Transformation of shape information in the ventral pathway.

By C. Connor, Scott L. Brincat, Anitha Pasupathy. 2007

200 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “, 2013) seem compatible with insights about the visual pathway in the primate cerebral cortex, which has been studied for many decades (e.g., Hubel and Wiesel, 1968; Perrett et al., 1982; Desimone et al., 1984; Felleman and Van Essen, 1991; Perrett et al., 1992; Kobatake and Tanaka, 1994; Logothetis”
Figure–ground organization and the emergence of proto-objects in the visual cortex (2015, background): “Examining the ventral stream from V1 to anterior IT cortex, one finds increasing size of receptive fields, increasing shape selectivity and increasing position tolerance (Zeki, 1978; Desimone et al., 1985; Tanaka et al., 1991; Connor et al., 2007; Rust and DiCarlo, 2010).”
Performance-optimized hierarchical models predict neural responses in higher visual cortex (2014, methodology): “For example, neurons in the lowest area, V1, are well described by Gabor-like edge detectors that extract rough object outlines (10), although the V1 population does not show robust tolerance to complex image transformations (9).”
A neural code for three-dimensional object shape in macaque inferotemporal cortex (2008, background): “Tuning for nonzero curvature reflects the coding advantage of higher-order derivative”
Physiologically Inspired Model for the Visual Recognition of Transitive Hand Actions (2013, unknown): “object-centered representations in the ventral stream (Jellema and Perrett, 2006; Connor et al., 2007).”