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 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.

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 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.

Selective effects of aging on simple and complex cells in primary visual cortex of rhesus monkeys

2012 ◽  
Vol 1470 ◽  
pp. 17-23 ◽  
Author(s):  
Zhen Liang ◽  
Hongxin Li ◽  
Yun Yang ◽  
Guangxing Li ◽  
Yong Tang ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Rhesus Monkeys ◽  
Complex Cells ◽  
Simple And Complex Cells ◽  
Effects Of Aging ◽  
Selective Effects

Learning Invariance from Natural Images Inspired by Observations in the Primary Visual Cortex

2012 ◽  
Vol 24 (5) ◽  
pp. 1271-1296 ◽  
Author(s):  
Michael Teichmann ◽  
Jan Wiltschut ◽  
Fred Hamker
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Hebbian Learning ◽  
Natural Images ◽  
Complex Cells ◽  
Cell Responses ◽  
Feature Detectors ◽  
Learning Rules ◽  
Static Images ◽  
Good Interpretation

The human visual system has the remarkable ability to largely recognize objects invariant of their position, rotation, and scale. A good interpretation of neurobiological findings involves a computational model that simulates signal processing of the visual cortex. In part, this is likely achieved step by step from early to late areas of visual perception. While several algorithms have been proposed for learning feature detectors, only few studies at hand cover the issue of biologically plausible learning of such invariance. In this study, a set of Hebbian learning rules based on calcium dynamics and homeostatic regulations of single neurons is proposed. Their performance is verified within a simple model of the primary visual cortex to learn so-called complex cells, based on a sequence of static images. As a result, the learned complex-cell responses are largely invariant to phase and position.

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 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 Mechanisms of Feature Selectivity and Invariance in Primary Visual Cortex

2020 ◽  
Author(s):  
Ali Almasi ◽  
Hamish Meffin ◽  
Shaun L. Cloherty ◽  
Yan Wong ◽  
Molis Yunzab ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Processing ◽  
Object Identification ◽  
Visual Object ◽  
Object Processing ◽  
Complex Cells ◽  
Feature Selectivity ◽  
Visual Object Identification ◽  
Neuronal Computation

AbstractVisual object identification requires both selectivity for specific visual features that are important to the object’s identity and invariance to feature manipulations. For example, a hand can be shifted in position, rotated, or contracted but still be recognised as a hand. How are the competing requirements of selectivity and invariance built into the early stages of visual processing? Typically, cells in the primary visual cortex are classified as either simple or complex. They both show selectivity for edge-orientation but complex cells develop invariance to edge position within the receptive field (spatial phase). Using a data-driven model that extracts the spatial structures and nonlinearities associated with neuronal computation, we show that the balance between selectivity and invariance in complex cells is more diverse than thought. Phase invariance is frequently partial, thus retaining sensitivity to brightness polarity, while invariance to orientation and spatial frequency are more extensive than expected. The invariance arises due to two independent factors: (1) the structure and number of filters and (2) the form of nonlinearities that act upon the filter outputs. Both vary more than previously considered, so primary visual cortex forms an elaborate set of generic feature sensitivities, providing the foundation for more sophisticated object processing.

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