The binocular organization of simple cells in the cat's visual cortex

1986 ◽  
Vol 56 (1) ◽  
pp. 221-242 ◽  
Author(s):  
I. Ohzawa ◽  
R. D. Freeman
Keyword(s):  
Visual Cortex ◽  
Linear Model ◽  
Specific Interaction ◽  
Binocular Summation ◽  
Inhibitory Influence ◽  
Neural Signals ◽  
Simple Cells ◽  
Spatial Phase ◽  
Cell Responses ◽  
Binocular Interaction

We have studied the manner by which inputs from the two eyes are combined in simple cells of the cat's visual cortex. The stimuli for this study are drifting sinusoidal gratings, shown dichoptically at optimal spatial frequency and orientation. The relative spatial phase (disparity) between the gratings for left and right eyes is varied over 360 degrees. Most simple cells show phase-specific binocular interaction such that response amplitudes and phases vary depending on the relative spatial phase. At one phase, response is greater than either of the monocular responses and often greater than the sum of the two. At the phase 180 degrees away from the optimal, the cell's responses are strongly inhibited and often completely suppressed. Phase-specific binocular interaction disappears when the gratings presented to one eye are made orthogonal to the optimal orientation. The degree of binocular interaction does not depend critically on the ocular dominance of the cells. Simple cells that are nearly equally dominated by each eye always exhibit strong phase-specific interaction. The majority of cells that are strongly dominated by one eye, and even those that appear monocular, show phase-dependent changes in responses. We examined the extent of binocular interaction for cells with preferred orientations near vertical compared with those tuned to other optimal orientations. If these cells are conveying information about depth, one might expect a greater degree of binocular phase-specificity for units preferring nearly vertical orientations, which would then be processing horizontal disparities. We find no evidence for this. Predictions of simple-cell responses are derived from a linear model of binocular convergence in which light-evoked neural signals from each eye are summed linearly to determine cell responses. Data from cells generally follow the prediction of the model for both response amplitude and phase. Deviations from predictions of the linear model are found for a minority of cells. This deviation may be accounted for by a threshold mechanism that comes into play after the linear binocular summation. A small proportion of simple cells that appear monocular by alternate tests of each eye show a purely inhibitory influence from the silent eye. This inhibition is not generally dependent on the relative phase of the gratings. We conclude that most binocular interaction in striate simple cells may be accounted for by linear summation of neural signals from each eye.(ABSTRACT TRUNCATED AT 400 WORDS)

The binocular organization of complex cells in the cat's visual cortex

1986 ◽  
Vol 56 (1) ◽  
pp. 243-259 ◽  
Author(s):  
I. Ohzawa ◽  
R. D. Freeman
Keyword(s):  
Visual Cortex ◽  
Relative Phase ◽  
Specific Interaction ◽  
Linear Convergence ◽  
Inhibitory Influence ◽  
Complex Cell ◽  
Neural Signals ◽  
Simple Cells ◽  
Complex Cells ◽  
Binocular Interaction

We have studied the manner by which inputs from the two eyes are combined in complex cells of the cat's visual cortex. The stimuli are drifting sinusoidal gratings presented dichoptically at optimal spatial frequency and orientation. The relative phase between the gratings for left and right eyes is varied over 360 degrees. Approximately 40% of complex cells show phase-specific binocular interaction where response amplitudes vary depending on the relative phase of the gratings shown to the two eyes. This interaction is similar to that observed for most simple cells. We devised a test to examine whether the phase-specific interaction in complex cells results from linear convergence of neural signals at subunits of the receptive fields. The data from this test are consistent with a linear combination model. The phase-specific binocular interaction data from complex cells imply that the optimal relative phase of the receptive field subunits is closely matched. Another type of complex cell, approximately 40% of the total, could be driven through either eye, but exhibited non-phase-specific responses to dichoptically presented gratings. This type of interaction is found only in complex cells. Binocularly non-phase-specific complex cells may have subunits whose optimal relative phases are random or monocular. The division of complex cells into these two major groups (binocularly phase specific and non-phase specific) is independent of whether they are standard or special complex-cell types. A small proportion (8%) of complex cells that appear monocular by alternate tests of each eye show a purely inhibitory influence from the silent eye. This inhibition is not generally dependent on the relative phase of the gratings. Unlike simple cells, complex cells are not a homogeneous group. However, nearly half of complex cells show phase-specific binocular interaction that is probably the result of linear convergence. Combined with the results from simple cells, the majority of binocular interaction in the striate cortex may be accounted for by linear summation of neural signals from each eye. This provides a simplified view of the nature of binocular interaction in the visual cortex.

