Natural Image Statistics and Neural Representation

Simoncelli, Eero P.; Olshausen, Bruno A.

doi:10.1146/annurev.neuro.24.1.1193

Cited by 2,081 publications

(1,689 citation statements)

References 73 publications

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“…These resolve the ill-posed problems that confound recognition based on purely forward architectures. It has long been assumed that sensory units adapt to the statistical properties of the signals to which they are exposed (see Simoncelli and Olshausen 2001, for review). The Bayesian framework for perceptual inference has its origins in Helmholtz's notion of perception as unconscious inference.…”

Section: Perception and Sensationmentioning

confidence: 99%

Free-energy and the brain

2007

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If one formulates Helmholtz's ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organisation and responses. In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception, respectively, and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system's state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure.

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Section: Perception and Sensationmentioning

confidence: 99%

Free-energy and the brain

2007

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“…Since such signals often have widely different attributes (Simoncelli and Olshausen, 2001), sensory systems must have developed strategies to efficiently process them. One mechanism used to encode specific aspects of the signal involves multiple neuronal representations ('maps') that differentially encode the same stimulus, as found in the auditory and olfactory systems.…”

Section: Multiple Mapsmentioning

confidence: 99%

Ionic and neuromodulatory regulation of burst discharge controls frequency tuning

Mehaffey

Ellis

Krahe

et al. 2008

Journal of Physiology-Paris

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Sensory neurons encode natural stimuli by changes in firing rate or by generating specific firing patterns, such as bursts. Many neural computations rely on the fact that neurons can be tuned to specific stimulus frequencies. It is thus important to understand the mechanisms underlying frequency tuning. In the electrosensory system of the weakly electric fish, Apteronotus leptorhynchus, the primary processing of behaviourally relevant sensory signals occurs in pyramidal neurons of the electrosensory lateral line lobe (ELL). These cells encode low frequency prey stimuli with bursts of spikes and high frequency communication signals with single spikes. We describe here how bursting in pyramidal neurons can be regulated by intrinsic conductances in a cell subtype specific fashion across the sensory maps found within the ELL, thereby regulating their frequency tuning. Further, the neuromodulatory regulation of such conductances within individual cells and the consequences to frequency tuning are highlighted. Such alterations in the tuning of the pyramidal neurons may allow weakly electric fish to preferentially select for certain stimuli under various behaviourally relevant circumstances.

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“…As the input to MDS or PCA, we used a 128 ϫ 128 correlation matrix, each element of which represented the correlation coefficient of the responses of the V2 cells (averaged across trials, but not normalized) to a given pair of the 128 stimuli. We used the correlation coefficient rather than the coefficient of covariance, because the former is scale invariant, measures response similarities independent of the absolute firing rates, and is commonly used for this purpose (see Young & Yamane, 1992;Oja, 1995;Simoncelli & Olshausen, 2001).…”

Section: Analysis Of Response Correlationsmentioning

confidence: 99%

“…This suggests that V2 cells sample the space of grating and contour stimuli widely and are not specialized for analyzing a narrow subset of shape characteristics of contours and0or textures within our stimulus set. This apparent diversity in analysis strategies may reflect the diversity of perceptually relevant shape cues in natural images (Cho et al, 2000;Geisler et al, 2001;Sigman et al, 2001; also see Field, 1987;Simoncelli & Olshausen, 2001).…”

Section: Role Of V2 Cells In Shape Analysis and Image Segmentationmentioning

confidence: 99%

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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Natural Image Statistics and Neural Representation

Cited by 2,081 publications

References 73 publications

Free-energy and the brain

Free-energy and the brain

Ionic and neuromodulatory regulation of burst discharge controls frequency tuning

Strategies of shape representation in macaque visual area V2

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