scholarly journals Robust representation of natural images by sparse and variable population of active neurons in visual cortex

2018 ◽  
Author(s):  
Takashi Yoshida ◽  
Kenichi Ohki
Keyword(s):  
Visual Cortex ◽  
Receptive Fields ◽  
Natural Images ◽  
Natural Image ◽  
Natural Scenes ◽  
Variable Population ◽  
Reliable Representation ◽  
Neuronal Patterns ◽  
Representation Scheme ◽  
Optimal Decoding

AbstractNatural scenes sparsely activate neurons in the primary visual cortex (V1). However, how sparsely active neurons robustly represent natural images and how the information is optimally decoded from the representation have not been revealed. We reconstructed natural images from V1 activity in anaesthetized and awake mice. A single natural image was linearly decodable from a surprisingly small number of highly responsive neurons, and an additional use of remaining neurons even degraded the decoding. This representation was achieved by diverse receptive fields (RFs) of the small number of highly responsive neurons. Furthermore, these neurons reliably represented the image across trials, regardless of trial-to-trial response variability. The reliable representation was supported by multiple neurons with overlapping RFs. Based on our results, the diverse, partially overlapping RFs ensure sparse and reliable representation. We propose a new representation scheme in which information is reliably represented while the representing neuronal patterns change across trials and that collecting only the activity of highly responsive neurons is an optimal decoding strategy for the downstream neurons

Virtual evolution for visual search in natural images results in behavioral receptive fields with inhibitory surrounds

2009 ◽  
Vol 26 (1) ◽  
pp. 93-108 ◽  
Author(s):  
SHENG ZHANG ◽  
CRAIG K. ABBEY ◽  
MIGUEL P. ECKSTEIN
Keyword(s):  
Receptive Field ◽  
White Noise ◽  
Receptive Fields ◽  
Image Data ◽  
Optimization Method ◽  
Natural Images ◽  
Natural Image ◽  
Natural Scenes ◽  
Linear Combinations ◽  
Data Set

AbstractThe neural mechanisms driving perception and saccades during search use information about the target but are also based on an inhibitory surround not present in the target luminance profile (e.g., Eckstein et al., 2007). Here, we ask whether these inhibitory surrounds might reflect a strategy that the brain has adapted to optimize the search for targets in natural scenes. To test this hypothesis, we sought to estimate the best linear template (behavioral receptive field), built from linear combinations of Gabor channels representing V1 simple cells in search for an additive Gaussian target embedded in natural images. Statistically nonstationary and non-Gaussian properties of natural scenes preclude calculation of the best linear template from analytic expressions and require an iterative optimization method such as a virtual evolution via a genetic algorithm. Evolved linear receptive fields built from linear combinations of Gabor functions include substantial inhibitory surround, larger than those found in humans performing target search in white noise. The inhibitory surrounds were robust to changes in the contrast of the signal, generalized to a larger calibrated natural image data set, and tasks in which the signal occluded other objects in the image. We show that channel nonlinearities can have strong effects on the observed linear behavioral receptive field but preserve the inhibitory surrounds. Together, the results suggest that the apparent suboptimality of inhibitory surrounds in human behavioral receptive fields when searching for a target in white noise might reflect a strategy to optimize detection of signals in natural scenes. Finally, we contend that optimized linear detection of spatially compact signals in natural images might be a new possible hypothesis, distinct from decorrelation of visual input and sparse representations (e.g., Graham et al., 2006), to explain the evolution of center–surround organization of receptive fields in early vision.