scholarly journals Learning receptive field properties of complex cells in V1

2020 ◽  
Author(s):  
Yanbo Lian ◽  
Ali Almasi ◽  
David B. Grayden ◽  
Tatiana Kameneva ◽  
Anthony N. Burkitt ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Experimental Studies ◽  
Learning Rule ◽  
Energy Model ◽  
Learning Ability ◽  
Simple Cells ◽  
Complex Cells ◽  
Spatial Phase ◽  
Model Complex ◽  
The Brain

AbstractThere are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a diverse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a diversity of complex cells, similar to those observed experimentally.Author summaryMany cortical functions originate from the learning ability of the brain. How the properties of cortical cells are learned is vital for understanding how the brain works. There are many models that explain how V1 simple cells can be learned. However, how V1 complex cells are learned still remains unclear. In this paper, we propose a model of learning in complex cells based on the Bienenstock, Cooper, and Munro (BCM) rule. We demonstrate that properties of receptive fields of complex cells can be learned using this biologically plausible learning rule. Quantitative comparisons between the model and experimental data are performed. Results show that model complex cells can account for the diversity of complex cells found in experimental studies. In summary, this study provides a plausible explanation for how complex cells can be learned using biologically plausible plasticity mechanisms. Our findings help us to better understand biological vision processing and provide us with insights into the general signal processing principles that the visual cortex employs to process visual information.

scholarly journals Spatial phase sensitivity of complex cells in primary visual cortex depends on stimulus contrast

2015 ◽  
Vol 114 (6) ◽  
pp. 3326-3338 ◽  
Author(s):  
H. Meffin ◽  
M. A. Hietanen ◽  
S. L. Cloherty ◽  
M. R. Ibbotson
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Spatial Summation ◽  
Phase Sensitivity ◽  
Simple Cells ◽  
Low Contrast ◽  
Complex Cells ◽  
Spatial Phase ◽  
Phase Sensitive

Neurons in primary visual cortex are classified as simple, which are phase sensitive, or complex, which are significantly less phase sensitive. Previously, we have used drifting gratings to show that the phase sensitivity of complex cells increases at low contrast and after contrast adaptation while that of simple cells remains the same at all contrasts (Cloherty SL, Ibbotson MR. J Neurophysiol 113: 434–444, 2015; Crowder NA, van Kleef J, Dreher B, Ibbotson MR. J Neurophysiol 98: 1155–1166, 2007; van Kleef JP, Cloherty SL, Ibbotson MR. J Physiol 588: 3457–3470, 2010). However, drifting gratings confound the influence of spatial and temporal summation, so here we have stimulated complex cells with gratings that are spatially stationary but continuously reverse the polarity of the contrast over time (contrast-reversing gratings). By varying the spatial phase and contrast of the gratings we aimed to establish whether the contrast-dependent phase sensitivity of complex cells results from changes in spatial or temporal processing or both. We found that most of the increase in phase sensitivity at low contrasts could be attributed to changes in the spatial phase sensitivities of complex cells. However, at low contrasts the complex cells did not develop the spatiotemporal response characteristics of simple cells, in which paired response peaks occur 180° out of phase in time and space. Complex cells that increased their spatial phase sensitivity at low contrasts were significantly overrepresented in the supragranular layers of cortex. We conclude that complex cells in supragranular layers of cat cortex have dynamic spatial summation properties and that the mechanisms underlying complex cell receptive fields differ between cortical layers.

scholarly journals Layer 4 in Primary Visual Cortex of the Awake Rabbit: Contrasting Properties of Simple Cells and Putative Feedforward Inhibitory Interneurons

2013 ◽  
Vol 33 (28) ◽  
pp. 11372-11389 ◽  
Author(s):  
J. Zhuang ◽  
C. R. Stoelzel ◽  
Y. Bereshpolova ◽  
J. M. Huff ◽  
X. Hei ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Inhibitory Interneurons ◽  
Simple Cells ◽  
Layer 4

scholarly journals Prediction of Orientation Selectivity from Receptive Field Architecture in Simple Cells of Cat Visual Cortex

Neuron ◽  
2001 ◽  
Vol 30 (1) ◽  
pp. 263-274 ◽  
Author(s):  
Ilan Lampl ◽  
Jeffrey S. Anderson ◽  
Deda C. Gillespie ◽  
David Ferster
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Orientation Selectivity ◽  
Simple Cells ◽  
Cat Visual Cortex

scholarly journals Modeling simple-cell direction selectivity with normalized, half-squared, linear operators

