The response dynamics of primate visual cortical neurons to simulated optical blur

2009 ◽  
Vol 26 (4) ◽  
pp. 411-420 ◽  
Author(s):  
MICHAEL L. RISNER ◽  
TIMOTHY J. GAWNE
Keyword(s):  
Cortical Neurons ◽  
Receptive Fields ◽  
Luminance Contrast ◽  
Form Perception ◽  
Response Dynamics ◽  
V1 Neurons ◽  
Spatial Frequencies ◽  
Wide Range ◽  
Optical Blur ◽  
Visual Cortical

AbstractNeurons in visual cortical area V1 typically respond well to lines or edges of specific orientations. There have been many studies investigating how the responses of these neurons to an oriented edge are affected by changes in luminance contrast. However, in natural images, edges vary not only in contrast but also in the degree of blur, both because of changes in focus and also because shadows are not sharp. The effect of blur on the response dynamics of visual cortical neurons has not been explored. We presented luminance-defined single edges in the receptive fields of parafoveal (1–6 deg eccentric) V1 neurons of two macaque monkeys trained to fixate a spot of light. We varied the width of the blurred region of the edge stimuli up to 0.36 deg of visual angle. Even though the neurons responded robustly to stimuli that only contained high spatial frequencies and 0.36 deg is much larger than the limits of acuity at this eccentricity, changing the degree of blur had minimal effect on the responses of these neurons to the edge. Primates need to measure blur at the fovea to evaluate image quality and control accommodation, but this might only involve a specialist subpopulation of neurons. If visual cortical neurons in general responded differently to sharp and blurred stimuli, then this could provide a cue for form perception, for example, by helping to disambiguate the luminance edges created by real objects from those created by shadows. On the other hand, it might be important to avoid the distraction of changing blur as objects move in and out of the plane of fixation. Our results support the latter hypothesis: the responses of parafoveal V1 neurons are largely unaffected by changes in blur over a wide range.

Surround Suppression of V1 Neurons Mediates Orientation-Based Representation of High-Order Visual Features

2009 ◽  
Vol 101 (3) ◽  
pp. 1444-1462 ◽  
Author(s):  
Hiroki Tanaka ◽  
Izumi Ohzawa
Keyword(s):  
Visual Information ◽  
Receptive Fields ◽  
High Order ◽  
Visual Features ◽  
Surround Suppression ◽  
V1 Neurons ◽  
Spatial Frequencies ◽  
Wide Range ◽  
Novel Method ◽  
Optimal Values

Neurons with surround suppression have been implicated in processing high-order visual features such as contrast- or texture-defined boundaries and subjective contours. However, little is known regarding how these neurons encode high-order visual information in a systematic manner as a population. To address this issue, we have measured detailed spatial structures of classical center and suppressive surround regions of receptive fields of primary visual cortex (V1) neurons and examined how a population of such neurons allow encoding of various high-order features and shapes in visual scenes. Using a novel method to reconstruct structures, we found that the center and surround regions are often both elongated parallel to each other, reminiscent of on and off subregions of simple cells without surround suppression. These structures allow V1 neurons to extract high-order contours of various orientations and spatial frequencies, with a variety of optimal values across neurons. The results show that a wide range of orientations and widths of the high-order features are systematically represented by the population of V1 neurons with surround suppression.

scholarly journals Spatial Distribution of Contextual Interactions in Primary Visual Cortex and in Visual Perception

2000 ◽  
Vol 84 (4) ◽  
pp. 2048-2062 ◽  
Author(s):  
Mitesh K. Kapadia ◽  
Gerald Westheimer ◽  
Charles D. Gilbert
Keyword(s):  
Spatial Distribution ◽  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Cortical Neurons ◽  
Receptive Fields ◽  
Human Perception ◽  
Spatial Arrangement ◽  
Orthogonal Axis ◽  
V1 Neurons ◽  
Contextual Interactions

To examine the role of primary visual cortex in visuospatial integration, we studied the spatial arrangement of contextual interactions in the response properties of neurons in primary visual cortex of alert monkeys and in human perception. We found a spatial segregation of opposing contextual interactions. At the level of cortical neurons, excitatory interactions were located along the ends of receptive fields, while inhibitory interactions were strongest along the orthogonal axis. Parallel psychophysical studies in human observers showed opposing contextual interactions surrounding a target line with a similar spatial distribution. The results suggest that V1 neurons can participate in multiple perceptual processes via spatially segregated and functionally distinct components of their receptive fields.

Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell

1997 ◽  
Vol 9 (5) ◽  
pp. 971-983 ◽  
Author(s):  
Todd W. Troyer ◽  
Kenneth D. Miller
Keyword(s):  
Steady State ◽  
Cortical Neurons ◽  
Cortical Cell ◽  
Firing Frequency ◽  
Cell Physiology ◽  
State Behavior ◽  
Integrate And Fire ◽  
Wide Range ◽  
Visual Cortical

To understand the interspike interval (ISI) variability displayed by visual cortical neurons (Softky & Koch, 1993), it is critical to examine the dynamics of their neuronal integration, as well as the variability in their synaptic input current. Most previous models have focused on the latter factor. We match a simple integrate-and-fire model to the experimentally measured integrative properties of cortical regular spiking cells (McCormick, Connors, Lighthall, & Prince, 1985). After setting RC parameters, the postspike voltage reset is set to match experimental measurements of neuronal gain (obtained from in vitro plots of firing frequency versus injected current). Examination of the resulting model leads to an intuitive picture of neuronal integration that unifies the seemingly contradictory [Formula: see text] and random walk pictures that have previously been proposed. When ISIs are dominated by postspike recovery,[Formula: see text] arguments hold and spiking is regular; after the “memory” of the last spike becomes negligible, spike threshold crossing is caused by input variance around a steady state and spiking is Poisson. In integrate-and-fire neurons matched to cortical cell physiology, steady-state behavior is predominant, and ISIs are highly variable at all physiological firing rates and for a wide range of inhibitory and excitatory inputs.

Range and Mechanism of Encoding of Horizontal Disparity in Macaque V1

2002 ◽  
Vol 87 (1) ◽  
pp. 209-221 ◽  
Author(s):  
S.J.D. Prince ◽  
B. G. Cumming ◽  
A. J. Parker
Keyword(s):  
Receptive Field ◽  
Cortical Neurons ◽  
Tuning Curve ◽  
Rate Of Change ◽  
Careful Examination ◽  
Phase Encoding ◽  
Tuning Curves ◽  
V1 Neurons ◽  
Gabor Functions ◽  
Spatial Frequencies

The responses of single cortical neurons were measured as a function of the binocular disparity of dynamic random dot stereograms for a large sample of neurons ( n = 787) from V1 of the awake macaque. From this sample, we selected 180 neurons whose tuning curves were strongly tuned for disparity, well sampled and well described by one-dimensional Gabor functions. The fitted parameters of the Gabor functions were used to resolve three outstanding issues in binocular stereopsis. First, we considered whether tuning curves can be meaningfully divided into discrete tuning types. Careful examination of the distributions of the Gabor parameters that determine tuning shape revealed no evidence for clustering. We conclude that a continuum of tuning types is present. Second, we investigated the mechanism of disparity encoding for V1 neurons. The shape of the disparity tuning function can be used to distinguish between position-encoding (in which disparity is encoded by an interocular shift in receptive field position) and phase-encoding (in which disparity is encoded by a difference in the receptive field profile in the 2 eyes). Both position and phase encoding were found to be common. This was confirmed by an independent assessment of disparity encoding based on the measurement of disparity sensitivity for sinusoidal luminance gratings of different spatial frequencies. The contributions of phase and position to disparity encoding were compared by estimating a population average of the rate of change in firing rate per degree of disparity. When this was calculated separately for the phase and position contributions, they were found to be closely similar. Third, we investigated the range of disparity tuning in V1 as a function of eccentricity in the parafoveal range. We find few cells which are selective for disparities greater than ±1 ° even at the largest eccentricity of ∼5 °. The preferred disparity was correlated with the spatial scale of the tuning curve, and for most units lay within a ±π radians phase limit. Such a size-disparity correlation is potentially useful for the solution of the correspondence problem.