Natural-Image Discrimination in the Periphery: The Importance of Phase and Amplitude Information

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 85-85
Author(s):  
G M Kennedy ◽  
D J Tolhurst
Keyword(s):  
Phase Change ◽  
Scaling Factor ◽  
Natural Images ◽  
Natural Image ◽  
Rapid Decline ◽  
Natural Scenes ◽  
Phase Identification ◽  
Phase Information ◽  
Black And White ◽  
Sinusoidal Gratings

Previous studies with simplified stimuli such as combinations of sinusoidal gratings have revealed phase identification losses in the periphery that are not eliminated by a scaling factor. How do these phase processing problems influence our ability to discriminate natural images in the periphery? In this study the ability of an observer to identify the ‘odd-image-out’ when there is either an amplitude-only, phase-only, or amplitude and phase change in one out of three stimuli is compared. Pairs of Fourier-manipulated black-and-white digitised photographs of natural images were used and phase and amplitude spectral exchanges of varying proportions were made between two different images. Measurements were made to determine the smallest phase change needed in order for the observer to reliably discriminate the manipulated image, compared to two reference stimuli, at eccentricities of 0°, 2.5°, 5°, and 10°. This was compared to discrimination thresholds found when amplitude and phase, and amplitude alone were exchanged. The ability to discriminate images on the basis of phase information alone did fall off quickly with eccentricity (comparable to phase and amplitude discriminations). However, there was a much more rapid decline in amplitude-only discrimination. It appears that phase information in natural scenes remains a relatively more important visual cue in the periphery than amplitude.

scholarly journals Systematic differences between visually-relevant global and local image statistics of brain MRI and natural scenes

2018 ◽  
Author(s):  
Yueyang Xu ◽  
Ashish Raj ◽  
Jonathan Victor ◽  
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Image Formation ◽  
Natural Images ◽  
Natural Image ◽  
Natural Scenes ◽  
Image Statistics ◽  
Processing Technologies ◽  
Global And Local ◽  
Local Image

AbstractAn important heuristic in developing image processing technologies is to mimic the computational strategies used by humans. Relevant to this, recent studies have shown that the human brain’s processing strategy is closely matched to the characteristics of natural scenes, both in terms of global and local image statistics. However, structural MRI images and natural scenes have fundamental differences: the former are two-dimensional sections through a volume, the latter are projections. MRI image formation is also radically different from natural image formation, involving acquisition in Fourier space, followed by several filtering and processing steps that all have the potential to alter image statistics. As a consequence, aspects of the human visual system that are finely-tuned to processing natural scenes may not be equally well-suited for MRI images, and identification of the differences between MRI images and natural scenes may lead to improved machine analysis of MRI.With these considerations in mind, we analyzed spectra and local image statistics of MRI images in several databases including T1 and FLAIR sequence types and of simulated MRI images,[1]–[6] and compared this analysis to a parallel analysis of natural images[7] and visual sensitivity[7][8]. We found substantial differences between the statistical features of MRI images and natural images. Power spectra of MRI images had a steeper slope than that of natural images, indicating a lack of scale invariance. Independent of this, local image statistics of MRI and natural images differed: compared to natural images, MRI images had smaller variations in their local two-point statistics and larger variations in their local three-point statistics – to which the human visual system is relatively insensitive. Our findings were consistent across MRI databases and simulated MRI images, suggesting that they result from brain geometry at the scale of MRI resolution, rather than characteristics of specific imaging and reconstruction methods.

scholarly journals Orientation tuning depends on spatial frequency in mouse visual cortex

2016 ◽  
Author(s):  
Inbal Ayzenshtat ◽  
Jesse Jackson ◽  
Rafael Yuste
Keyword(s):  
Visual Cortex ◽  
Spatial Frequency ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Orientation Selectivity ◽  
Natural Scenes ◽  
Response Properties ◽  
Layer 2 ◽  
Mouse Visual Cortex

AbstractThe response properties of neurons to sensory stimuli have been used to identify their receptive fields and functionally map sensory systems. In primary visual cortex, most neurons are selective to a particular orientation and spatial frequency of the visual stimulus. Using two-photon calcium imaging of neuronal populations from the primary visual cortex of mice, we have characterized the response properties of neurons to various orientations and spatial frequencies. Surprisingly, we found that the orientation selectivity of neurons actually depends on the spatial frequency of the stimulus. This dependence can be easily explained if one assumed spatially asymmetric Gabor-type receptive fields. We propose that receptive fields of neurons in layer 2/3 of visual cortex are indeed spatially asymmetric, and that this asymmetry could be used effectively by the visual system to encode natural scenes.Significance StatementIn this manuscript we demonstrate that the orientation selectivity of neurons in primary visual cortex of mouse is highly dependent on the stimulus SF. This dependence is realized quantitatively in a decrease in the selectivity strength of cells in non-optimum SF, and more importantly, it is also evident qualitatively in a shift in the preferred orientation of cells in non-optimum SF. We show that a receptive-field model of a 2D asymmetric Gabor, rather than a symmetric one, can explain this surprising observation. Therefore, we propose that the receptive fields of neurons in layer 2/3 of mouse visual cortex are spatially asymmetric and this asymmetry could be used effectively by the visual system to encode natural scenes.Highlights–Orientation selectivity is dependent on spatial frequency.–Asymmetric Gabor model can explain this dependence.

