scholarly journals The threshold of binocularity: natural image statistics explain the reduction of visual acuity in peripheral vision

2017 ◽  
Author(s):  
David W. Hunter ◽  
Paul B. Hibbard
Keyword(s):  
Visual Acuity ◽  
Visual Cortex ◽  
Visual Processing ◽  
Visual Information ◽  
Peripheral Vision ◽  
Visual Performance ◽  
Natural Image ◽  
Natural Scenes ◽  
Natural Image Statistics ◽  
Efficient Coding

AbstractVisual acuity is greatest in the centre of the visual field, peaking in the fovea and degrading significantly towards the periphery. The rate of decay of visual performance with eccentricity depends strongly on the stimuli and task used in measurement. While detailed measures of this decay have been made across a broad range of tasks, a comprehensive theoretical account of this phenomenon is lacking. We demonstrate that the decay in visual performance can be attributed to the efficient encoding of binocular information in natural scenes. The efficient coding hypothesis holds that the early stages of visual processing attempt to form an efficient coding of ecologically valid stimuli. Using Independent Component Analysis to learn an efficient coding of stereoscopic images, we show that the ratio of binocular to monocular components varied with eccentricity at the same rate as human stereo acuity and Vernier acuity. Our results demonstrate that the organisation of the visual cortex is dependent on the underlying statistics of binocular scenes and, strikingly, that monocular acuity depends on the mechanisms by which the visual cortex processes binocular information. This result has important theoretical implications for understanding the encoding of visual information in the brain.

scholarly journals The emergence of multiple retinal cell types through efficient coding of natural movies

2018 ◽  
Author(s):  
Samuel A. Ocko ◽  
Jack Lindsey ◽  
Surya Ganguli ◽  
Stephane Deny
Keyword(s):  
Visual Processing ◽  
Visual Information ◽  
Ganglion Cells ◽  
Receptive Fields ◽  
Cell Types ◽  
Retinal Cell ◽  
Retinal Eccentricity ◽  
Natural Scenes ◽  
Efficient Coding ◽  
Cell Densities

AbstractOne of the most striking aspects of early visual processing in the retina is the immediate parcellation of visual information into multiple parallel pathways, formed by different retinal ganglion cell types each tiling the entire visual field. Existing theories of efficient coding have been unable to account for the functional advantages of such cell-type diversity in encoding natural scenes. Here we go beyond previous theories to analyze how a simple linear retinal encoding model with different convolutional cell types efficiently encodes naturalistic spatiotemporal movies given a fixed firing rate budget. We find that optimizing the receptive fields and cell densities of two cell types makes them match the properties of the two main cell types in the primate retina, midget and parasol cells, in terms of spatial and temporal sensitivity, cell spacing, and their relative ratio. Moreover, our theory gives a precise account of how the ratio of midget to parasol cells decreases with retinal eccentricity. Also, we train a nonlinear encoding model with a rectifying nonlinearity to efficiently encode naturalistic movies, and again find emergent receptive fields resembling those of midget and parasol cells that are now further subdivided into ON and OFF types. Thus our work provides a theoretical justification, based on the efficient coding of natural movies, for the existence of the four most dominant cell types in the primate retina that together comprise 70% of all ganglion cells.

scholarly journals Emergence of transformation-tolerant representations of visual objects in rat lateral extrastriate cortex

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Sina Tafazoli ◽  
Houman Safaai ◽  
Gioia De Franceschi ◽  
Federica Bianca Rosselli ◽  
Walter Vanzella ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Visual Processing ◽  
Visual Information ◽  
Neuronal Firing ◽  
Extrastriate Cortex ◽  
Level Energy ◽  
Object Processing ◽  
Neuronal Populations ◽  
Visual Objects ◽  
Extrastriate Areas

Rodents are emerging as increasingly popular models of visual functions. Yet, evidence that rodent visual cortex is capable of advanced visual processing, such as object recognition, is limited. Here we investigate how neurons located along the progression of extrastriate areas that, in the rat brain, run laterally to primary visual cortex, encode object information. We found a progressive functional specialization of neural responses along these areas, with: (1) a sharp reduction of the amount of low-level, energy-related visual information encoded by neuronal firing; and (2) a substantial increase in the ability of both single neurons and neuronal populations to support discrimination of visual objects under identity-preserving transformations (e.g., position and size changes). These findings strongly argue for the existence of a rat object-processing pathway, and point to the rodents as promising models to dissect the neuronal circuitry underlying transformation-tolerant recognition of visual objects.

scholarly journals A Unified Theory Of Early Visual Representations From Retina To Cortex Through Anatomically Constrained Deep CNNs

