Dimensionality of object representations in monkey inferotemporal cortex

Sidney R. Lehky, Roozbeh Kiani, Hossein Esteky, Keiji Tanaka

Research output: Contribution to journalArticle

Abstract

We have calculated the intrinsic dimensionality of visual object representations in anterior inferotemporal (AIT) cortex, based on responses of a large sample of cells stimulated with photographs of diverse objects. Because dimensionality was dependent on data set size, we determined asymptotic dimensionality as both the number of neurons and number of stimulus image approached infinity.Our final dimensionality estimate was 93 (SD: ± 11), indicating that there is basis set of approximately 100 independent features that characterize the dimensions of neural object space.We believe this is the first estimate of the dimensionality of neural visual representations based on single-cell neurophysiological data. The dimensionality of AIT object representations was much lower than the dimensionality of the stimuli. We suggest that there may be a gradual reduction in the dimensionality of object representations in neural populations going from retina to inferotemporal cortex as receptive fields become increasingly complex.

Original languageEnglish (US)
Pages (from-to)2135-2162
Number of pages28
JournalNeural Computation
Volume26
Issue number10
DOIs
StatePublished - Oct 1 2014

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Haplorhini
Retina
Neurons
Population
Cortex
Object Representation
Monkey
Cells
Stimulus
Datasets
Intrinsic
Visual Representation
Neuron
Infinity

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Arts and Humanities (miscellaneous)

Cite this

Dimensionality of object representations in monkey inferotemporal cortex. / Lehky, Sidney R.; Kiani, Roozbeh; Esteky, Hossein; Tanaka, Keiji.

In: Neural Computation, Vol. 26, No. 10, 01.10.2014, p. 2135-2162.

Research output: Contribution to journalArticle

Lehky, Sidney R. ; Kiani, Roozbeh ; Esteky, Hossein ; Tanaka, Keiji. / Dimensionality of object representations in monkey inferotemporal cortex. In: Neural Computation. 2014 ; Vol. 26, No. 10. pp. 2135-2162.
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