scholarly journals Neural coding of image structure and contrast polarity of Cartesian, hyperbolic, and polar gratings in the primary and secondary visual cortex of the tree shrew

2016 ◽  
Vol 115 (4) ◽  
pp. 2000-2013 ◽  
Author(s):  
Jordan Poirot ◽  
Paolo De Luna ◽  
Gregor Rainer
Keyword(s):  
Visual Cortex ◽  
Neural Coding ◽  
Visual Evoked Potential ◽  
Single Neuron ◽  
Shape Representation ◽  
Tree Shrew ◽  
Columnar Organization ◽  
Visual Areas ◽  
Early Visual Cortex ◽  
Neural Selectivity

We comprehensively characterize spiking and visual evoked potential (VEP) activity in tree shrew V1 and V2 using Cartesian, hyperbolic, and polar gratings. Neural selectivity to structure of Cartesian gratings was higher than other grating classes in both visual areas. From V1 to V2, structure selectivity of spiking activity increased, whereas corresponding VEP values tended to decrease, suggesting that single-neuron coding of Cartesian grating attributes improved while the cortical columnar organization of these neurons became less precise from V1 to V2. We observed that neurons in V2 generally exhibited similar selectivity for polar and Cartesian gratings, suggesting that structure of polar-like stimuli might be encoded as early as in V2. This hypothesis is supported by the preference shift from V1 to V2 toward polar gratings of higher spatial frequency, consistent with the notion that V2 neurons encode visual scene borders and contours. Neural sensitivity to modulations of polarity of hyperbolic gratings was highest among all grating classes and closely related to the visual receptive field (RF) organization of ON- and OFF-dominated subregions. We show that spatial RF reconstructions depend strongly on grating class, suggesting that intracortical contributions to RF structure are strongest for Cartesian and polar gratings. Hyperbolic gratings tend to recruit least cortical elaboration such that the RF maps are similar to those generated by sparse noise, which most closely approximate feedforward inputs. Our findings complement previous literature in primates, rodents, and carnivores and highlight novel aspects of shape representation and coding occurring in mammalian early visual cortex.

Neural Coding of Global Form in the Human Visual Cortex

2008 ◽  
Vol 99 (5) ◽  
pp. 2456-2469 ◽  
Author(s):  
Dirk Ostwald ◽  
Judith M. Lam ◽  
Sheng Li ◽  
Zoe Kourtzi
Keyword(s):  
Visual Cortex ◽  
Neural Coding ◽  
Temporal Cortex ◽  
Complex Object ◽  
Neural Basis ◽  
Activation Patterns ◽  
Global Form ◽  
Pattern Size ◽  
Visual Areas ◽  
Object Features

Extensive psychophysical and computational work proposes that the perception of coherent and meaningful structures in natural images relies on neural processes that convert information about local edges in primary visual cortex to complex object features represented in the temporal cortex. However, the neural basis of these mid-level vision mechanisms in the human brain remains largely unknown. Here, we examine functional MRI (fMRI) selectivity for global forms in the human visual pathways using sensitive multivariate analysis methods that take advantage of information across brain activation patterns. We use Glass patterns, parametrically varying the perceived global form (concentric, radial, translational) while ensuring that the local statistics remain similar. Our findings show a continuum of integration processes that convert selectivity for local signals (orientation, position) in early visual areas to selectivity for global form structure in higher occipitotemporal areas. Interestingly, higher occipitotemporal areas discern differences in global form structure rather than low-level stimulus properties with higher accuracy than early visual areas while relying on information from smaller but more selective neural populations (smaller voxel pattern size), consistent with global pooling mechanisms of local orientation signals. These findings suggest that the human visual system uses a code of increasing efficiency across stages of analysis that is critical for the successful detection and recognition of objects in complex environments.

scholarly journals Running reduces firing but improves coding in rodent higher-order visual cortex

2017 ◽  
Author(s):  
Amelia J. Christensen ◽  
Jonathan W. Pillow
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Single Neuron ◽  
Higher Order ◽  
Visual Responses ◽  
Cortical Layers ◽  
Response Properties ◽  
Stimulus Response ◽  
Allen Brain ◽  
Visual Areas

Running profoundly alters stimulus-response properties in mouse primary visual cortex (V1), but its effects in higher-order visual cortex remain unknown. Here we systematically investigated how locomotion modulates visual responses across six visual areas and three cortical layers using a massive dataset from the Allen Brain Institute. Although running has been shown to increase firing in V1, we found that it suppressed firing in higher-order visual areas. Despite this reduction in gain, visual responses during running could be decoded more accurately than visual responses during stationary periods. We show that this effect was not attributable to changes in noise correlations, and propose that it instead arises from increased reliability of single neuron responses during running.

scholarly journals Evolving super stimuli for real neurons using deep generative networks

