Receptive-field dynamics in the central visual pathways

DeAngelis, Gregory C.; Ohzawa, Izumi; Freeman, Ray

doi:10.1016/0166-2236(95)94496-r

Cited by 406 publications

(430 citation statements)

References 51 publications

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“…Common for all these scale-spaces is that they are generated from a parameterized family of continuous, discrete or semi-discrete diffusion processes, implying that they can be computed by local connections in a distributed computational architecture. Notably the receptive field profiles generated by this scale-space concept have high qualitative similarity to receptive fields profiles recorded from biological vision (DeAngelis et al 1995, Valois et al 2000 in analogy with previously established relations between spatial receptive fields and Gaussian derivative operators (Young 1985, Young 1987.…”

Section: Introductionmentioning

confidence: 70%

“…Concerning motion selectivity, (DeAngelis et al 1995) report that most cortical neurons are quite sensitive to stimulus velocity, and the speed tuning is more narrow than for LGN cells. Simple cells with inseparable receptive fields have directional preference, while cells with space-time separable receptive fields do not.…”

Section: Relations To Biological Visionmentioning

confidence: 99%

“…In a recent review, (DeAngelis et al 1995) present an overview of recent results on temporal response properties of receptive fields in the central visual pathways. Foremost, the authors point out the limitations of defining receptive fields in the spatial domain only, and emphasize the need to characterize receptive fields in the joint space-time domain, in order to describe how a neuron processes the visual image.…”

Section: Relations To Biological Visionmentioning

confidence: 99%

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Linear spatio-temporal scale-space

Lindeberg

1997

Scale-Space Theory in Computer Vision

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This article shows how a linear scale-space formulation previously expressed for spatial domains extends to spatio-temporal data. Starting from the main assumptions that: (i) the scale-space should be generated by convolution with a semi-group of filter kernels and that (ii) local extrema must not be enhanced when the scale parameter increases, a complete taxonomy is given of the linear scale-space concepts that satisfy these conditions on spatial, temporal and spatio-temporal domains, including the cases with continuous as well as discrete data.Key aspects captured by this theory include that: (i) time-causal scale-space kernels must not extend into the future, (ii) filter shapes can be tuned from specific context information, permitting mechanisms such local shifting, shape adaptation and velocity adaptation, all expressed in terms of local diffusion operations.Receptive field profiles generated by the proposed theory show high qualitative similarities to receptive field profiles recorded from biological vision.£ Earlier versions of this manuscript have been presented at

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Section: Introductionmentioning

confidence: 70%

Section: Relations To Biological Visionmentioning

confidence: 99%

Section: Relations To Biological Visionmentioning

confidence: 99%

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Linear spatio-temporal scale-space

Lindeberg

1997

Scale-Space Theory in Computer Vision

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“…Two-dimensional spatial sensitivity distributions of ERs were quantitatively measured in 118 RGCs (Jones & Palmer, 1987;Shapley et al, 1991;DeAngelis et al, 1995), and then their directional preference was examined ( ,5B), indicating that both the leading off-on and the trailing on-off transitions are effective. Off DS RGCs showed single-peak responses to moving stimuli.…”

Section: Resultsmentioning

confidence: 99%

“…In order to examine this possibility, careful measurements of local directional preferences must be performed. RF structures of DS neurons have been extensively studied in the mammalian primary visual cortex (see Emerson et al, 1987;Shapley et al, 1991;Gaska et al, 1994;DeAngelis et al, 1995). The 2nd order kernel measurement used for monkey V1 complex cells (Gaska et al, 1994) would more precisely describe spatiotemporal structures of RFs of the quail DS RGCs.…”

Section: Discussionmentioning

confidence: 99%

Computation of motion direction by quail retinal ganglion cells that have a nonconcentric receptive field

2000

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One type of retinal ganglion cells prefers object motion in a particular direction. Neuronal mechanisms for the computation of motion direction are still unknown. We quantitatively mapped excitatory and inhibitory regions of receptive fields for directionally selective retinal ganglion cells in the Japanese quail, and found that the inhibitory regions are displaced about 1-3 deg. toward the side where the null sweep starts, relative to the excitatory regions. Directional selectivity thus results from delayed transient suppression exerted by the nonconcentrically-arranged inhibitory regions, and not by local directional inhibition as hypothesized by Barlow and Levick (1965).

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Scale‐Space

Lindeberg¹

2008

Wiley Encyclopedia of Computer Science and Engineering

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Scale‐space theory is a framework for multiscale image representation, which has been developed by the computer vision community with complementary motivations from physics and biologic vision. The idea is to handle the multiscale nature of real‐world objects, which implies that object may be perceived in different ways depending on the scale of observation. If one aims to develop automatic algorithms for interpreting images of unknown scenes, no way exists to know a priori what scales are relevant. Hence, the only reasonable approach is to consider representations at all scales simultaneously. From axiomatic derivations is has been shown that given the requirement that coarse‐scale representations should correspond to true simplifications of fine scale structures, convolution with Gaussian kernels and Gaussian derivatives is singled out as a canonical class of image operators forthe earliest stages of visual processing. These image operators can be used as basis to solve a large variety of visual tasks, including feature detection, feature classification, stereo matching, motion descriptors, shape cues, and image‐based recognition. By complementing scale‐space representation with a module for automatic scale selection based on the maximization of normalized derivatives over scales, early visual modules can be made scale invariant. In this way, visual modules canadapt automatically to the unknown scale variations that may occur because of objects and substructures of varying physical size as well as objects with varying distances to the camera. An interesting similarity to biologic vision is that the scale‐space operators resemble closely receptive field profiles registered in neurophysiologic studies of the mammalian retina and visual cortex.

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Receptive-field dynamics in the central visual pathways

Cited by 406 publications

References 51 publications

Linear spatio-temporal scale-space

Linear spatio-temporal scale-space

Computation of motion direction by quail retinal ganglion cells that have a nonconcentric receptive field

Scale‐Space

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