scholarly journals Evidence of High-Detail Perceptual Modeling in the Visual Cortex

2021 ◽  
Vol 2020 (01) ◽  
pp. 0120
Author(s):  
Terry Bollinger
Keyword(s):  
Visual Cortex ◽  
Current Model ◽  
Load Reduction ◽  
Minimal Processing ◽  
Time Model ◽  
Sensory Inputs ◽  
Physical Motion ◽  
Long Duration ◽  
Direct Corollary ◽  
The Brain

This informal but well-referenced description of an afterimage experiment called Ghost Tap provides persuasive and easily reproducible evidence that the visual cortex plays a significant role in certain classes of long-duration visual afterimages. Subjects of the experiment literally cannot discern the difference between the afterimage and reality, resulting in easy startling of the subjects when physical motion in the room no longer matches the persuasive afterimages they are perceiving. Anecdotal examples of less extreme versions of the same effect suggest that the Ghost Tap effect has, over centuries, intentionally and unintentionally helped persuade people of the existence of nominally “supernatural” effects that are just persuasive long-duration afterimages. While this description is informal, the easy reproducibility of the Ghost Tap makes it a good candidate for more precise and quantitative studies. One theory why Ghost Tap exists is that it is part of load reduction and speed enhancement strategy to compensate for the slow processing speeds of neurons. Maintaining a dynamic and predictive real-time model of likely sensory inputs from the external world would enable the brain to discard quickly and with minimal processing any sensory inputs that fall within the predictive tolerance limits to the current model state. A perceptive load reduction interpretation of the Ghost Tap argues that the ability of the brain to support dreaming in vivid detail is likely a direct corollary of its ability to create dream-like waking states for faster and more efficient processing of large sensory loads. If the brain regularly uses dream-like waking states to reduce data, more study of effects like Ghost Tap might help explain the frequency of pathologies in which perception becomes disconnected from reality.

Relation Between Level of Prefrontal Activity and Subject's Performance

1999 ◽  
Vol 13 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Laurence Casini ◽  
Françoise Macar ◽  
Marie-Hélène Giard
Keyword(s):  
Brain Activity ◽  
Sensory Information ◽  
Practice Phase ◽  
Trained Subjects ◽  
Anagram Task ◽  
Economic Information ◽  
Long Duration ◽  
Level Of Activity ◽  
The Brain ◽  
Processing Mechanism

Abstract The experiment reported here was aimed at determining whether the level of brain activity can be related to performance in trained subjects. Two tasks were compared: a temporal and a linguistic task. An array of four letters appeared on a screen. In the temporal task, subjects had to decide whether the letters remained on the screen for a short or a long duration as learned in a practice phase. In the linguistic task, they had to determine whether the four letters could form a word or not (anagram task). These tasks allowed us to compare the level of brain activity obtained in correct and incorrect responses. The current density measures recorded over prefrontal areas showed a relationship between the performance and the level of activity in the temporal task only. The level of activity obtained with correct responses was lower than that obtained with incorrect responses. This suggests that a good temporal performance could be the result of an efficacious, but economic, information-processing mechanism in the brain. In addition, the absence of this relation in the anagram task results in the question of whether this relation is specific to the processing of sensory information only.

scholarly journals Decoding the future from past experience: learning shapes predictions in early visual cortex

2015 ◽  
Vol 113 (9) ◽  
pp. 3159-3171 ◽  
Author(s):  
Caroline D. B. Luft ◽  
Alan Meeson ◽  
Andrew E. Welchman ◽  
Zoe Kourtzi
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Sequence Structure ◽  
Neural Populations ◽  
Early Visual Cortex ◽  
Temporal Structures ◽  
Future Events ◽  
Sensory Predictions ◽  
Actual Stimulus ◽  
The Brain

Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex.

scholarly journals Homeostatic plasticity mechanisms in mouse V1

2017 ◽  
Vol 372 (1715) ◽  
pp. 20160504 ◽  
Author(s):  
Megumi Kaneko ◽  
Michael P. Stryker
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Neuronal Activity ◽  
Dynamic Range ◽  
Ocular Dominance ◽  
Homeostatic Plasticity ◽  
Ocular Dominance Plasticity ◽  
The Brain