2019 ◽  
Author(s):  
Carlos R. Ponce ◽  
Will Xiao ◽  
Peter F. Schade ◽  
Till S. Hartmann ◽  
Gabriel Kreiman ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Visual Cortex ◽  
Neural Coding ◽  
Deep Neural Network ◽  
Natural Images ◽  
List Type ◽  
Neuronal Responses ◽  
Diagnostic Features ◽  
Neural Selectivity

AbstractFinding the best stimulus for a neuron is challenging because it is impossible to test all possible stimuli. Here we used a vast, unbiased, and diverse hypothesis space encoded by a generative deep neural network model to investigate neuronal selectivity in inferotemporal cortex without making any assumptions about natural features or categories. A genetic algorithm, guided by neuronal responses, searched this space for optimal stimuli. Evolved synthetic images evoked higher firing rates than even the best natural images and revealed diagnostic features, independently of category or feature selection. This approach provides a way to investigate neural selectivity in any modality that can be represented by a neural network and challenges our understanding of neural coding in visual cortex.HighlightsA generative deep neural network interacted with a genetic algorithm to evolve stimuli that maximized the firing of neurons in alert macaque inferotemporal and primary visual cortex.The evolved images activated neurons more strongly than did thousands of natural images.Distance in image space from the evolved images predicted responses of neurons to novel images.

scholarly journals Population spatial frequency tuning in human early visual cortex

2020 ◽  
Vol 123 (2) ◽  
pp. 773-785 ◽  
Author(s):  
Sara Aghajari ◽  
Louis N. Vinke ◽  
Sam Ling
Keyword(s):  
Visual Cortex ◽  
Spatial Frequency ◽  
Frequency Tuning ◽  
Efficient Estimation ◽  
Logarithmic Scale ◽  
Frequency Sensitivity ◽  
Spatial Frequency Tuning ◽  
Visual Areas ◽  
Early Visual Cortex ◽  
Band Pass

Neurons within early visual cortex are selective for basic image statistics, including spatial frequency. However, these neurons are thought to act as band-pass filters, with the window of spatial frequency sensitivity varying across the visual field and across visual areas. Although a handful of previous functional (f)MRI studies have examined human spatial frequency sensitivity using conventional designs and analysis methods, these measurements are time consuming and fail to capture the precision of spatial frequency tuning (bandwidth). In this study, we introduce a model-driven approach to fMRI analyses that allows for fast and efficient estimation of population spatial frequency tuning (pSFT) for individual voxels. Blood oxygen level-dependent (BOLD) responses within early visual cortex were acquired while subjects viewed a series of full-field stimuli that swept through a large range of spatial frequency content. Each stimulus was generated by band-pass filtering white noise with a central frequency that changed periodically between a minimum of 0.5 cycles/degree (cpd) and a maximum of 12 cpd. To estimate the underlying frequency tuning of each voxel, we assumed a log-Gaussian pSFT and optimized the parameters of this function by comparing our model output against the measured BOLD time series. Consistent with previous studies, our results show that an increase in eccentricity within each visual area is accompanied by a drop in the peak spatial frequency of the pSFT. Moreover, we found that pSFT bandwidth depends on eccentricity and is correlated with the pSFT peak; populations with lower peaks possess broader bandwidths in logarithmic scale, whereas in linear scale this relationship is reversed. NEW & NOTEWORTHY Spatial frequency selectivity is a hallmark property of early visuocortical neurons, and mapping these sensitivities gives us crucial insight into the hierarchical organization of information within visual areas. Due to technical obstacles, we lack a comprehensive picture of the properties of this sensitivity in humans. Here, we introduce a new method, coined population spatial frequency tuning mapping, which circumvents the limitations of the conventional neuroimaging methods, yielding a fuller visuocortical map of spatial frequency sensitivity.

scholarly journals Attending to What and Where: Background Connectivity Integrates Categorical and Spatial Attention

2018 ◽  
Vol 30 (9) ◽  
pp. 1281-1297 ◽  
Author(s):  
Alexa Tompary ◽  
Naseem Al-Aidroos ◽  
Nicholas B. Turk-Browne
Keyword(s):  
Visual Cortex ◽  
Spatial Attention ◽  
Temporal Cortex ◽  
Relevant Information ◽  
Neural Basis ◽  
Attentional Modulation ◽  
Temporal Correlations ◽  
Visual Areas ◽  
Early Visual Cortex ◽  
Ventral Temporal Cortex

Top–down attention prioritizes the processing of goal-relevant information throughout visual cortex based on where that information is found in space and what it looks like. Whereas attentional goals often have both spatial and featural components, most research on the neural basis of attention has examined these components separately. Here we investigated how these attentional components are integrated by examining the attentional modulation of functional connectivity between visual areas with different selectivity. Specifically, we used fMRI to measure temporal correlations between spatially selective regions of early visual cortex and category-selective regions in ventral temporal cortex while participants performed a task that benefitted from both spatial and categorical attention. We found that categorical attention modulated the connectivity of category-selective areas, but only with retinotopic areas that coded for the spatially attended location. Similarly, spatial attention modulated the connectivity of retinotopic areas only with the areas coding for the attended category. This pattern of results suggests that attentional modulation of connectivity is driven both by spatial selection and featural biases. Combined with exploratory analyses of frontoparietal areas that track these changes in connectivity among visual areas, this study begins to shed light on how different components of attention are integrated in support of more complex behavioral goals.

