scholarly journals Selective Activation of the Deep Layers of the Human Primary Visual Cortex by Top-Down Feedback

2016 ◽  
Vol 26 (3) ◽  
pp. 371-376 ◽  
Author(s):  
Peter Kok ◽  
Lauren J. Bains ◽  
Tim van Mourik ◽  
David G. Norris ◽  
Floris P. de Lange
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Top Down ◽  
Selective Activation ◽  
Deep Layers

scholarly journals Bottom-up saliency and top-down learning in the primary visual cortex of monkeys

2018 ◽  
Vol 115 (41) ◽  
pp. 10499-10504 ◽  
Author(s):  
Yin Yan ◽  
Li Zhaoping ◽  
Wu Li
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Detection Task ◽  
Late Response ◽  
Late Component ◽  
Neuronal Responses ◽  
Top Down ◽  
Bottom Up ◽  
Response Component ◽  
Orientation Contrast

Early sensory cortex is better known for representing sensory inputs but less for the effect of its responses on behavior. Here we explore the behavioral correlates of neuronal responses in primary visual cortex (V1) in a task to detect a uniquely oriented bar—the orientation singleton—in a background of uniformly oriented bars. This singleton is salient or inconspicuous when the orientation contrast between the singleton and background bars is sufficiently large or small, respectively. Using implanted microelectrodes, we measured V1 activities while monkeys were trained to quickly saccade to the singleton. A neuron’s responses to the singleton within its receptive field had an early and a late component, both increased with the orientation contrast. The early component started from the outset of neuronal responses; it remained unchanged before and after training on the singleton detection. The late component started ∼40 ms after the early one; it emerged and evolved with practicing the detection task. Training increased the behavioral accuracy and speed of singleton detection and increased the amount of information in the late response component about a singleton’s presence or absence. Furthermore, for a given singleton, faster detection performance was associated with higher V1 responses; training increased this behavioral–neural correlate in the early V1 responses but decreased it in the late V1 responses. Therefore, V1’s early responses are directly linked with behavior and represent the bottom-up saliency signals. Learning strengthens this link, likely serving as the basis for making the detection task more reflexive and less top-down driven.

scholarly journals Layer-dependent multiplicative effects of spatial attention on contrast responses in human early visual cortex

2020 ◽  
Author(s):  
Fanhua Guo ◽  
Chengwen Liu ◽  
Chencan Qian ◽  
Zihao Zhang ◽  
Kaibao Sun ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Spatial Attention ◽  
Middle Layer ◽  
Additive Effect ◽  
List Type ◽  
Strong Nonlinearity ◽  
Top Down ◽  
Contrast Gain ◽  
Early Visual Cortex ◽  
Deep Layers

AbstractAttention mechanisms at different cortical layers of human visual cortex remain poorly understood. Using submillimeter-resolution fMRI at 7T, we investigated the effects of top-down spatial attention on the contrast responses across different cortical depths in human early visual cortex. Gradient echo (GE) T2* weighted BOLD signal showed an additive effect of attention on contrast responses across cortical depths. Compared to the middle cortical depth, attention modulation was stronger in the superficial and deep depths of V1, and also stronger in the superficial depth of V2 and V3. Using ultra-high resolution (0.3mm in-plane) balanced steady-state free precession (bSSFP) fMRI, a multiplicative scaling effect of attention was found in the superficial and deep layers, but not in the middle layer of V1. Attention modulation of low contrast response was strongest in the middle cortical depths, indicating baseline enhancement or contrast gain of attention modulation on feedforward input. Finally, the additive effect of attention on T2* BOLD can be explained by strong nonlinearity of BOLD signals from large blood vessels, suggesting multiplicative effect of attention on neural activity. These findings support that top-down spatial attention mainly operates through feedback connections from higher order cortical areas, and a distinct mechanism of attention may also be associated with feedforward input through subcortical pathway.HighlightsResponse or activity gain of spatial attention in superficial and deep layersContrast gain or baseline shift of attention in V1 middle layerNonlinearity of large blood vessel causes additive effect of attention on T2* BOLD

scholarly journals Selective Activation of a Putative Reinforcement Signal Conditions Cued Interval Timing in Primary Visual Cortex

