Coarse-to-Fine Processing Drives the Efficient Coding of Natural Scenes in Mouse Visual Cortex

2021 ◽  
Author(s):  
Rolf Skyberg ◽  
Seiji Tanabe ◽  
Hui Chen ◽  
Jianhua Cang
Keyword(s):  
Visual Cortex ◽  
Natural Scenes ◽  
Efficient Coding ◽  
Mouse Visual Cortex ◽  
Coarse To Fine

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 Spatial Correlations in Natural Scenes Modulate Response Reliability in Mouse Visual Cortex

2015 ◽  
Vol 35 (43) ◽  
pp. 14661-14680 ◽  
Author(s):  
R. V. Rikhye ◽  
M. Sur
Keyword(s):  
Visual Cortex ◽  
Natural Scenes ◽  
Spatial Correlations ◽  
Mouse Visual Cortex

scholarly journals A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex

2018 ◽  
Author(s):  
Saskia E. J. de Vries ◽  
Jerome Lecoq ◽  
Michael A. Buice ◽  
Peter A. Groblewski ◽  
Gabriel K. Ocker ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Sensory Information ◽  
Natural Scenes ◽  
Visual Coding ◽  
Adult Mice ◽  
Cortical Responses ◽  
Visual Cortical ◽  
Mouse Visual Cortex ◽  
Hierarchical Computation

SummaryTo understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.

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 Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex

2021 ◽  
Author(s):  
Colin Conwell ◽  
David Mayo ◽  
Boris Katz ◽  
Michael A. Buice ◽  
George A. Alvarez ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Visual Cortex ◽  
Cortical Neurons ◽  
Large Scale ◽  
Deep Neural Network ◽  
Neural Network Modeling ◽  
Natural Scenes ◽  
Neural Network Models ◽  
Mouse Visual Cortex

How well do deep neural networks fare as models of mouse visual cortex? A majority of research to date suggests results far more mixed than those produced in the modeling of primate visual cortex. Here, we perform a large-scale benchmarking of dozens of deep neural network models in mouse visual cortex with multiple methods of comparison and multiple modes of verification. Using the Allen Brain Observatory's 2-photon calcium-imaging dataset of activity in over 59,000 rodent visual cortical neurons recorded in response to natural scenes, we replicate previous findings and resolve previous discrepancies, ultimately demonstrating that modern neural networks can in fact be used to explain activity in the mouse visual cortex to a more reasonable degree than previously suggested. Using our benchmark as an atlas, we offer preliminary answers to overarching questions about levels of analysis (e.g. do models that better predict the representations of individual neurons also predict representational geometry across neural populations?); questions about the properties of models that best predict the visual system overall (e.g. does training task or architecture matter more for augmenting predictive power?); and questions about the mapping between biological and artificial representations (e.g. are there differences in the kinds of deep feature spaces that predict neurons from primary versus posteromedial visual cortex?). Along the way, we introduce a novel, highly optimized neural regression method that achieves SOTA scores (with gains of up to 34%) on the publicly available benchmarks of primate BrainScore. Simultaneously, we benchmark a number of models (including vision transformers, MLP-Mixers, normalization free networks and Taskonomy encoders) outside the traditional circuit of convolutional object recognition. Taken together, our results provide a reference point for future ventures in the deep neural network modeling of mouse visual cortex, hinting at novel combinations of method, architecture, and task to more fully characterize the computational motifs of visual representation in a species so indispensable to neuroscience.

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