1993 ◽  
Vol 70 (5) ◽  
pp. 1885-1898 ◽  
Author(s):  
D. J. Heeger
Keyword(s):  
Linear Prediction ◽  
Direction Selectivity ◽  
Linear Operators ◽  
Simple Cell ◽  
Physiological Data ◽  
Simple Cells ◽  
Cell Responses ◽  
Preferred Direction ◽  
Model Cell ◽  
Actual Response

1. A longstanding view of simple cells is that they sum their inputs linearly. However, the linear model falls short of a complete account of simple-cell direction selectivity. We have developed a nonlinear model of simple-cell responses (hereafter referred to as the normalization model) to explain a larger body of physiological data. 2. The normalization model consists of an underlying linear stage along with two additional nonlinear stages. The first is a half-squaring nonlinearity; half-squaring is half-wave rectification followed by squaring. The second is a divisive normalization non-linearity in which each model cell is suppressed by the pooled activity of a large number of cells. 3. By comparing responses with counterphase (flickering) gratings and drifting gratings, researchers have demonstrated that there is a nonlinear contribution to simple-cell responses. Specifically they found 1) that the linear prediction from counterphase grating responses underestimates a direction index computed from drifting grating responses, 2) that the linear prediction correctly estimates responses to gratings drifting in the preferred direction, and 3) that the linear prediction overestimates responses to gratings drifting in the nonpreferred direction. 4. We have simulated model cell responses and derived mathematical expressions to demonstrate that the normalization model accounts for this empirical data. Specifically the model behaves as follows. 1) The linear prediction from counterphase data underestimates the direction index computed from drifting grating responses. 2) The linear prediction from counterphase data overestimates the response to gratings drifting in the nonpreferred direction. The discrepancy between the linear prediction and the actual response is greater when using higher contrast stimuli. 3) For an appropriate choice of contrast, the linear prediction from counterphase data correctly estimates the response to gratings drifting in the preferred direction. For higher contrasts the linear prediction overestimates the actual response, and for lower contrasts the linear prediction underestimates the actual response. 5. In addition, the normalization model is qualitatively consistent with data on the dynamics of simple-cell responses. Tolhurst et al. found that simple cells respond with an initial transient burst of activity when a stimulus first appears. The normalization model behaves similarly; it takes some time after a stimulus first appears before the model cells are fully normalized. We derived the dynamics of the model and found that the transient burst of activity in model cells depends in a particular way on stimulus contrast. The burst is short for high-contrast stimuli and longer for low-contrast stimuli.(ABSTRACT TRUNCATED AT 400 WORDS)

Simulated Optic Flow and Extrastriate Cortex. I. Optic Flow Versus Texture

1997 ◽  
Vol 77 (2) ◽  
pp. 554-561 ◽  
Author(s):  
Jong-Nam Kim ◽  
Kathleen Mulligan ◽  
Helen Sherk
Keyword(s):  
Visual Cortex ◽  
Visual Field ◽  
Optic Flow ◽  
Visual Experience ◽  
Extrastriate Cortex ◽  
Gray Level ◽  
Visual Guidance ◽  
Forward Motion ◽  
Cell Responses ◽  
Single Origin

Kim, Jong-Nam, Kathleen Mulligan, and Helen Sherk. Simulated optic flow and extrastriate cortex. I. Optic flow versus texture. J. Neurophysiol. 77: 554–561, 1997. A locomoting observer sees a very different visual scene than an observer at rest: images throughout the visual field accelerate and expand, and they follow approximately radial outward paths from a single origin. This so-called optic flow field is presumably used for visual guidance, and it has been suggested that particular areas of visual cortex are specialized for the analysis of optic flow. In the cat, the lateral suprasylvian visual area (LS) is a likely candidate. To test the hypothesis that LS is specialized for analysis of optic flow fields, we recorded cell responses to optic flow displays. Stimulus movies simulated the experience of a cat trotting slowly across an endless plain covered with small balls. In different simulations we varied the size of balls, their organization (randomly or regularly dispersed), and their color (all one gray level, or multiple shades of gray). For each optic flow movie, a “texture” movie composed of the same elements but lacking optic flow cues was tested. In anesthetized cats, >500 neurons in LS were studied with a variety of movies. Most (70%) of 454 visually responsive cells responded to optic flow movies. Visually responsive cells generally preferred optic flow to texture movies (69% of those responsive to any movie). The direction in which a movie was shown (forward or reverse) was also an important factor. Most cells (68%) strongly preferred forward motion, which corresponded to visual experience during locomotion.

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