scholarly journals Active Object Recognition with a Space-Variant Retina

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Christopher Kanan
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Human Vision ◽  
Natural Images ◽  
Active Object ◽  
Color Structure ◽  
Opponent Color ◽  
Active Object Recognition ◽  
Space Variant

When independent component analysis (ICA) is applied to color natural images, the representation it learns has spatiochromatic properties similar to the responses of neurons in primary visual cortex. Existing models of ICA have only been applied to pixel patches. This does not take into account the space-variant nature of human vision. To address this, we use the space-variant log-polar transformation to acquire samples from color natural images, and then we apply ICA to the acquired samples. We analyze the spatiochromatic properties of the learned ICA filters. Qualitatively, the model matches the receptive field properties of neurons in primary visual cortex, including exhibiting the same opponent-color structure and a higher density of receptive fields in the foveal region compared to the periphery. We also adopt the “self-taught learning” paradigm from machine learning to assess the model’s efficacy at active object and face classification, and the model is competitive with the best approaches in computer vision.

scholarly journals Spatiochromatic Receptive Field Properties Derived from Information-Theoretic Analyses of Cone Mosaic Responses to Natural Scenes

2003 ◽  
Vol 15 (2) ◽  
pp. 397-417 ◽  
Author(s):  
Eizaburo Doi ◽  
Toshio Inui ◽  
Te-Won Lee ◽  
Thomas Wachtler ◽  
Terrence J. Sejnowski
Keyword(s):  
Visual System ◽  
Receptive Fields ◽  
Independent Component ◽  
Natural Images ◽  
Natural Scenes ◽  
Subsequent Stage ◽  
Type I ◽  
Sensory Signals ◽  
Cone Mosaic ◽  
Unsupervised Neural Network

Neurons in the early stages of processing in the primate visual system efficiently encode natural scenes. In previous studies of the chromatic properties of natural images, the inputs were sampled on a regular array, with complete color information at every location. However, in the retina cone photoreceptors with different spectral sensitivities are arranged in a mosaic. We used an unsupervised neural network model to analyze the statistical structure of retinal cone mosaic responses to calibrated color natural images. The second-order statistical dependencies derived from the covariance matrix of the sensory signals were removed in the first stage of processing. These decorrelating filters were similar to type I receptive fields in parvo- or konio-cellular LGN in both spatial and chromatic characteristics. In the subsequent stage, the decorrelated signals were linearly transformed to make the output as statistically independent as possible, using independent component analysis. The independent component filters showed luminance selectivity with simple-cell-like receptive fields, or had strong color selectivity with large, often double-opponent, receptive fields, both of which were found in the primary visual cortex (V1). These results show that the “form” and “color” channels of the early visual system can be derived from the statistics of sensory signals.

Learning Nonlinear Statistical Regularities in Natural Images by Modeling the Outer Product of Image Intensities

2014 ◽  
Vol 26 (4) ◽  
pp. 693-711 ◽  
Author(s):  
Peng Qi ◽  
Xiaolin Hu
Keyword(s):  
Visual Cortex ◽  
Layer Structure ◽  
Receptive Fields ◽  
Natural Images ◽  
Masking Effect ◽  
Covariance Model ◽  
Outer Product ◽  
Complex Cells ◽  
Statistical Regularities ◽  
Efficient Learning

