The Manifold Ways of Perception

Seung, H. Sebastian; Lee, Daniel D.

doi:10.1126/science.290.5500.2268

Cited by 729 publications

(302 citation statements)

References 7 publications

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“…Young & Yamane, 1992). In addition, analyzing the extent to which each principal component accounts for the overall response variation provides an objective basis for assessing the dimensionality of the neural representation (Young & Yamane, 1992;Field, 1995;Seung & Lee, 2000). Our analyses indicate that a relatively small number of principal components in V2 account for a high proportion of the response variance associated with complex shapes presented within the classical receptive field.…”

Section: Introductionmentioning

confidence: 81%

Strategies of shape representation in macaque visual area V2

Hegdé

Essen

2003

Vis Neurosci

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Contours and surface textures provide powerful cues used in image segmentation and the analysis of object shape.To learn more about how the visual system extracts and represents these visual cues, we studied the responses of V2 neurons in awake, fixating monkeys to complex contour stimuli (angles, intersections, arcs, and circles) and texture patterns such as non-Cartesian gratings, along with conventional bars and sinusoidal gratings. Substantial proportions of V2 cells conveyed information about many contour and texture characteristics associated with our stimuli, including shape, size, orientation, and spatial frequency. However, the cells differed considerably in terms of their degree of selectivity for the various stimulus characteristics. On average, V2 cells responded better to grating stimuli but were more selective for contour stimuli. Metric multidimensional scaling and principal components analysis showed that, as a population, V2 cells show strong correlations in how they respond to different stimulus types. The first two and five principal components accounted for 69% and 85% of the overall response variation, respectively, suggesting that the response correlations simplified the population representation of shape information with relatively little loss of information. Moreover, smaller random subsets of the population carried response correlation patterns very similar to the population as a whole, indicating that the response correlations were a widespread property of V2 cells. Thus, V2 cells extract information about a number of higher order shape cues related to contours and surface textures and about similarities among many of these shape cues. This may reflect an efficient strategy of representing cues for image segmentation and object shape using finite neuronal resources.

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Section: Introductionmentioning

confidence: 81%

Strategies of shape representation in macaque visual area V2

Hegdé

Essen

2003

Vis Neurosci

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“…The manifold concept in modern physics may help our understanding of the compression of perceptual spacetime during saccades. During fixation, the retinal input could be of a continuous spatial manifold within which visible objects are embedded 35,36) . However, if this spatial manifold is tilted intrasaccadically (Fig.…”

Section: Relativity In Visual Spacetimementioning

confidence: 99%

The old-but-new theories about human voluntary motor control

Suzuki

Wasaka

2012

JPFSM

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For more than a century, neuroscientists have debated the problem of how selfgenerated voluntary motor actions are executed without interference from sensory information elicited by the movements. In the 1950's, two theories were proposed: the corollary discharge theory by Sperry 1) and the efference copy theory by von Holst and Mittelstaedt 2) . They are essentially identical in proposing the necessity of specific neuronal mechanisms to suppress undesirable percepts or reflexes, both of which might disturb ongoing movements. However, this raises a simple question as to whether the control system always ignores sensory information whenever the intended movements are in progress. Here, we review recent findings and currently debated problems in corollary discharge theory.

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“…Basically, as in [9] and [11,10], we calculate a weighted sum of evidence bodies for the representative value for each hypothesis from all the evidence. However, it is natural to assume that evidence bodies make a structure as in other data sets (see manifold learning methods [14][15][16]) and this structure can be described by a probability distribution. Then, we can use the probability of evidence on the distribution for different weights.…”

Section: Probabilistic Combination Rulementioning

confidence: 99%

Probabilistic Combination of Multiple Evidence

Choi

Katake

Choi

et al. 2009

Neural Information Processing

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Abstract. In pattern recognition systems, data fusion is an important issue and evidence theory is one such method that has been successful. Many researchers have proposed different rules for evidence theory, and recently, a variety of averaging rules emerged that are better than others. In these methods, the key issue becomes how to give the weights to the multiple contributing factors, in order to calculate the average. To get better weights for the multiple bodies of evidence, we propose the use of structural information of the evidence. The bodies of evidence lie on a certain informational structure which can be described by a probability distribution and the probability of each evidence can serve as a weight for the evidence. Our experimental results show that our method outperforms other previous methods.

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The Manifold Ways of Perception

Cited by 729 publications

References 7 publications

Strategies of shape representation in macaque visual area V2

Strategies of shape representation in macaque visual area V2

The old-but-new theories about human voluntary motor control

Probabilistic Combination of Multiple Evidence

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