Mechanisms thought of as homeostatic must exist to maintain neuronal activity in the brain within the dynamic range in which neurons can signal. Several distinct mechanisms have been demonstrated experimentally. Three mechanisms that act to restore levels of activity in the primary visual cortex of mice after occlusion and restoration of vision in one eye, which give rise to the phenomenon of ocular dominance plasticity, are discussed. The existence of different mechanisms raises the issue of how these mechanisms operate together to converge on the same set points of activity. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

scholarly journals Cortical state transitions and stimulus response evolve along stiff and sloppy parameter dimensions, respectively

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Adrian Ponce-Alvarez ◽  
Gabriela Mochol ◽  
Ainhoa Hermoso-Mendizabal ◽  
Jaime de la Rocha ◽  
Gustavo Deco
Keyword(s):  
Collective Behavior ◽  
Neural Population ◽  
State Transitions ◽  
Population Activity ◽  
Sensory Inputs ◽  
Stimulus Response ◽  
Spontaneous Neuronal Activity ◽  
The Brain ◽  
Shed Light ◽  
Neural Population Activity

Previous research showed that spontaneous neuronal activity presents sloppiness: the collective behavior is strongly determined by a small number of parameter combinations, defined as ‘stiff’ dimensions, while it is insensitive to many others (‘sloppy’ dimensions). Here, we analyzed neural population activity from the auditory cortex of anesthetized rats while the brain spontaneously transited through different synchronized and desynchronized states and intermittently received sensory inputs. We showed that cortical state transitions were determined by changes in stiff parameters associated with the activity of a core of neurons with low responses to stimuli and high centrality within the observed network. In contrast, stimulus-evoked responses evolved along sloppy dimensions associated with the activity of neurons with low centrality and displaying large ongoing and stimulus-evoked fluctuations without affecting the integrity of the network. Our results shed light on the interplay among stability, flexibility, and responsiveness of neuronal collective dynamics during intrinsic and induced activity.

scholarly journals Cortical state transitions and stimulus response evolve along stiff and sloppy parameter dimensions, respectively

2019 ◽  
Author(s):  
Adrián Ponce-Alvarez ◽  
Gabriela Mochol ◽  
Ainhoa Hermoso-Mendizabal ◽  
Jaime de la Rocha ◽  
Gustavo Deco
Keyword(s):  
Collective Behavior ◽  
Neural Population ◽  
State Transitions ◽  
Population Activity ◽  
Sensory Inputs ◽  
Stimulus Response ◽  
Spontaneous Neuronal Activity ◽  
The Brain ◽  
Shed Light ◽  
Neural Population Activity

SummaryPrevious research showed that spontaneous neuronal activity presents sloppiness: the collective behavior is strongly determined by a small number of parameter combinations, defined as “stiff” dimensions, while it is insensitive to many others (“sloppy” dimensions). Here, we analyzed neural population activity from the auditory cortex of anesthetized rats while the brain spontaneously transited through different synchronized and desynchronized states and intermittently received sensory inputs. We showed that cortical state transitions were determined by changes in stiff parameters associated with the activity of a core of neurons with low responses to stimuli and high centrality within the observed network. In contrast, stimulus-evoked responses evolved along sloppy dimensions associated with the activity of neurons with low centrality and displaying large ongoing and stimulus-evoked fluctuations without affecting the integrity of the network. Our results shed light on the interplay among stability, flexibility, and responsiveness of neuronal collective dynamics during intrinsic and induced activity.

scholarly journals Task dependence of neural representations of global scene properties but not scene categories in the prefrontal cortex

2020 ◽  
Author(s):  
Yaelan Jung ◽  
Dirk B. Walther
Keyword(s):  
Prefrontal Cortex ◽  
Visual Cortex ◽  
Scene Perception ◽  
Sound Level ◽  
Task Context ◽  
Complex Scene ◽  
Neural Representations ◽  
Task Conditions ◽  
Scene Content ◽  
The Brain

