Context-Dependent Modulation of Early Visual Cortical Responses to Numerical and Nonnumerical Magnitudes

2021 ◽  
pp. 1-12
Author(s):  
Joonkoo Park ◽  
Sonia Godbole ◽  
Marty G. Woldorff ◽  
Elizabeth M. Brannon
Keyword(s):  
Visual Processing ◽  
Context Dependency ◽  
Numerical Magnitude ◽  
Continuous Variables ◽  
Processing Stream ◽  
Cortical Responses ◽  
Early Visual Cortex ◽  
Context Dependent ◽  
Visual Cortical ◽  
The Brain

Abstract Whether and how the brain encodes discrete numerical magnitude differently from continuous nonnumerical magnitude is hotly debated. In a previous set of studies, we orthogonally varied numerical (numerosity) and nonnumerical (size and spacing) dimensions of dot arrays and demonstrated a strong modulation of early visual evoked potentials (VEPs) by numerosity and not by nonnumerical dimensions. Although very little is known about the brain's response to systematic changes in continuous dimensions of a dot array, some authors intuit that the visual processing stream must be more sensitive to continuous magnitude information than to numerosity. To address this possibility, we measured VEPs of participants viewing dot arrays that changed exclusively in one nonnumerical magnitude dimension at a time (size or spacing) while holding numerosity constant and compared this to a condition where numerosity was changed while holding size and spacing constant. We found reliable but small neural sensitivity to exclusive changes in size and spacing; however, changing numerosity elicited a much more robust modulation of the VEPs. Together with previous work, these findings suggest that sensitivity to magnitude dimensions in early visual cortex is context dependent: The brain is moderately sensitive to changes in size and spacing when numerosity is held constant, but sensitivity to these continuous variables diminishes to a negligible level when numerosity is allowed to vary at the same time. Neurophysiological explanations for the encoding and context dependency of numerical and nonnumerical magnitudes are proposed within the framework of neuronal normalization.

scholarly journals An encoding model of temporal processing in human visual cortex

2017 ◽  
Author(s):  
Anthony Stigliani ◽  
Brianna Jeska ◽  
Kalanit Grill-Spector
Keyword(s):  
Visual Cortex ◽  
Temporal Processing ◽  
Visual Processing ◽  
Time Varying ◽  
Transient Responses ◽  
Human Visual Cortex ◽  
Cortical Responses ◽  
Neural Computations ◽  
Early Visual Cortex ◽  
The Brain

ABSTRACTHow is temporal information processed in human visual cortex? There is intense debate as to how sustained and transient temporal channels contribute to visual processing beyond V1. Using fMRI, we measured cortical responses to time-varying stimuli, then implemented a novel 2 temporal-channel encoding model to estimate the contributions of each channel. The model predicts cortical responses to time-varying stimuli from milliseconds to seconds and reveals that (i) lateral occipito-temporal regions and peripheral early visual cortex are dominated by transient responses, and (ii) ventral occipito-temporal regions and central early visual cortex are not only driven by both channels, but that transient responses exceed the sustained. These findings resolve an outstanding debate and elucidate temporal processing in human visual cortex. Importantly, this approach has vast implications because it can be applied with fMRI to decipher neural computations in millisecond resolution in any part of the brain.

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.

scholarly journals Defining the Units of Competition: Influences of Perceptual Organization on Competitive Interactions in Human Visual Cortex

2010 ◽  
Vol 22 (11) ◽  
pp. 2417-2426 ◽  
Author(s):  
Stephanie A. McMains ◽  
Sabine Kastner
Keyword(s):  
Visual Cortex ◽  
Perceptual Organization ◽  
Visual Processing ◽  
Perceptual Grouping ◽  
Neural Representation ◽  
Competitive Interactions ◽  
Illusory Figure ◽  
Processing Stream ◽  
Neural Processes ◽  
Early Visual Cortex