2019 ◽  
Author(s):  
Jack Lindsey ◽  
Samuel A. Ocko ◽  
Surya Ganguli ◽  
Stephane Deny
Keyword(s):  
Visual System ◽  
Visual Processing ◽  
Visual Information ◽  
Resource Constraints ◽  
Visual Representations ◽  
Direct Consequence ◽  
Visual Scene ◽  
Natural Scenes ◽  
Unified Theory ◽  
Cortical Networks

AbstractThe vertebrate visual system is hierarchically organized to process visual information in successive stages. Neural representations vary drastically across the first stages of visual processing: at the output of the retina, ganglion cell receptive fields (RFs) exhibit a clear antagonistic center-surround structure, whereas in the primary visual cortex (V1), typical RFs are sharply tuned to a precise orientation. There is currently no unified theory explaining these differences in representations across layers. Here, using a deep convolutional neural network trained on image recognition as a model of the visual system, we show that such differences in representation can emerge as a direct consequence of different neural resource constraints on the retinal and cortical networks, and for the first time we find a single model from which both geometries spontaneously emerge at the appropriate stages of visual processing. The key constraint is a reduced number of neurons at the retinal output, consistent with the anatomy of the optic nerve as a stringent bottleneck. Second, we find that, for simple downstream cortical networks, visual representations at the retinal output emerge as nonlinear and lossy feature detectors, whereas they emerge as linear and faithful encoders of the visual scene for more complex cortical networks. This result predicts that the retinas of small vertebrates (e.g. salamander, frog) should perform sophisticated nonlinear computations, extracting features directly relevant to behavior, whereas retinas of large animals such as primates should mostly encode the visual scene linearly and respond to a much broader range of stimuli. These predictions could reconcile the two seemingly incompatible views of the retina as either performing feature extraction or efficient coding of natural scenes, by suggesting that all vertebrates lie on a spectrum between these two objectives, depending on the degree of neural resources allocated to their visual system.

scholarly journals Efficient coding of natural scenes improves neural system identification

2022 ◽  
Author(s):  
Yongrong Qiu ◽  
David A Klindt ◽  
Klaudia P Szatko ◽  
Dominic Gonschorek ◽  
Larissa Hoefling ◽  
...  
Keyword(s):  
System Identification ◽  
Visual Processing ◽  
Neural System ◽  
Natural Scenes ◽  
Natural Environments ◽  
Retinal Neurons ◽  
Efficient Coding ◽  
Recorded Data ◽  
Identification Model ◽  
Regularized Model

Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage coding principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an autoencoder which aims to efficiently encode natural stimuli. To this end, we built a hybrid model to predict the responses of retinal neurons to noise stimuli. This approach did not only yield a higher performance than the stand-alone system identification model, it also produced more biologically-plausible filters. We found these results to be consistent for retinal responses to different stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. In summary, our results support the hypothesis that efficiently encoding environmental inputs can improve system identification models of early visual processing.

scholarly journals Cortical Visual Performance Test Setup for Parkinson’s Disease Based on Motion Blur Orientation

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
M. Erdem Isenkul
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Visual Cortex ◽  
High Performance ◽  
Performance Test ◽  
Vertical Motion ◽  
Motion Blur ◽  
Visual Performance ◽  
Natural Image ◽  
Success Rates

Studies on Parkinson’s disease (PD) are becoming very popular on multidisciplinary platforms. The development of predictable telemonitored early detection models has become closely related to many different research areas. The aim of this article is to develop a visual performance test that can examine the effects of Parkinson’s disease on the visual cortex, which can be a subtitle scoring test in UPDRS. However, instead of showing random images and asking for discrepancies between them, it is expected that the questions to be asked to patients should be provable in the existing cortex models, should be deduced between the images, and produce a reference threshold value to compare with the practical results. In a developed test, horizontal and vertical motion blur orientation was applied to natural image samples, and then neural outputs were produced by representing three (original-horizontal-vertical) image groups with the Layer 4 (L4) cortex model. This image representation is then compared with a filtering model which is very similar to thalamus’ functionality. Thus, the linear problem-solving performance of the L4 cortex model is also addressed in the study. According to the obtained classification results, the L4 model produces high-performance success rates compared to the thalamic model, which shows the adaptation power of the visual cortex on the image pattern differences. In future studies, developed motion-based visual tests are planned to be applied to PD patient groups/controls, and their performances with mathematical threshold values will be examined.

scholarly journals Natural image statistics at depth edges modulate perceptual stability

2020 ◽  
Author(s):  
Zeynep Başgöze ◽  
David N. White ◽  
Johannes Burge ◽  
Emily A. Cooper
Keyword(s):  
Visual System ◽  
Three Dimensional ◽  
Natural Image ◽  
Natural Scenes ◽  
Natural Environments ◽  
Large Set ◽  
Natural Image Statistics ◽  
Binocular Fusion ◽  
Perceptual Stability ◽  
Statistical Regularities

