Sparse Coding and Decorrelation in Primary Visual Cortex During Natural Vision

Vinje, William E.; Gallant, Jack L.

doi:10.1126/science.287.5456.1273

Cited by 1,139 publications

(1,105 citation statements)

References 25 publications

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“…5B). The results were qualitatively similar when sparseness (see Rolls & Tovee, 1995;Vinje & Gallant, 2000;Friedrich & Laurent, 2001), instead of SSI, was taken as a measure of the peakedness of the response profile (data not shown).…”

Section: Individual V2 Cells Carry Information About Many Shape Charamentioning

confidence: 58%

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: Individual V2 Cells Carry Information About Many Shape Charamentioning

confidence: 58%

Strategies of shape representation in macaque visual area V2

Hegdé

Essen

2003

Vis Neurosci

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“…To meet the challenge we must modify both our experimental designs and our methods for analyzing the responses to these much more complicated inputs. Recent examples of laboratory based approaches to the problem of natural stimulation are studies of bullfrog auditory neurons responding to synthesized frog calls (Rieke et al , 1995), insect olfactory neurons responding to odour plumes (Vickers et al , 2001), cat LGN cells responding to movies (Dan et al , 1996, Stanley et al , 1999, primate visual cortical cells during free viewing of natural images (Gallant et al , 1998, Vinje andGallant, 2000), auditory neurons in song birds stimulated by song and song-like signals (Theunissen and Doupe, 1998, Theunissen et al , 2000, the responses in cat auditory cortex to signals with naturalistic statistical properties (Rotman et al , 1999), and motion sensitive cells in the fly Egelhaaf, 2001, de Ruyter van Steveninck et al , 2001). In each case compromises are struck between well controlled stimuli with understandable statistical properties and the fully natural case.…”

Section: Introductionmentioning

confidence: 99%

Neural coding of naturalistic motion stimuli

Lewen¹,

Bialek²,

Steveninck³

2001

Network: Computation in Neural Systems

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Abstract.We study a wide field motion sensitive neuron in the visual system of the blowfly Calliphora vicina. By rotating the fly on a stepper motor outside in a wooded area, and along an angular motion trajectory representative of natural flight, we stimulate the fly's visual system with input that approaches the natural situation. The neural response is analyzed in the framework of information theory, using methods that are free from assumptions. We demonstrate that information about the motion trajectory increases as the light level increases over a natural range. This indicates that the fly's brain utilizes the increase in photon flux to extract more information from the photoreceptor array, suggesting that imprecision in neural signals is dominated by photon shot noise in the physical input, rather than by noise generated within the nervous system itself.

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“…Indeed, the frequency of H3-patterns in relation to a behavior is in the range of the rare single spike activity of many neuronal subpopulations, including numerous cortico-cortical, cortico-thalamic, and cortico-striatal projecting populations in mammals [8,9,10,56,60,66,67,68,71], as well as a group of neurons that connect two song-production nuclei in song birds [30]. Although in most of these cases the functional role of such sparse firing is yet to be determined, it seems to be computationally efficient in visual processing [73] and in songs learning in songbirds [24].…”

Section: Discussionmentioning

confidence: 99%

Precise rhythmicity in activity of neocortical, thalamic and brain stem neurons in behaving cats and rabbits

Dunin-Barkowski

Sirota

Lovering

et al. 2006

Behavioural Brain Research

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Rhythmic discharges of neurons are believed to be involved in information processing in both sensory and motor systems. However their fine structure and functional role need further elucidation. We employed a pattern-based approach to search for episodes of precisely rhythmic activity of single neurons recorded in different brain structures in behaving cats and rabbits. We defined discharge patterns using an algorithmic description, which is different from the previously suggested template methods. We detected episodes of precisely rhythmic discharges, specifically, triads of constant (precision ± 2.5%) inter-spike intervals in the 10-70 ms range. In 54% (67/125) of neurons tested, these patterns could not be explained by random occurrences or by steady or slowly changing input. Rhythmic patterns occurred at a wide range of inter-spike intervals, and were imbedded in nonrhythmic activity. In many neurons, timing of these precisely rhythmic patterns was related to different locomotion tasks or to respiration.

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Sparse Coding and Decorrelation in Primary Visual Cortex During Natural Vision

Cited by 1,139 publications

References 25 publications

Strategies of shape representation in macaque visual area V2

Strategies of shape representation in macaque visual area V2

Neural coding of naturalistic motion stimuli

Precise rhythmicity in activity of neocortical, thalamic and brain stem neurons in behaving cats and rabbits

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