2015 ◽  
Vol 25 (12) ◽  
pp. 1551-1561 ◽  
Author(s):  
Cheng-Hang Liu ◽  
Jason E. Coleman ◽  
Heydar Davoudi ◽  
Kechen Zhang ◽  
Marshall G. Hussain Shuler
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Interval Timing ◽  
Selective Activation

scholarly journals Characterization of Feedback Neurons in the High-Level Visual Cortical Areas That Project Directly to the Primary Visual Cortex in the Cat

2021 ◽  
Vol 14 ◽  
Author(s):  
Huijun Pan ◽  
Shen Zhang ◽  
Deng Pan ◽  
Zheng Ye ◽  
Hao Yu ◽  
...  
Keyword(s):  
Information Processing ◽  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Information ◽  
Higher Order ◽  
Top Down ◽  
Cortical Areas ◽  
Area 21A ◽  
High Level ◽  
Visual Cortical

Previous studies indicate that top-down influence plays a critical role in visual information processing and perceptual detection. However, the substrate that carries top-down influence remains poorly understood. Using a combined technique of retrograde neuronal tracing and immunofluorescent double labeling, we characterized the distribution and cell type of feedback neurons in cat’s high-level visual cortical areas that send direct connections to the primary visual cortex (V1: area 17). Our results showed: (1) the high-level visual cortex of area 21a at the ventral stream and PMLS area at the dorsal stream have a similar proportion of feedback neurons back projecting to the V1 area, (2) the distribution of feedback neurons in the higher-order visual area 21a and PMLS was significantly denser than in the intermediate visual cortex of area 19 and 18, (3) feedback neurons in all observed high-level visual cortex were found in layer II–III, IV, V, and VI, with a higher proportion in layer II–III, V, and VI than in layer IV, and (4) most feedback neurons were CaMKII-positive excitatory neurons, and few of them were identified as inhibitory GABAergic neurons. These results may argue against the segregation of ventral and dorsal streams during visual information processing, and support “reverse hierarchy theory” or interactive model proposing that recurrent connections between V1 and higher-order visual areas constitute the functional circuits that mediate visual perception. Also, the corticocortical feedback neurons from high-level visual cortical areas to the V1 area are mostly excitatory in nature.

scholarly journals Prior expectations evoke stimulus-specific activity in the deep layers of the primary visual cortex

PLoS Biology ◽  
2020 ◽  
Vol 18 (12) ◽  
pp. e3001023
Author(s):  
Fraser Aitken ◽  
Georgios Menelaou ◽  
Oliver Warrington ◽  
Renée S. Koolschijn ◽  
Nadège Corbin ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Specific Activity ◽  
Predictive Processing ◽  
Cortical Layers ◽  
Computational Architecture ◽  
Deep Layers ◽  
Flow Through ◽  
The Brain ◽  
The Way

The way we perceive the world is strongly influenced by our expectations. In line with this, much recent research has revealed that prior expectations strongly modulate sensory processing. However, the neural circuitry through which the brain integrates external sensory inputs with internal expectation signals remains unknown. In order to understand the computational architecture of the cortex, we need to investigate the way these signals flow through the cortical layers. This is crucial because the different cortical layers have distinct intra- and interregional connectivity patterns, and therefore determining which layers are involved in a cortical computation can inform us on the sources and targets of these signals. Here, we used ultra-high field (7T) functional magnetic resonance imaging (fMRI) to reveal that prior expectations evoke stimulus-specific activity selectively in the deep layers of the primary visual cortex (V1). These findings are in line with predictive processing theories proposing that neurons in the deep cortical layers represent perceptual hypotheses and thereby shed light on the computational architecture of cortex.

scholarly journals Layer-specificity in the effects of attention and working memory on activity in primary visual cortex

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Timo van Kerkoerle ◽  
Matthew W. Self ◽  
Pieter R. Roelfsema
Keyword(s):  
Working Memory ◽  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Neuronal Activity ◽  
Current Source ◽  
Memory Task ◽  
Neuronal Firing ◽  
Top Down ◽  
Attention Task ◽  
Early Visual Cortex