AbstractBinocular fusion relies on matching points in the two eyes that correspond to the same physical feature in the world. However, not all world features are binocularly visible. In particular, at depth edges parts of a scene are often visible to only one eye (so-called half occlusions). Accurate detection of these monocularly visible regions is likely to be important for stable visual perception. If monocular regions are not detected as such, the visual system may attempt to binocularly fuse non-corresponding points, which can result in unstable percepts. We investigated the hypothesis that the visual system capitalizes upon statistical regularities associated with depth edges in natural scenes to aid binocular fusion and facilitate perceptual stability. By sampling from a large set of stereoscopic natural image patches, we found evidence that monocularly visible regions near depth edges in natural scenes tend to have features more visually similar to the adjacent binocularly visible background region than to the adjacent binocularly visible foreground. The generality of these results was supported by a parametric study of three-dimensional (3D) viewing geometry in simulated environments. In two perceptual experiments, we examined if this statistical regularity may be leveraged by the visual system. The results show that perception tended to be more stable when the visual properties of the depth edge were statistically more likely. Exploiting regularities in natural environments may allow the visual system to facilitate fusion and perceptual stability of natural scenes when both binocular and monocular regions are visible.PrecisWe report an analysis of natural scenes and two perceptual studies aimed at understanding how the visual statistics of depth edges impact perceptual stability. Our results suggest that the visual system exploits natural scene regularities to aid binocular fusion and facilitate perceptual stability.

scholarly journals Natural Image Statistics for Mouse Vision

2021 ◽  
Author(s):  
Luca Abballe ◽  
Hiroki Asari
Keyword(s):  
Visual Field ◽  
Uv Light ◽  
Green Light ◽  
Mouse Retina ◽  
Natural Image ◽  
Natural Scenes ◽  
Lower Visual Field ◽  
Local Contrast ◽  
Natural Image Statistics ◽  
Image Statistics

The mouse has dichromatic colour vision based on two different types of opsins: short (S)-and middle (M)-wavelength-sensitive opsins with peak sensitivity to ultraviolet (UV; 360 nm) and green light (508 nm), respectively. In the mouse retina, the cone photoreceptors that predominantly express the S-opsin are more sensitive to contrasts, and denser towards the ventral retina, preferentially sampling the upper part of the visual field. In contrast, the expression of the M-opsin gradually increases towards the dorsal retina that encodes the lower visual field. Such distinct retinal organizations are assumed to arise from a selective pressure in evolution to efficiently encode the natural scenes. However, natural image statistics of UV light have never been examined beyond the spectral analysis. Here we developed a multi-spectral camera and examined the UV and green image statistics of the same natural scenes. We found that the local contrast and the spatial correlation were higher in UV than in green for images above the horizon, but lower in UV than in green for those below the horizon. This suggests that the mouse retina is not necessarily optimal for maximizing the bandwidth of information transmission. Factors besides the coding efficiency, such as visual behavioural requirements, will thus need to be considered to fully explain the characteristic organization of the mouse retina.

scholarly journals Predictability in natural images determines V1 firing rates and synchronization: A deep neural network approach

2020 ◽  
Author(s):  
Cem Uran ◽  
Alina Peter ◽  
Andreea Lazar ◽  
William Barnes ◽  
Johanna Klon-Lipok ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Recognition ◽  
Visual Processing ◽  
Deep Neural Network ◽  
Natural Images ◽  
Natural Image ◽  
Natural Image Statistics ◽  
Neural Network Approach ◽  
Firing Rates

AbstractFeedforward deep neural networks for object recognition are a promising model of visual processing and can accurately predict firing-rate responses along the ventral stream. Yet, these networks have limitations as models of various aspects of cortical processing related to recurrent connectivity, including neuronal synchronization and the integration of sensory inputs with spatio-temporal context. We trained self-supervised, generative neural networks to predict small regions of natural images based on the spatial context (i.e. inpainting). Using these network predictions, we determined the spatial predictability of visual inputs into (macaque) V1 receptive fields (RFs), and distinguished low- from high-level predictability. Spatial predictability strongly modulated V1 activity, with distinct effects on firing rates and synchronization in gamma-(30-80Hz) and beta-bands (18-30Hz). Furthermore, firing rates, but not synchronization, were accurately predicted by a deep neural network for object recognition. Neural networks trained to specifically predict V1 gamma-band synchronization developed large, grating-like RFs in the deepest layer. These findings suggest complementary roles for firing rates and synchronization in self-supervised learning of natural-image statistics.

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