Multiple stimuli that are present simultaneously in the visual field compete for neural representation. At the same time, however, multiple stimuli in cluttered scenes also undergo perceptual organization according to certain rules originally defined by the Gestalt psychologists such as similarity or proximity, thereby segmenting scenes into candidate objects. How can these two seemingly orthogonal neural processes that occur early in the visual processing stream be reconciled? One possibility is that competition occurs among perceptual groups rather than at the level of elements within a group. We probed this idea using fMRI by assessing competitive interactions across visual cortex in displays containing varying degrees of perceptual organization or perceptual grouping (Grp). In strong Grp displays, elements were arranged such that either an illusory figure or a group of collinear elements were present, whereas in weak Grp displays the same elements were arranged randomly. Competitive interactions among stimuli were overcome throughout early visual cortex and V4, when elements were grouped regardless of Grp type. Our findings suggest that context-dependent grouping mechanisms and competitive interactions are linked to provide a bottom–up bias toward candidate objects in cluttered scenes.

Neural Activation in the Dorsal Visual Processing Stream is Context-Dependent in Williams Syndrome

2021 ◽  
Vol 89 (9) ◽  
pp. S267
Author(s):  
Katherine M. Soderberg ◽  
Tiffany A. Nash ◽  
Philip D. Kohn ◽  
J. Shane Kippenhan ◽  
Madeline R. Hamborg ◽  
...  
Keyword(s):  
Williams Syndrome ◽  
Visual Processing ◽  
Neural Activation ◽  
Processing Stream ◽  
Context Dependent

scholarly journals Single circuit in V1 capable of switching contexts during movement using VIP population as a switch

2020 ◽  
Author(s):  
Doris Voina ◽  
Stefano Recanatesi ◽  
Brian Hu ◽  
Eric Shea-Brown ◽  
Stefan Mihalas
Keyword(s):  
Visual System ◽  
Visual Processing ◽  
Visual Space ◽  
Cell Types ◽  
Flexible Architecture ◽  
Multiple Cell ◽  
Context Dependent ◽  
Spatio Temporal ◽  
Context Integration ◽  
The Brain

AbstractAs animals adapt to their environments, their brains are tasked with processing stimuli in different sensory contexts. Whether these computations are context dependent or independent, they are all implemented in the same neural tissue. A crucial question is what neural architectures can respond flexibly to a range of stimulus conditions and switch between them. This is a particular case of flexible architecture that permits multiple related computations within a single circuit.Here, we address this question in the specific case of the visual system circuitry, focusing on context integration, defined as the integration of feedforward and surround information across visual space. We show that a biologically inspired microcircuit with multiple inhibitory cell types can switch between visual processing of the static context and the moving context. In our model, the VIP population acts as the switch and modulates the visual circuit through a disinhibitory motif. Moreover, the VIP population is efficient, requiring only a relatively small number of neurons to switch contexts. This circuit eliminates noise in videos by using appropriate lateral connections for contextual spatio-temporal surround modulation, having superior denoising performance compared to circuits where only one context is learned. Our findings shed light on a minimally complex architecture that is capable of switching between two naturalistic contexts using few switching units.Author SummaryThe brain processes information at all times and much of that information is context-dependent. The visual system presents an important example: processing is ongoing, but the context changes dramatically when an animal is still vs. running. How is context-dependent information processing achieved? We take inspiration from recent neurophysiology studies on the role of distinct cell types in primary visual cortex (V1).We find that relatively few “switching units” — akin to the VIP neuron type in V1 in that they turn on and off in the running vs. still context and have connections to and from the main population — is sufficient to drive context dependent image processing. We demonstrate this in a model of feature integration, and in a test of image denoising. The underlying circuit architecture illustrates a concrete computational role for the multiple cell types under increasing study across the brain, and may inspire more flexible neurally inspired computing architectures.

scholarly journals Early cross-modal interactions and adult human visual cortical plasticity revealed by binocular rivalry