Anodal Occipital Transcranial Direct Current Stimulation Enhances Perceived Visual Size Illusions

2020 ◽  
pp. 1-8
Author(s):  
Anqi Wang ◽  
Lihong Chen ◽  
Yi Jiang
Keyword(s):  
Visual Cortex ◽  
Direct Current ◽  
Transcranial Direct Current Stimulation ◽  
Size Perception ◽  
Central Target ◽  
Direct Current Stimulation ◽  
Visual Areas ◽  
Early Visual Cortex ◽  
Context Dependent ◽  
Visual Size

Human early visual cortex has long been suggested to play a crucial role in context-dependent visual size perception through either lateral interaction or feedback projections from higher to lower visual areas. We investigated the causal contribution of early visual cortex to context-dependent visual size perception using the technique of transcranial direct current stimulation and two well-known size illusions (i.e., the Ebbinghaus and Ponzo illusions) and further elucidated the underlying mechanism that mediates the effect of transcranial direct current stimulation over early visual cortex. The results showed that the magnitudes of both size illusions were significantly increased by anodal stimulation relative to sham stimulation but left unaltered by cathodal stimulation. Moreover, the anodal effect persisted even when the central target and surrounding inducers of the Ebbinghaus configuration were presented to different eyes, with the effect lasting no more than 15 min. These findings provide compelling evidence that anodal occipital stimulation enhances the perceived visual size illusions, which is possibly mediated by weakening the suppressive function of the feedback connections from higher to lower visual areas. Moreover, the current study provides further support for the causal role of early visual cortex in the neural processing of context-dependent visual size perception.

scholarly journals Enhanced attentional gain as a mechanism for generalized perceptual learning in human visual cortex

2014 ◽  
Vol 112 (5) ◽  
pp. 1217-1227 ◽  
Author(s):  
Anna Byers ◽  
John T. Serences
Keyword(s):  
Visual Cortex ◽  
Perceptual Learning ◽  
Broad Class ◽  
Sensory Information ◽  
Large Set ◽  
Human Visual Cortex ◽  
Visual Areas ◽  
Early Visual Cortex ◽  
Response Profiles ◽  
Sensory Responses

Learning to better discriminate a specific visual feature (i.e., a specific orientation in a specific region of space) has been associated with plasticity in early visual areas ( sensory modulation) and with improvements in the transmission of sensory information from early visual areas to downstream sensorimotor and decision regions ( enhanced readout). However, in many real-world scenarios that require perceptual expertise, observers need to efficiently process numerous exemplars from a broad stimulus class as opposed to just a single stimulus feature. Some previous data suggest that perceptual learning leads to highly specific neural modulations that support the discrimination of specific trained features. However, the extent to which perceptual learning acts to improve the discriminability of a broad class of stimuli via the modulation of sensory responses in human visual cortex remains largely unknown. Here, we used functional MRI and a multivariate analysis method to reconstruct orientation-selective response profiles based on activation patterns in the early visual cortex before and after subjects learned to discriminate small offsets in a set of grating stimuli that were rendered in one of nine possible orientations. Behavioral performance improved across 10 training sessions, and there was a training-related increase in the amplitude of orientation-selective response profiles in V1, V2, and V3 when orientation was task relevant compared with when it was task irrelevant. These results suggest that generalized perceptual learning can lead to modified responses in the early visual cortex in a manner that is suitable for supporting improved discriminability of stimuli drawn from a large set of exemplars.

scholarly journals Decoding Multivoxel Representations of Affective Scenes in Retinotopic Visual Cortex

2020 ◽  
Author(s):  
Ke Bo ◽  
Siyang Yin ◽  
Yuelu Liu ◽  
Zhenhong Hu ◽  
Sreenivasan Meyyapan ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Event Related Potential ◽  
Affective Content ◽  
Neural Representations ◽  
Visual Areas ◽  
Early Visual Cortex ◽  
Decoding Accuracy