scholarly journals Development of cortical orientation selectivity in the absence of visual experience with contour

2011 ◽  
Vol 106 (4) ◽  
pp. 1923-1932 ◽  
Author(s):  
Tomokazu Ohshiro ◽  
Shaista Hussain ◽  
Michael Weliky
Keyword(s):  
Cortical Neurons ◽  
Computational Models ◽  
Visual Experience ◽  
Receptive Fields ◽  
Orientation Selectivity ◽  
Experimental Result ◽  
Full Development ◽  
Visual Inputs ◽  
Eye Opening ◽  
Visual Cortical

Visual cortical neurons are selective for the orientation of lines, and the full development of this selectivity requires natural visual experience after eye opening. Here we examined whether this selectivity develops without seeing lines and contours. Juvenile ferrets were reared in a dark room and visually trained by being shown a movie of flickering, sparse spots. We found that despite the lack of contour visual experience, the cortical neurons of these ferrets developed strong orientation selectivity and exhibited simple-cell receptive fields. This finding suggests that overt contour visual experience is unnecessary for the maturation of orientation selectivity and is inconsistent with the computational models that crucially require the visual inputs of lines and contours for the development of orientation selectivity. We propose that a correlation-based model supplemented with a constraint on synaptic strength dynamics is able to account for our experimental result.

Receptive fields of visual cortical neurons during changes in parameters of photic stimulation in cats

1980 ◽  
Vol 12 (2) ◽  
pp. 77-84
Author(s):  
I. A. Shevelev ◽  
V. G. Marchenko ◽  
I. V. Maksimova
Keyword(s):  
Cortical Neurons ◽  
Receptive Fields ◽  
Photic Stimulation ◽  
Visual Cortical

Cooperative Representation of Visual Borders

Perception ◽  
1992 ◽  
Vol 21 (2) ◽  
pp. 185-193 ◽  
Author(s):  
Geoffrey W Stuart ◽  
Terence R J Bossomaier
Keyword(s):  
Spatial Frequency ◽  
Spatial Scales ◽  
Receptive Fields ◽  
Frequency Content ◽  
Cortical Cells ◽  
Firing Rates ◽  
Spatial Frequencies ◽  
Cooperative Coding ◽  
Stimulus Features ◽  
Visual Cortical

Recently it has been reported that the visual cortical cells which are engaged in cooperative coding of global stimulus features, display synchrony in their firing rates when both are stimulated. Alternative models identify global stimulus features with the coarse spatial scales of the image. Versions of the Munsterberg or Café Wall illusions which differ in their low spatial frequency content were used to show that in all cases it was the high spatial frequencies in the image which determined the strength and direction of these illusions. Since cells responsive to high spatial frequencies have small receptive fields, cooperative coding must be involved in the representation of long borders in the image.

scholarly journals Coding of chromatic spatial contrast by macaque V1 neurons

2021 ◽  
Author(s):  
Abhishek De ◽  
Gregory D. Horwitz
Keyword(s):  
Color Image ◽  
Receptive Fields ◽  
Luminance Contrast ◽  
Chromatic Contrast ◽  
Simple Cells ◽  
Complex Cells ◽  
V1 Neurons ◽  
Full Color ◽  
Common Principle ◽  
Neural Selectivity

ABSTRACTColor perception relies on spatial comparisons of chromatic signals, but how the brain performs these comparisons is poorly understood. Here, we show that many V1 neurons compare signals across their receptive fields (RF) using a common principle. We estimated the spatial-chromatic RFs of each neuron, and then measured neural selectivity to customized colored edges that were sampled using a novel closed-loop technique. We found that many double-opponent (DO) cells, which have spatially and chromatically opponent RFs, responded to chromatic contrast as a weighted sum, akin to how simple cells respond to luminance contrast. Other neurons integrated chromatic signals non-linearly, confirming that linear signal integration is not an obligate property of V1 neurons. The functional similarity of DO and simple cells suggests a common underlying neural circuitry, promotes the construction of image-computable models for full-color image representation, and sheds new light on V1 complex cells.

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