scholarly journals Wakefulness suppresses retinal wave-related neural activity in visual cortex

2017 ◽  
Vol 118 (2) ◽  
pp. 1190-1197 ◽  
Author(s):  
Didhiti Mukherjee ◽  
Alex J. Yonk ◽  
Greta Sokoloff ◽  
Mark S. Blumberg
Keyword(s):  
Neural Networks ◽  
Visual Cortex ◽  
Environmental Factors ◽  
Visual System ◽  
Early Development ◽  
Neural Activity ◽  
Functional Role ◽  
Retinal Waves ◽  
Infant Rats ◽  
Retinal Wave

By recording in visual cortex in unanesthetized infant rats, we show that neural activity attributable to retinal waves is specifically suppressed when pups spontaneously awaken or are experimentally aroused. These findings suggest that the relatively abundant sleep of early development plays a permissive functional role for the visual system. It follows, then, that biological or environmental factors that disrupt sleep may interfere with the development of these neural networks.

scholarly journals Blindsight and Unconscious Vision: What They Teach Us about the Human Visual System

2016 ◽  
Vol 23 (5) ◽  
pp. 529-541 ◽  
Author(s):  
Sara Ajina ◽  
Holly Bridge
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Neural Activity ◽  
Human Visual System ◽  
Visual Information ◽  
Visual Loss ◽  
Physiological Conditions ◽  
The World ◽  
The Brain

Damage to the primary visual cortex removes the major input from the eyes to the brain, causing significant visual loss as patients are unable to perceive the side of the world contralateral to the damage. Some patients, however, retain the ability to detect visual information within this blind region; this is known as blindsight. By studying the visual pathways that underlie this residual vision in patients, we can uncover additional aspects of the human visual system that likely contribute to normal visual function but cannot be revealed under physiological conditions. In this review, we discuss the residual abilities and neural activity that have been described in blindsight and the implications of these findings for understanding the intact system.

scholarly journals Ten days of darkness causes temporary blindness during an early critical period in felines

2015 ◽  
Vol 282 (1803) ◽  
pp. 20142756 ◽  
Author(s):  
Donald E. Mitchell ◽  
Nathan A. Crowder ◽  
Kaitlyn Holman ◽  
Matthew Smithen ◽  
Kevin R. Duffy
Keyword(s):  
Visual Acuity ◽  
Visual Cortex ◽  
Visual System ◽  
Neural Activity ◽  
Critical Period ◽  
Time Course ◽  
System Development ◽  
Monocular Deprivation ◽  
Relative Ease ◽  
Visual System Development

Extended periods of darkness have long been used to study how the mammalian visual system develops in the absence of any instruction from vision. Because of the relative ease of implementation of darkness as a means to eliminate visually driven neural activity, it has usually been imposed earlier in life and for much longer periods than was the case for other manipulations of the early visual input used for study of their influences on visual system development. Recently, it was shown that following a very brief (10 days) period of darkness imposed at five weeks of age, kittens emerged blind. Although vision as assessed by measurements of visual acuity eventually recovered, the time course was very slow as it took seven weeks for visual acuity to attain normal levels. Here, we document the critical period of this remarkable vulnerability to the effects of short periods of darkness by imposing 10 days of darkness on nine normal kittens at progressively later ages. Results indicate that the period of susceptibility to darkness extends only to about 10 weeks of age, which is substantially shorter than the critical period for the effects of monocular deprivation in the primary visual cortex, which extends beyond six months of age.

scholarly journals Face recognition depends on specialized mechanisms tuned to view-invariant facial features: Insights from deep neural networks optimized for face or object recognition

2020 ◽  
Author(s):  
Naphtali Abudarham ◽  
Galit Yovel
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Visual Cortex ◽  
Object Recognition ◽  
Visual System ◽  
Functional Organization ◽  
Computational Modelling ◽  
General System ◽  
Hierarchical Architecture ◽  
Facial Features

SummaryFaces are processed by specialized neural mechanisms in high-level visual cortex. How does this divergence to a face-specific and an object-general system contribute to face recognition? Recent advances in machine face recognition together with our understanding of how humans recognize faces enable us to address this fundamental question. We hypothesized that face recognition depends on a face-selective system that is tuned to view-invariant facial features, which cannot be accomplished by an object-general system. To test this hypothesis, we used deep convolutional neural networks (DCNNs) that were optimized for face or object recognition. Consistent with the hierarchical architecture of the visual system, results show that a human-like, view-invariant face representation emerges only at higher layers of a face-trained but not the object-trained neural network. Thus, by combining human psychophysics and computational modelling of the visual system, we revealed how the functional organization of the visual cortex may contribute to recognition.

Intensity and structure of visually evoked neural activity: Rivals in modelling a visual system

1977 ◽  
Vol 17 (1) ◽  
pp. 29-35 ◽  
Author(s):  
H.A.K. Mastebroek ◽  
W.H. Zaagman ◽  
J.W. Kuiper
Keyword(s):  
Visual System ◽  
Neural Activity

Man, Monkey, and Blindsight

1998 ◽  
Vol 4 (4) ◽  
pp. 227-230 ◽  
Author(s):  
Tirin Moore ◽  
Hillary R. Rodman ◽  
Charles G. Gross
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Visual Function ◽  
Forced Choice ◽  
Neural Mechanisms ◽  
Residual Vision ◽  
Choice Testing