AbstractThe perception of opportunities and threats in complex scenes represents one of the main functions of the human visual system. In the laboratory, its neurophysiological basis is often studied by having observers view pictures varying in affective content. This body of work has consistently shown that viewing emotionally engaging, compared to neutral, pictures (1) heightens blood flow in limbic structures and frontoparietal cortex, as well as in anterior ventral and dorsal visual cortex, and (2) prompts an increase in the late positive event-related potential (LPP), a scalp-recorded and time-sensitive index of engagement within the network of aforementioned neural structures. The role of retinotopic visual cortex in this process has, however, been contentious, with competing theoretical notions predicting the presence versus absence of emotion-specific signals in retinotopic visual areas. The present study used multimodal neuroimaging and machine learning to address this question by examining the large-scale neural representations of affective pictures. Recording EEG and fMRI simultaneously while observers viewed pleasant, unpleasant, and neutral affective pictures, and applying multivariate pattern analysis to single-trial BOLD activities in retinotopic visual cortex, we identified three robust findings: First, unpleasant-versus-neutral decoding accuracy, as well as pleasant-versus-neutral decoding accuracy, were well above chance level in all retinotopic visual areas, including primary visual cortex. Second, the decoding accuracy in ventral visual cortex, but not in early visual cortex or dorsal visual cortex, was significantly correlated with LPP amplitude. Third, effective connectivity from amygdala to ventral visual cortex predicted unpleasant-versus-neutral decoding accuracy, and effective connectivity from ventral frontal cortex to ventral visual cortex predicted pleasant-versus-neutral decoding accuracy. These results suggest that affective pictures evoked valence-specific multivoxel neural representations in retinotopic visual cortex and that these multivoxel representations were influenced by reentry signals from limbic and frontal brain regions.

scholarly journals Cortical Double-Opponent Cells in Color Perception: Perceptual Scaling and Chromatic Visual Evoked Potentials

i-Perception ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 204166951775271 ◽  
Author(s):  
Valerie Nunez ◽  
Robert M. Shapley ◽  
James Gordon
Keyword(s):  
Visual Cortex ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Color Perception ◽  
Neural Substrates ◽  
Response Dynamics ◽  
Early Visual Cortex ◽  
Visual Evoked

In the early visual cortex V1, there are currently only two known neural substrates for color perception: single-opponent and double-opponent cells. Our aim was to explore the relative contributions of these neurons to color perception. We measured the perceptual scaling of color saturation for equiluminant color checkerboard patterns (designed to stimulate double-opponent neurons preferentially) and uniformly colored squares (designed to stimulate only single-opponent neurons) at several cone contrasts. The spatially integrative responses of single-opponent neurons would produce the same response magnitude for checkerboards as for uniform squares of the same space-averaged cone contrast. However, perceived saturation of color checkerboards was higher than for the corresponding squares. The perceptual results therefore imply that double-opponent cells are involved in color perception of patterns. We also measured the chromatic visual evoked potential (cVEP) produced by the same stimuli; checkerboard cVEPs were much larger than those for corresponding squares, implying that double-opponent cells also contribute to the cVEP response. The total Fourier power of the cVEP grew sublinearly with cone contrast. However, the 6-Hz Fourier component’s power grew linearly with contrast-like saturation perception. This may also indicate that cortical coding of color depends on response dynamics.

scholarly journals Attending to what and where: Background connectivity integrates categorical and spatial attention

2017 ◽  
Author(s):  
Alexa Tompary ◽  
Naseem Al-Aidroos ◽  
Nicholas B. Turk-Browne
Keyword(s):  
Visual Cortex ◽  
Spatial Attention ◽  
Temporal Cortex ◽  
Relevant Information ◽  
Neural Basis ◽  
Attentional Modulation ◽  
Temporal Correlations ◽  
Visual Areas ◽  
Early Visual Cortex ◽  
Ventral Temporal Cortex

AbstractTop-down attention prioritizes the processing of goal-relevant information throughout visual cortex, based on where that information is found in space and what it looks like. Whereas attentional goals often have both spatial and featural components, most research on the neural basis of attention has examined these components separately. This may reflect the fact that attention is typically studied in individual visual areas that preferentially code for either spatial locations or particular features. Here we investigated how these attentional components are integrated by examining the attentional modulation of functional connectivity between visual areas with different selectivity. Specifically, we used fMRI to measure temporal correlations between spatially-selective regions of early visual cortex and category-selective regions in ventral temporal cortex while participants performed a task that benefitted from both spatial and categorical attention. We found that categorical attention modulated the connectivity of category-selective areas, but only with retinotopic areas that coded for the spatially attended location. The reverse was not true, however, with spatial attention modulating the connectivity of retinotopic areas with category-selective areas coding for both attended and unattended features. This pattern of results suggests that attentional modulation of connectivity is dominated by spatial selection, which in turn gates featural biases. Combined with exploratory analyses of frontoparietal areas that track these changes in connectivity among visual areas, this study begins to shed light on how different components of attention are integrated in support of more complex behavioral goals.

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