimentally, and thus unknown.”
Compositional Convolutional Neural Networks: A Deep Architecture With Innate Robustness to Partial Occlusion (2020, background): “Compositionality is a fundamental aspect of human cognition [2, 28, 9, 3] that is also reflected in the hierarchical compositional structure of the ventral stream in visual cortex [34, 27, 21].”
Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action Recognition (2013, background): “To find the relationship between neuronal activity and 3D shape parameters, the authors in [11] showed with high confidence that the neural activity can be modeled as a non-linear function of the combination of various mathematical properties of the 3D shapes including medial-axis components such as”
Learning Intermediate-Level Representations of Form and Motion from Natural Movies (2012, background): “Invariances are learned as the result of factoring apart the temporally stable and dynamic components embedded in the early feature representation.”
Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet (2012, background): “Finally, in the top layer, neurons respond to combinations of what is represented in the preceding intermediate layer, and thus provide evidence about objects in a position (and scale and even view) invariant way.”