The visual function that survives damage to the primary visual cortex (V1) in humans is often unaccompanied by awareness. This type of residual vision, called “blindsight,” has raised considerable interest because it implies a separation of conscious from unconscious vision mechanisms. The monkey visual system has proven to be a useful model in elucidating the possible neural mechanisms of residual vision and blindsight in humans. Clear similarities, however, between the phenomenology of human and monkey residual vision have only recently become evident. This article summarizes parallels between residual vision in monkeys and humans with damage to V1. These parallels Include the tendency of the remaining vision to require forced-choice testing and the fact that more robust residual vision remains when V1 damage is sustained early in life. NEUROSCIENTIST 4:227–230

The interplay of genotype and environment in the development of fear and anxiety

e-Neuroforum ◽  
2013 ◽  
Vol 19 (3) ◽  
Author(s):  
N. Sachser ◽  
K.-P. Lesch
Keyword(s):  
Individual Differences ◽  
Environmental Factors ◽  
Anxiety Disorders ◽  
Candidate Gene ◽  
Early Development ◽  
Genetic Variants ◽  
External Influences ◽  
Postnatal Environment ◽  
Fear And Anxiety

AbstractIndividual differences in fear, anxiety, and the etiology of anxiety disorders develop dur­ing ontogeny. They are due to both genet­ic and environmental factors. With regard to the role of the environment, the organism is most susceptible to external influences dur­ing early development. Accordingly, stressors that impinge on the maternal organism dur­ing pregnancy evoke high levels of anxiety in the offspring later in life, as does an adverse early postnatal environment. However, anxi­ety-related circuits in the central nervous sys­tem retain their plasticity in adulthood, i.e., levels of anxiety can also be modified by ex­perience across the entire successive lifespan. Notably, the effects of external stressors on the individual’s level of anxiety are modulat­ed by genotype. Such genotype-by-environ­ment interactions are particularly well stud­ied in relation to genetic variants that modu­late the function of the serotonin transport­er. Thus, this review focuses on this candidate gene to elucidate the interplay of genotype and environment in the development of fear and anxiety.

scholarly journals Potassium diffusive coupling in neural networks

2010 ◽  
Vol 365 (1551) ◽  
pp. 2347-2362 ◽  
Author(s):  
Dominique M. Durand ◽  
Eun-Hyoung Park ◽  
Alicia L. Jensen
Keyword(s):  
Neural Networks ◽  
Neural Activity ◽  
Neuronal Excitability ◽  
Epileptiform Activity ◽  
Lateral Diffusion ◽  
Normal Activity ◽  
Ionic Concentration ◽  
Extracellular Potassium ◽  
Potassium Accumulation ◽  
Diffusive Coupling

Conventional neural networks are characterized by many neurons coupled together through synapses. The activity, synchronization, plasticity and excitability of the network are then controlled by its synaptic connectivity. Neurons are surrounded by an extracellular space whereby fluctuations in specific ionic concentration can modulate neuronal excitability. Extracellular concentrations of potassium ([K + ] o ) can generate neuronal hyperexcitability. Yet, after many years of research, it is still unknown whether an elevation of potassium is the cause or the result of the generation, propagation and synchronization of epileptiform activity. An elevation of potassium in neural tissue can be characterized by dispersion (global elevation of potassium) and lateral diffusion (local spatial gradients). Both experimental and computational studies have shown that lateral diffusion is involved in the generation and the propagation of neural activity in diffusively coupled networks. Therefore, diffusion-based coupling by potassium can play an important role in neural networks and it is reviewed in four sections. Section 2 shows that potassium diffusion is responsible for the synchronization of activity across a mechanical cut in the tissue. A computer model of diffusive coupling shows that potassium diffusion can mediate communication between cells and generate abnormal and/or periodic activity in small (§3) and in large networks of cells (§4). Finally, in §5, a study of the role of extracellular potassium in the propagation of axonal signals shows that elevated potassium concentration can block the propagation of neural activity in axonal pathways. Taken together, these results indicate that potassium accumulation and diffusion can interfere with normal activity and generate abnormal activity in neural networks.

scholarly journals Visual search for object categories is predicted by the representational architecture of high-level visual cortex

2017 ◽  
Vol 117 (1) ◽  
pp. 388-402 ◽  
Author(s):  
Michael A. Cohen ◽  
George A. Alvarez ◽  
Ken Nakayama ◽  
Talia Konkle
Keyword(s):  
Visual Cortex ◽  
Visual Search ◽  
Visual System ◽  
Visual Processing ◽  
Search Task ◽  
Visual Search Task ◽  
Visual Object ◽  
Neural Responses ◽  
Object Categories ◽  
High Level

Visual search is a ubiquitous visual behavior, and efficient search is essential for survival. Different cognitive models have explained the speed and accuracy of search based either on the dynamics of attention or on similarity of item representations. Here, we examined the extent to which performance on a visual search task can be predicted from the stable representational architecture of the visual system, independent of attentional dynamics. Participants performed a visual search task with 28 conditions reflecting different pairs of categories (e.g., searching for a face among cars, body among hammers, etc.). The time it took participants to find the target item varied as a function of category combination. In a separate group of participants, we measured the neural responses to these object categories when items were presented in isolation. Using representational similarity analysis, we then examined whether the similarity of neural responses across different subdivisions of the visual system had the requisite structure needed to predict visual search performance. Overall, we found strong brain/behavior correlations across most of the higher-level visual system, including both the ventral and dorsal pathways when considering both macroscale sectors as well as smaller mesoscale regions. These results suggest that visual search for real-world object categories is well predicted by the stable, task-independent architecture of the visual system. NEW & NOTEWORTHY Here, we ask which neural regions have neural response patterns that correlate with behavioral performance in a visual processing task. We found that the representational structure across all of high-level visual cortex has the requisite structure to predict behavior. Furthermore, when directly comparing different neural regions, we found that they all had highly similar category-level representational structures. These results point to a ubiquitous and uniform representational structure in high-level visual cortex underlying visual object processing.

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