2001
DOI: 10.1038/90547
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Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns

Abstract: Intrinsic signal imaging from inferotemporal (IT) cortex, a visual area essential for object perception and recognition, revealed that visually presented objects activated patches in a distributed manner. When visual features of these objects were partially removed, the simplified stimuli activated only a subset of the patches elicited by the originals. This result, in conjunction with extracellular recording, suggests that an object is represented by a combination of cortical columns, each of which represents… Show more

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Cited by 337 publications
(262 citation statements)
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“…IT neurons are able to learn new arbitrary shapes (Logothetis et al, 1995), and arbitrary pairings of stimuli (Sakai and Miyashita, 1991), which might be an analogue of learning letters and of linking arbitrarily related upper-and lowercase letters. Likewise, the binding of letters into words might be related to the representation of complex objects in IT cortex through the coactivation of neurons tuned to their elementary parts (Tsunoda et al, 2001). Binding of letters may also be based on more holistic coding, as suggested by the recent demonstration that single IT neurons develop selectivity for learned complex shapes, above and beyond what could be expected from the additive influence of their component parts (Baker et al, 2002).…”
Section: Resultsmentioning
confidence: 99%
“…IT neurons are able to learn new arbitrary shapes (Logothetis et al, 1995), and arbitrary pairings of stimuli (Sakai and Miyashita, 1991), which might be an analogue of learning letters and of linking arbitrarily related upper-and lowercase letters. Likewise, the binding of letters into words might be related to the representation of complex objects in IT cortex through the coactivation of neurons tuned to their elementary parts (Tsunoda et al, 2001). Binding of letters may also be based on more holistic coding, as suggested by the recent demonstration that single IT neurons develop selectivity for learned complex shapes, above and beyond what could be expected from the additive influence of their component parts (Baker et al, 2002).…”
Section: Resultsmentioning
confidence: 99%
“…They also exhibit learning phenomena, even for essentially arbitrary figures [41]. Furthermore, combined optical imaging and single-cell recordings suggest that the IT cortex is composed of a mosaic of neuronal patches that respond to elementary shapes, whose combination can encode arbitrary objects [39,42]. Remarkably, some of those primitive shapes closely resemble printed or handwritten characters, such as T junctions, a figure-of-eight, a star, and so forth [39].…”
Section: V4 V4mentioning
confidence: 99%
“…In the low-level visual system, neural selectivity varies depending on the physical properties such as arcs, intersecting lines, and non-Cartesian gratings (Hegde & Van Essen, 2000). In the highly specialized object-perception domain, neurons are tuned to a dictionary of features at different levels of complexity (Tsunoda, Yamane, Nishizaki, & Tanifuji, 2001). After specific neurons repeatedly and selectively respond to given objects, these objects are represented by their similarity to stored views of prototypes created (Palmeri, Wong, & Gauthier, 2004).…”
mentioning
confidence: 99%