2015 ◽  
Author(s):  
Claudia Lunghi
Keyword(s):  
Visual Cortex ◽  
Binocular Rivalry ◽  
Visual Processing ◽  
Cortical Plasticity ◽  
Monocular Deprivation ◽  
Homeostatic Plasticity ◽  
Multisensory Perception ◽  
Modal Interactions ◽  
Early Visual Cortex ◽  
Visual Cortical

In this research binocular rivalry is used as a tool to investigate different aspects of visual and multisensory perception. Several experiments presented here demonstrated that touch specifically interacts with vision during binocular rivalry and that the interaction likely occurs at early stages of visual processing, probably V1 or V2. Another line of research also presented here demonstrated that human adult visual cortex retains an unexpected high degree of experience-dependent plasticity by showing that a brief period of monocular deprivation produced important perceptual consequences on the dynamics of binocular rivalry, reflecting a homeostatic plasticity. In summary, this work shows that binocular rivalry is a powerful tool to investigate different aspects of visual perception and can be used to reveal unexpected properties of early visual cortex.

scholarly journals Transferring and Generalizing Deep-Learning-based Neural Encoding Models across Subjects

2017 ◽  
Author(s):  
Haiguang Wen ◽  
Junxing Shi ◽  
Wei Chen ◽  
Zhongming Liu
Keyword(s):  
Deep Learning ◽  
Learning Models ◽  
Neural Encoding ◽  
Natural Vision ◽  
Cortical Responses ◽  
Single Subjects ◽  
Visual Cortical ◽  
The Relationship ◽  
Cortical Representations ◽  
The Brain

Recent studies have shown the value of using deep learning models for mapping and characterizing how the brain represents and organizes information for natural vision. However, modeling the relationship between deep learning models and the brain (or encoding models), requires measuring cortical responses to large and diverse sets of natural visual stimuli from single subjects. This requirement limits prior studies to few subjects, making it difficult to generalize findings across subjects or for a population. In this study, we developed new methods to transfer and generalize encoding models across subjects. To train encoding models specific to a subject, the models trained for other subjects were used as the prior models and were refined efficiently using Bayesian inference with a limited amount of data from the specific subject. To train encoding models for a population, the models were progressively trained and updated with incremental data from different subjects. For the proof of principle, we applied these methods to functional magnetic resonance imaging (fMRI) data from three subjects watching tens of hours of naturalistic videos, while deep residual neural network driven by image recognition was used to model the visual cortical processing. Results demonstrate that the methods developed herein provide an efficient and effective strategy to establish subject-specific or populationwide predictive models of cortical representations of high-dimensional and hierarchical visual features.

Ultrastructure of developing visual cortical synapses in the cat superior colliculus

1991 ◽  
Vol 49 ◽  
pp. 156-157
Author(s):  
Caroline A. Miller ◽  
Laura L. Bruce
Keyword(s):  
Superior Colliculus ◽  
Phosphate Buffer ◽  
Receptive Fields ◽  
Visual Cortical Area ◽  
Cortical Synapses ◽  
Visual Cortical ◽  
And Function ◽  
Phosphate Buffered Saline ◽  
The Brain ◽  
Cat Superior Colliculus

The first visual cortical axons arrive in the cat superior colliculus by the time of birth. Adultlike receptive fields develop slowly over several weeks following birth. The developing cortical axons go through a sequence of changes before acquiring their adultlike morphology and function. To determine how these axons interact with neurons in the colliculus, cortico-collicular axons were labeled with biocytin (an anterograde neuronal tracer) and studied with electron microscopy.Deeply anesthetized animals received 200-500 nl injections of biocytin (Sigma; 5% in phosphate buffer) in the lateral suprasylvian visual cortical area. After a 24 hr survival time, the animals were deeply anesthetized and perfused with 0.9% phosphate buffered saline followed by fixation with a solution of 1.25% glutaraldehyde and 1.0% paraformaldehyde in 0.1M phosphate buffer. The brain was sectioned transversely on a vibratome at 50 μm. The tissue was processed immediately to visualize the biocytin.

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