The spatial structure of correlations in natural scenes shapes neural coding in mouse primary visual cortex

Sparse coding theory demonstrates that the neurons in the primary visual cortex form a sparse representation of natural scenes in the viewpoint of statistics, but a typical scene contains many different patterns (corresponding to neurons in cortex) competing for neural representation because of the limited processing capacity of the visual system. We propose an attention-guided sparse coding model. This model includes two modules: the non-uniform sampling module simulating the process of retina and a data-driven attention module based on the response saliency. Our experiment results show that the model notably decreases the number of coefficients which may be activated, and retains the main vision information at the same time. It provides a way to improve the coding efficiency for sparse coding model and to achieve good performance in both population sparseness and lifetime sparseness.

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Orientation tuning depends on spatial frequency in mouse visual cortex

10.1101/069997 ◽

2016 ◽

Cited By ~ 1

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.

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Effects of V1 surround modulation tuning on visual saliency and the tilt illusion

Journal of Neurophysiology ◽

10.1152/jn.00864.2017 ◽

2018 ◽

Vol 120 (3) ◽

pp. 942-952

Author(s):

Sander W. Keemink ◽

Clemens Boucsein ◽

Mark C. W. van Rossum

Keyword(s):

Visual Cortex ◽

Receptive Field ◽

Primary Visual Cortex ◽

Neural Coding ◽

Saliency Detection ◽

Tilt Illusion ◽

Wide Range ◽

Neurophysiological Studies ◽

Salient Features ◽

The Impact

Neurons in the primary visual cortex respond to oriented stimuli placed in the center of their receptive field, yet their response is modulated by stimuli outside the receptive field (the surround). Classically, this surround modulation is assumed to be strongest if the orientation of the surround stimulus aligns with the neuron’s preferred orientation, irrespective of the actual center stimulus. This neuron-dependent surround modulation has been used to explain a wide range of psychophysical phenomena, such as biased tilt perception and saliency of stimuli with contrasting orientation. However, several neurophysiological studies have shown that for most neurons surround modulation is instead center dependent: it is strongest if the surround orientation aligns with the center stimulus. As the impact of such center-dependent modulation on the population level is unknown, we examine this using computational models. We find that with neuron-dependent modulation the biases in orientation coding, commonly used to explain the tilt illusion, are larger than psychophysically reported, but disappear with center-dependent modulation. Therefore we suggest that a mixture of the two modulation types is necessary to quantitatively explain the psychophysically observed biases. Next, we find that under center-dependent modulation average population responses are more sensitive to orientation differences between stimuli, which in theory could improve saliency detection. However, this effect depends on the specific saliency model. Overall, our results thus show that center-dependent modulation reduces coding bias, while possibly increasing the sensitivity to salient features. NEW & NOTEWORTHY Neural responses in the primary visual cortex are modulated by stimuli surrounding the receptive field. Most earlier studies assume this modulation depends on the neuron’s tuning properties, but experiments have shown that instead it depends mostly on the stimulus characteristics. We show that this simple change leads to neural coding that is less biased and under some conditions more sensitive to salient features.

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Neural representation of the bottom-up saliency map of natural scenes in human primary visual cortex

Journal of Vision ◽

10.1167/13.9.233 ◽

2013 ◽

Vol 13 (9) ◽

pp. 233-233

Author(s):

C. Chen ◽

X. Zhang ◽

T. Zhou ◽

Y. Wang ◽

F. Fang

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Saliency Map ◽

Neural Representation ◽

Natural Scenes ◽

Bottom Up

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On efficient sparse spike coding schemes for learning natural scenes in the primary visual cortex

BMC Neuroscience ◽

10.1186/1471-2202-8-s2-p206 ◽

2007 ◽

Vol 8 (S2) ◽

Cited By ~ 2

Author(s):

Laurent Perrinet

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Natural Scenes ◽

Coding Schemes ◽

Spike Coding

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Coding of Natural Scenes in Primary Visual Cortex

Neuron ◽

10.1016/s0896-6273(03)00022-9 ◽

2003 ◽

Vol 37 (4) ◽

pp. 703-718 ◽

Cited By ~ 85

Author(s):

Michael Weliky ◽

József Fiser ◽

Ruskin H Hunt ◽

David N Wagner

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Natural Scenes

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The spatial structure of feedforward information in mouse primary visual cortex

10.1101/2019.12.24.888156 ◽

2019 ◽

Author(s):

Jun Zhuang ◽

Rylan S Larsen ◽

Kevin T Takasaki ◽

Naveen D Ouellette ◽

Tanya L Daigle ◽

...

Keyword(s):

Visual Cortex ◽

Spatial Structure ◽

Structural Properties ◽

Primary Visual Cortex ◽

Receptive Fields ◽

Laminar Structure ◽

Motion Direction ◽

Multiple Modalities ◽

New Model ◽

Spatial Frequencies

Location-sensitive and motion-sensitive units are the two major functional types of feedforward projections from lateral genicular nucleus (LGN) to primary visual cortex (V1) in mouse. The distribution of these inputs in cortical depth remains under debate. By measuring the calcium activities of LGN axons in V1 of awake mice, we systematically mapped their functional and structural properties. Although both types distributed evenly across cortical depth, we found that they differ significantly across multiple modalities. Compared to the location-sensitive axons, which possessed confined spatial receptive fields, the motion-sensitive axons lacked spatial receptive fields, preferred lower temporal, higher spatial frequencies and had wider horizontal bouton spread. Furthermore, the motion-sensitive axons showed a strong depth-dependent motion direction bias while the location-sensitive axons showed a depth-independent OFF dominance. Overall, our results suggest a new model of receptive biases and laminar structure of thalamic inputs to V1.

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