It is well known that there exist nonlinear statistical regularities in natural images. Existing approaches for capturing such regularities always model the image intensities by assuming a parameterized distribution for the intensities and learn the parameters. In the letter, we propose to model the outer product of image intensities by assuming a gaussian distribution for it. A two-layer structure is presented, where the first layer is nonlinear and the second layer is linear. Trained on natural images, the first-layer bases resemble the receptive fields of simple cells in the primary visual cortex (V1), while the second-layer units exhibit some properties of the complex cells in V1, including phase invariance and masking effect. The model can be seen as an approximation of the covariance model proposed in Karklin and Lewicki ( 2009 ) but has more robust and efficient learning algorithms.

scholarly journals Categorically distinct types of receptive fields in early visual cortex

2016 ◽  
Vol 115 (5) ◽  
pp. 2556-2576 ◽  
Author(s):  
Vargha Talebi ◽  
Curtis L. Baker
Keyword(s):  
Visual Cortex ◽  
System Identification ◽  
Cortical Neurons ◽  
Temporal Dynamics ◽  
High Reliability ◽  
Receptive Fields ◽  
Direction Selectivity ◽  
Natural Image ◽  
Visual Responses ◽  
Neurophysiological Studies

In the visual cortex, distinct types of neurons have been identified based on cellular morphology, response to injected current, or expression of specific markers, but neurophysiological studies have revealed visual receptive field (RF) properties that appear to be on a continuum, with only two generally recognized classes: simple and complex. Most previous studies have characterized visual responses of neurons using stereotyped stimuli such as bars, gratings, or white noise and simple system identification approaches (e.g., reverse correlation). Here we estimate visual RF models of cortical neurons using visually rich natural image stimuli and regularized regression system identification methods and characterize their spatial tuning, temporal dynamics, spatiotemporal behavior, and spiking properties. We quantitatively demonstrate the existence of three functionally distinct categories of simple cells, distinguished by their degree of orientation selectivity (isotropic or oriented) and the nature of their output nonlinearity (expansive or compressive). In addition, these three types have differing average values of several other properties. Cells with nonoriented RFs tend to have smaller RFs, shorter response durations, no direction selectivity, and high reliability. Orientation-selective neurons with an expansive output nonlinearity have Gabor-like RFs, lower spontaneous activity and responsivity, and spiking responses with higher sparseness. Oriented RFs with a compressive nonlinearity are spatially nondescript and tend to show longer response latency. Our findings indicate multiple physiologically defined types of RFs beyond the simple/complex dichotomy, suggesting that cortical neurons may have more specialized functional roles rather than lying on a multidimensional continuum.

scholarly journals Role of Homeostasis in Learning Sparse Representations

2010 ◽  
Vol 22 (7) ◽  
pp. 1812-1836 ◽  
Author(s):  
Laurent U. Perrinet
Keyword(s):  
Sparse Coding ◽  
Hebbian Learning ◽  
Receptive Fields ◽  
Sparse Representations ◽  
Natural Images ◽  
Neural Representation ◽  
Natural Scenes ◽  
Efficient Coding ◽  
Sensory Data

Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the emergence of this response is to state that neural activity has to efficiently represent sensory data with respect to the statistics of natural scenes. Furthermore, it is believed that such an efficient coding is achieved using a competition across neurons so as to generate a sparse representation, that is, where a relatively small number of neurons are simultaneously active. Indeed, different models of sparse coding, coupled with Hebbian learning and homeostasis, have been proposed that successfully match the observed emergent response. However, the specific role of homeostasis in learning such sparse representations is still largely unknown. By quantitatively assessing the efficiency of the neural representation during learning, we derive a cooperative homeostasis mechanism that optimally tunes the competition between neurons within the sparse coding algorithm. We apply this homeostasis while learning small patches taken from natural images and compare its efficiency with state-of-the-art algorithms. Results show that while different sparse coding algorithms give similar coding results, the homeostasis provides an optimal balance for the representation of natural images within the population of neurons. Competition in sparse coding is optimized when it is fair. By contributing to optimizing statistical competition across neurons, homeostasis is crucial in providing a more efficient solution to the emergence of independent components.

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