Dataset Information

Synaptic Mechanisms of Feature Coding in the Visual Cortex of Awake Mice.

ABSTRACT: The synaptic mechanisms of feature coding in the visual cortex are poorly understood, particularly in awake animals. The ratio between excitation (E) and inhibition (I) might be constant across stimulus space, controlling only the gain and timing of neuronal responses, or it might change, directly contributing to feature coding. Whole-cell recordings in L2/3 of awake mice revealed that the E/I ratio systematically declines with increasing stimulus contrast or size. Suppressing somatostatin (SOM) neurons enhanced the E and I underlying size tuning, explaining SOM neurons' role in surround suppression. These data imply that contrast and size tuning result from a combination of a changing E/I ratio and the tuning of total synaptic input. Furthermore, they provide experimental support in awake animals for the "Stabilized Supralinear Network," a model that explains diverse cortical phenomena, and suggest that a decreasing E/I ratio with increasing cortical drive could contribute to many different cortical computations.

SUBMITTER: Adesnik H

PROVIDER: S-EPMC5580349 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Synaptic Mechanisms of Feature Coding in the Visual Cortex of Awake Mice.

Adesnik Hillel H

Neuron 20170801 5

The synaptic mechanisms of feature coding in the visual cortex are poorly understood, particularly in awake animals. The ratio between excitation (E) and inhibition (I) might be constant across stimulus space, controlling only the gain and timing of neuronal responses, or it might change, directly contributing to feature coding. Whole-cell recordings in L2/3 of awake mice revealed that the E/I ratio systematically declines with increasing stimulus contrast or size. Suppressing somatostatin (SOM) ...[more]

PMID: 28858618

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Project description:IntroductionThe visual cortex is a key region in the mouse brain, responsible for processing visual information. Comprised of six distinct layers, each with unique neuronal types and connections, the visual cortex exhibits diverse decoding properties across its layers. This study aimed to investigate the relationship between visual stimulus decoding properties and the cortical layers of the visual cortex while considering how this relationship varies across different decoders and brain regions.MethodsThis study reached the above conclusions by analyzing two publicly available datasets obtained through two-photon microscopy of visual cortex neuronal responses. Various types of decoders were tested for visual cortex decoding.ResultsOur findings indicate that the decoding accuracy of neuronal populations with consistent sizes varies among visual cortical layers for visual stimuli such as drift gratings and natural images. In particular, layer 4 neurons in VISp exhibited significantly higher decoding accuracy for visual stimulus identity compared to other layers. However, in VISm, the decoding accuracy of neuronal populations with the same size in layer 2/3 was higher than that in layer 4, despite the overall accuracy being lower than that in VISp and VISl. Furthermore, SVM surpassed other decoders in terms of accuracy, with the variation in decoding performance across layers being consistent among decoders. Additionally, we found that the difference in decoding accuracy across different imaging depths was not associated with the mean orientation selectivity index (OSI) and the mean direction selectivity index (DSI) neurons, but showed a significant positive correlation with the mean reliability and mean signal-to-noise ratio (SNR) of each layer's neuron population.DiscussionThese findings lend new insights into the decoding properties of the visual cortex, highlighting the role of different cortical layers and decoders in determining decoding accuracy. The correlations identified between decoding accuracy and factors such as reliability and SNR pave the way for more nuanced understandings of visual cortex functioning.

Project description:In the cat primary visual cortex, it is accepted that neurons optimally responding to similar stimulus orientations are clustered in a column extending from the superficial to deep layers. The cerebral cortex is, however, folded inside a skull, which makes gyri and fundi. The primary visual area of cats, area 17, is located on the fold of the cortex called the lateral gyrus. These facts raise the question of how to reconcile the tangential arrangement of the orientation columns with the curvature of the gyrus. In the present study, we show a possible configuration of feature representation in the visual cortex using a three-dimensional (3D) self-organization model. We took into account preferred orientation, preferred direction, ocular dominance and retinotopy, assuming isotropic interaction. We performed computer simulation only in the middle layer at the beginning and expanded the range of simulation gradually to other layers, which was found to be a unique method in the present model for obtaining orientation columns spanning all the layers in the flat cortex. Vertical columns of preferred orientations were found in the flat parts of the model cortex. On the other hand, in the curved parts, preferred orientations were represented in wedge-like columns rather than straight columns, and preferred directions were frequently reversed in the deeper layers. Singularities associated with orientation representation appeared as warped lines in the 3D model cortex. Direction reversal appeared on the sheets that were delimited by orientation-singularity lines. These structures emerged from the balance between periodic arrangements of preferred orientations and vertical alignment of the same orientations. Our theoretical predictions about orientation representation were confirmed by multi-slice, high-resolution functional MRI in the cat visual cortex. We obtained a close agreement between theoretical predictions and experimental observations. The present study throws a doubt about the conventional columnar view of orientation representation, although more experimental data are needed.

Dataset Information

Synaptic Mechanisms of Feature Coding in the Visual Cortex of Awake Mice.

Publications

Synaptic Mechanisms of Feature Coding in the Visual Cortex of Awake Mice.

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