Higher-Order Visual Cortex Shows Stronger Neural Correlates of Visual and Multisensory Detection Behavior Compared to Primary Visual Cortex

2019 ◽  
Author(s):  
Guido Meijer ◽  
Pietro Marchesi ◽  
Jorge Mejias ◽  
Jorrit Montijn ◽  
Carien Lansink ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Neural Correlates ◽  
Higher Order

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 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 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.

scholarly journals Neural Correlates of Multisensory Detection Behavior: Comparison of Primary and Higher-Order Visual Cortex

Cell Reports ◽  
2020 ◽  
Vol 31 (6) ◽  
pp. 107636 ◽  
Author(s):  
Guido T. Meijer ◽  
Pietro Marchesi ◽  
Jorge F. Mejias ◽  
Jorrit S. Montijn ◽  
Carien S. Lansink ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Neural Correlates ◽  
Higher Order

scholarly journals Feedforward and Recurrent Processing in Scene Segmentation: Electroencephalography and Functional Magnetic Resonance Imaging

2008 ◽  
Vol 20 (11) ◽  
pp. 2097-2109 ◽  
Author(s):  
H. Steven Scholte ◽  
Jacob Jolij ◽  
Johannes J. Fahrenfort ◽  
Victor A. F. Lamme
Keyword(s):  
Magnetic Resonance Imaging ◽  
Visual Cortex ◽  
Magnetic Resonance ◽  
Primary Visual Cortex ◽  
Surface Segregation ◽  
Boundary Detection ◽  
Neural Correlates ◽  
Scene Segmentation ◽  
Resonance Imaging ◽  
Visual Areas

In texture segregation, an example of scene segmentation, we can discern two different processes: texture boundary detection and subsequent surface segregation [Lamme, V. A. F., Rodriguez-Rodriguez, V., & Spekreijse, H. Separate processing dynamics for texture elements, boundaries and surfaces in primary visual cortex of the macaque monkey. Cerebral Cortex, 9, 406–413, 1999]. Neural correlates of texture boundary detection have been found in monkey V1 [Sillito, A. M., Grieve, K. L., Jones, H. E., Cudeiro, J., & Davis, J. Visual cortical mechanisms detecting focal orientation discontinuities. Nature, 378, 492–496, 1995; Grosof, D. H., Shapley, R. M., & Hawken, M. J. Macaque-V1 neurons can signal illusory contours. Nature, 365, 550–552, 1993], but whether surface segregation occurs in monkey V1 [Rossi, A. F., Desimone, R., & Ungerleider, L. G. Contextual modulation in primary visual cortex of macaques. Journal of Neuroscience, 21, 1698–1709, 2001; Lamme, V. A. F. The neurophysiology of figure ground segregation in primary visual-cortex. Journal of Neuroscience, 15, 1605–1615, 1995], and whether boundary detection or surface segregation signals can also be measured in human V1, is more controversial [Kastner, S., De Weerd, P., & Ungerleider, L. G. Texture segregation in the human visual cortex: A functional MRI study. Journal of Neurophysiology, 83, 2453–2457, 2000]. Here we present electroencephalography (EEG) and functional magnetic resonance imaging data that have been recorded with a paradigm that makes it possible to differentiate between boundary detection and scene segmentation in humans. In this way, we were able to show with EEG that neural correlates of texture boundary detection are first present in the early visual cortex around 92 msec and then spread toward the parietal and temporal lobes. Correlates of surface segregation first appear in temporal areas (around 112 msec) and from there appear to spread to parietal, and back to occipital areas. After 208 msec, correlates of surface segregation and boundary detection also appear in more frontal areas. Blood oxygenation level-dependent magnetic resonance imaging results show correlates of boundary detection and surface segregation in all early visual areas including V1. We conclude that texture boundaries are detected in a feedforward fashion and are represented at increasing latencies in higher visual areas. Surface segregation, on the other hand, is represented in “reverse hierarchical” fashion and seems to arise from feedback signals toward early visual areas such as V1.

scholarly journals Higher-order visual areas broaden stimulus responsiveness in mouse primary visual cortex

2021 ◽  
Author(s):  
Matthijs N. oude Lohuis ◽  
Alexis Cerván Cantón ◽  
Cyriel M. A. Pennartz ◽  
Umberto Olcese
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Processing ◽  
Higher Order ◽  
The Past ◽  
Early Visual Processing ◽  
Visual Areas ◽  
Sensory Evoked ◽  
Prevailing View

SummaryOver the past few years, the various areas that surround the primary visual cortex in the mouse have been associated with many functions, ranging from higher-order visual processing to decision making. Recently, some studies have shown that higher-order visual areas influence the activity of the primary visual cortex, refining its processing capabilities. Here we studied how in vivo optogenetic inactivation of two higher-order visual areas with different functional properties affects responses evoked by moving bars in the primary visual cortex. In contrast with the prevailing view, our results demonstrate that distinct higher-order visual areas similarly modulate early visual processing. In particular, these areas broaden stimulus responsiveness in the primary visual cortex, by amplifying sensory-evoked responses for stimuli not moving along the orientation preferred by individual neurons. Thus, feedback from higher-order visual areas amplifies V1 responses to non-preferred stimuli, which may aid their detection.

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