AbstractNatural scenes deliver rich sensory information about the world. Decades of research has shown that the scene-selective network in the visual cortex represents various aspects of scenes. It is, however, unknown how such complex scene information is processed beyond the visual cortex, such as in the prefrontal cortex. It is also unknown how task context impacts the process of scene perception, modulating which scene content is represented in the brain. In this study, we investigate these questions using scene images from four natural scene categories, which also depict two types of global scene properties, temperature (warm or cold), and sound-level (noisy or quiet). A group of healthy human subjects from both sexes participated in the present study using fMRI. In the study, participants viewed scene images under two different task conditions; temperature judgment and sound-level judgment. We analyzed how different scene attributes (scene categories, temperature, and sound-level information) are represented across the brain under these task conditions. Our findings show that global scene properties are only represented in the brain, especially in the prefrontal cortex, when they are task-relevant. However, scene categories are represented in the brain, in both the parahippocampal place area and the prefrontal cortex, regardless of task context. These findings suggest that the prefrontal cortex selectively represents scene content according to task demands, but this task selectivity depends on the types of scene content; task modulates neural representations of global scene properties but not of scene categories.

Predictability-dependent encoding of statistical regularities in the early visual cortex

2022 ◽  
Author(s):  
Andrea Kóbor ◽  
Karolina Janacsek ◽  
Petra Hermann ◽  
Zsofia Zavecz ◽  
Vera Varga ◽  
...  
Keyword(s):  
Visual Cortex ◽  
The Other ◽  
Block Type ◽  
Statistical Knowledge ◽  
Early Visual Cortex ◽  
Statistical Regularities ◽  
Visual Cortical ◽  
Increased Activity ◽  
The Brain ◽  
Subsequent Task

Previous research recognized that humans could extract statistical regularities of the environment to automatically predict upcoming events. However, it has remained unexplored how the brain encodes the distribution of statistical regularities if it continuously changes. To investigate this question, we devised an fMRI paradigm where participants (N = 32) completed a visual four-choice reaction time (RT) task consisting of statistical regularities. Two types of blocks involving the same perceptual elements alternated with one another throughout the task: While the distribution of statistical regularities was predictable in one block type, it was unpredictable in the other. Participants were unaware of the presence of statistical regularities and of their changing distribution across the subsequent task blocks. Based on the RT results, although statistical regularities were processed similarly in both the predictable and unpredictable blocks, participants acquired less statistical knowledge in the unpredictable as compared with the predictable blocks. Whole-brain random-effects analyses showed increased activity in the early visual cortex and decreased activity in the precuneus for the predictable as compared with the unpredictable blocks. Therefore, the actual predictability of statistical regularities is likely to be represented already at the early stages of visual cortical processing. However, decreased precuneus activity suggests that these representations are imperfectly updated to track the multiple shifts in predictability throughout the task. The results also highlight that the processing of statistical regularities in a changing environment could be habitual.

Text, Images, and Video Analytics for Fog Computing

2018 ◽  
pp. 390-410 ◽  
Author(s):  
A. Jayanthiladevi ◽  
S. Murugan ◽  
K. Manivel
Keyword(s):  
Big Data ◽  
Visual Cortex ◽  
Fog Computing ◽  
Unstructured Data ◽  
Video Analytics ◽  
Color Vector ◽  
Physical Features ◽  
Vector Images ◽  
Text Images ◽  
The Brain

Today, images and image sequences (videos) make up about 80% of all corporate and public unstructured big data. As growth of unstructured data increases, analytical systems must assimilate and interpret images and videos as well as they interpret structured data such as text and numbers. An image is a set of signals sensed by the human eye and processed by the visual cortex in the brain creating a vivid experience of a scene that is instantly associated with concepts and objects previously perceived and recorded in one's memory. To a computer, images are either a raster image or a vector image. Simply put, raster images are a sequence of pixels with discreet numerical values for color; vector images are a set of color-annotated polygons. To perform analytics on images or videos, the geometric encoding must be transformed into constructs depicting physical features, objects and movement represented by the image or video. This chapter explores text, images, and video analytics in fog computing.

