Spatial frequency selectivity of cells in macaque visual cortex

Valois, Russell L. De; Albrecht, Duane G.; Thorell, Lisa G.

doi:10.1016/0042-6989(82)90113-4

Cited by 1,357 publications

(981 citation statements)

References 28 publications

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“…Importantly, this duration after-effect only occurred when the adaptation duration was close to the tested duration, disappearing when the difference between the two stimuli exceeded $1.5 octaves. As such, adaptation to duration resulted in a pattern of repulsion similar to that observed for other visual properties such as orientation, spatial frequency, and temporal frequency, which are known to be processed by groups of neurons that show feature selectivity (De Valois, 1977;De Valois, Albrecht, & Thorell, 1982;Smith, 1971).…”

Section: Introductionmentioning

confidence: 81%

An investigation of the spatial selectivity of the duration after-effect

et al. 2017

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a b s t r a c tAdaptation to the duration of a visual stimulus causes the perceived duration of a subsequently presented stimulus with a slightly different duration to be skewed away from the adapted duration. This pattern of repulsion following adaptation is similar to that observed for other visual properties, such as orientation, and is considered evidence for the involvement of duration-selective mechanisms in duration encoding. Here, we investigated whether the encoding of duration -by duration-selective mechanisms -occurs early on in the visual processing hierarchy. To this end, we investigated the spatial specificity of the duration after-effect in two experiments. We measured the duration after-effect at adapter-test distances ranging between 0 and 15°of visual angle and for within-and between-hemifield presentations. We replicated the duration after-effect: the test stimulus was perceived to have a longer duration following adaptation to a shorter duration, and a shorter duration following adaptation to a longer duration. Importantly, this duration after-effect occurred at all measured distances, with no evidence for a decrease in the magnitude of the after-effect at larger distances or across hemifields. This shows that adaptation to duration does not result from adaptation occurring early on in the visual processing hierarchy. Instead, it seems likely that duration information is a high-level stimulus property that is encoded later on in the visual processing hierarchy.

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Section: Introductionmentioning

confidence: 81%

An investigation of the spatial selectivity of the duration after-effect

et al. 2017

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“…They extract simple features related to those observed in early visual pre-processing stages of biological systems (e.g., De Valois et al, 1982;Jones and Palmer, 1987).…”

Section: Potential Benefits Of Ul For Slmentioning

confidence: 99%

Deep learning in neural networks: An overview

2015

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In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.LATEX source: http://www.idsia.ch/˜juergen/DeepLearning8Oct2014.tex Complete BIBTEX file (888 kB): http://www.idsia.ch/˜juergen/deep.bib Preface This is the preprint of an invited Deep Learning (DL) overview. One of its goals is to assign credit to those who contributed to the present state of the art. I acknowledge the limitations of attempting to achieve this goal. The DL research community itself may be viewed as a continually evolving, deep network of scientists who have influenced each other in complex ways. Starting from recent DL results, I tried to trace back the origins of relevant ideas through the past half century and beyond, sometimes using "local search" to follow citations of citations backwards in time. Since not all DL publications properly acknowledge earlier relevant work, additional global search strategies were employed, aided by consulting numerous neural network experts. As a result, the present preprint mostly consists of references. Nevertheless, through an expert selection bias I may have missed important work. A related bias was surely introduced by my special familiarity with the work of my own DL research group in the past quarter-century. For these reasons, this work should be viewed as merely a snapshot of an ongoing credit assignment process. To help improve it, please do not hesitate to send corrections and suggestions to juergen@idsia.ch.

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“…The ratio σ/λ determines the spatial frequency bandwidth 3 Valois et al (1982) proposed that this spread is due to the gradual sharpening of the orientation and spatial frequency bandwidth at consecutive stages of the visual system and that the input to higher processing stages is provided by the more narrowly tuned simple cells with half-response spatial frequency bandwidth of approximately one octave. This value of the half-response spatial frequency bandwidth corresponds to the value 0.56 of the ratio σ/λ which is used in the simulations of this study.…”

Section: Preliminary: Computational Models Of Simple and Complex Cellsmentioning

confidence: 99%

Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells

Petkov

Kruizinga

1997

Biological Cybernetics

151

126

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Abstract. Computational models of periodic-and aperiodicpattern selective cells, also called grating and bar cells, respectively, are proposed. Grating cells are found in areas V1 and V2 of the visual cortex of monkeys and respond strongly to bar gratings of a given orientation and periodicity but very weakly or not at all to single bars. This nonlinear behaviour, which is quite different from the spatial frequency filtering behaviour exhibited by the other types of orientation-selective neurons such as the simple cells, is incorporated in the proposed computational model by using an AND-type non-linearity to combine the responses of simple cells with symmetric receptive field profiles and opposite polarities.

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Spatial frequency selectivity of cells in macaque visual cortex

Cited by 1,357 publications

References 28 publications

An investigation of the spatial selectivity of the duration after-effect

An investigation of the spatial selectivity of the duration after-effect

Deep learning in neural networks: An overview

Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells

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