Abstract Neuronal activity in early visual cortex depends on attention shifts but the contribution to working memory has remained unclear. Here, we examine neuronal activity in the different layers of the primary visual cortex (V1) in an attention-demanding and a working memory task. A current-source density analysis reveales top-down inputs in the superficial layers and layer 5, and an increase in neuronal firing rates most pronounced in the superficial and deep layers and weaker in input layer 4. This increased activity is strongest in the attention task but it is also highly reliable during working memory delays. A visual mask erases the V1 memory activity, but it reappeares at a later point in time. These results provide new insights in the laminar circuits involved in the top-down modulation of activity in early visual cortex in the presence and absence of visual stimuli.

scholarly journals Ultra-high resolution fMRI reveals origins of feedforward and feedback activity within laminae of human ocular dominance columns

2020 ◽  
Author(s):  
Gilles de Hollander ◽  
Wietske van der Zwaag ◽  
Chencan Qian ◽  
Peng Zhang ◽  
Tomas Knapen
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
High Field ◽  
Ocular Dominance ◽  
Top Down ◽  
Ultra High Field ◽  
Bottom Up ◽  
Ocular Dominance Columns ◽  
Cortical Columns ◽  
Model Free

AbstractUltra-high field MRI can functionally image the cerebral cortex of human subjects at the submillimeter scale of cortical columns and laminae. Here, we investigate both in concert, by, for the first time, imaging ocular dominance columns (ODCs) in primary visual cortex (V1) across different cortical depths. We ensured that putative ODC patterns in V1 (a) are stable across runs, sessions, and scanners located in different continents (b) have a width (∼1.3 mm) expected from post-mortem and animal work and (c) are absent at the retinotopic location of the blind spot. We then dissociated the effects of bottom-up thalamo-cortical input and attentional feedback processes on activity in V1 across cortical depth. Importantly, the separation of bottom-up information flows into ODCs allowed us to validly compare attentional conditions while keeping the stimulus identical throughout the experiment. We find that, when correcting for draining vein effects and using both model-based and model-free approaches, the effect of monocular stimulation is largest at deep and middle cortical depths. Conversely, spatial attention influences BOLD activity exclusively near the pial surface. Our findings show that simultaneous interrogation of columnar and laminar dimensions of the cortical fold can dissociate thalamocortical inputs from top-down processing, and allow the investigation of their interactions without any stimulus manipulation.Significance StatementThe advent of ultra-high field fMRI allows for the study of the human brain non-invasively at submillimeter resolution, bringing the scale of cortical columns and laminae into focus. De Hollander et al imaged the ocular dominance columns and laminae of V1 in concert, while manipulating top-down attention. This allowed them to separate feedforward from feedback processes in the brain itself, without resorting to the manipulation of incoming information. Their results show how feedforward and feedback processes interact in the primary visual cortex, highlighting the different computational roles separate laminae play.

scholarly journals Generative perspective of the primary visual cortex

2021 ◽  
Author(s):  
Zedong Bi
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Power Law ◽  
Computational Models ◽  
Higher Order ◽  
Top Down ◽  
Complex Cells ◽  
Shape Morphing ◽  
Neural Representations ◽  
Learning Principles

According to analysis-by-synthesis theories of perception, the primary visual cortex (V1) reconstructs visual stimuli through top-down pathway, and higher-order cortex reconstructs V1 activity. Experiments also found that neural representations are generated in a top-down cascade during visual imagination. What code does V1 provide higher-order cortex to reconstruct or simulate to improve perception or imaginative creativity? What unsupervised learning principles shape V1 for reconstructing stimuli so that V1 activity eigenspectrum is power-law with close-to-1 exponent? Using computational models, we reveal that reconstructing the activities of V1 complex cells facilitate higher-order cortex to form representations smooth to shape morphing of stimuli, improving perception and creativity. Power-law eigenspectrum with close-to-1 exponent results from the constraints of sparseness and temporal slowness when V1 is reconstructing stimuli, at a sparseness strength that best whitens V1 code and makes the exponent most insensitive to slowness strength. Our results provide fresh insights into V1 computation.

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