What Is Electroconvulsive Therapy?

2010 ◽  
Author(s):  
Max Fink MD
Keyword(s):  
Emotional Disorders ◽  
Electroconvulsive Therapy ◽  
The Elderly ◽  
The Body ◽  
Electroconvulsive Treatment ◽  
Single Episode ◽  
Psychotropic Medicines ◽  
Long Duration ◽  
Motor Activities ◽  
The Brain

Electroconvulsive therapy (ECT) is an effective medical treatment for severe and persistent psychiatric disorders. It relieves de pressed mood and thoughts of suicide, as well as mania, acute psychosis, delirium, and stupor. It is usually applied when medications have given limited relief or their side effects are intolerable. Electroconvulsive therapy is similar to a surgical treatment. It requires the specialized skills of a psychiatrist, an anesthesiologist, and nurses. The patient receives a short-acting anesthetic. While the patient is asleep, the physician, following a prescribed procedure, induces an epileptic seizure in the brain. By making sure that the patient’s lungs are filled with oxygen, the physician precludes the gasping and difficult breathing that accompany a spontaneous epileptic fit. By relaxing the patient’s muscles with chemicals and by inserting a mouth guard (not unlike those used in sports), the physician prevents the tongue biting, fractures, and injuries that occasionally occur in epilepsy. The patient is asleep, and so experiences neither the painful effects of the stimulus nor the discomforts of the seizure. The physiological functions of the body, such as breathing, heart rate, blood pressure, blood oxygen concentration, and degree of motor relaxation, are monitored, and anything out of the ordinary is immediately treated. Electroconvulsive therapy relieves symptoms more quickly than do psychotropic drugs. A common course of ECT consists of two or three treatments a week for two to seven weeks. To sustain the recovery, weekly or biweekly continuation treatments, either ECT or medications, are often administered for four to six months. If the illness recurs, ECT is prescribed for longer periods. The duration and course of ECT are similar to those of the psychotropic medicines frequently used for the same conditions. Electroconvulsive therapy has been used safely to treat emotional disorders in patients of all ages, from children to the elderly, in people with debilitating physical illnesses, and in pregnant women. Emotional disorders may be of short or long duration; they may be manifest as a single episode or as a recurring event. Electroconvulsive treatment is an option when the emotional disorder is acute in onset; when changes in mood, thought, and motor activities are pronounced; when the cause is believed to be biochemical or physiological; when the condition is so severe that it interferes with the patient’s daily life; or when other treatments have failed.

Decoding the sense of smell: understanding the structural organization within the brain

2020 ◽  
Vol 2 (7) ◽  
pp. 4-9
Author(s):  
Shripriya Singh
Keyword(s):  
Neural Activity ◽  
Structural Organization ◽  
Piriform Cortex ◽  
Molecular Structures ◽  
The Other ◽  
Multiphoton Imaging ◽  
Sensory Inputs ◽  
Correlated Patterns ◽  
Olfactory Sense ◽  
The Brain

The olfactory sense is a potent sensory tool which helps us perceive our environment much better. However, smells despite being similar have different impacts on individuals. What makes one odor categorically different from the other and why do people have a unique and personalized experience with smell is an answer that needs to be addressed. In the present article we have discussed the research in which neuroscientists have decoded and described how the relationships between different odors are encoded in the brain. How the brain transforms information about odor chemistry into the perception of smell is a major highlight of this publication. Carefully selected odors with defined molecular structures were delivered in mice and the neural activity was analyzed. It was observed that neuronal representations of smell in the cortex reflected chemical similarities between odors, thus allowing the brain to categorize scents. The study has employed chemo informatics and multiphoton imaging in the mouse to demonstrate both the piriform cortex and its sensory inputs from the olfactory bulb represent chemical odor relationships through correlated patterns of activity. The research has given us cues in the direction of how the brain translates odor chemistry into neurochemistry and eventually perception of smell.

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