Spatial phase properties of simple cells and their related cortical columns in the primary visual cortex: Model study

2000 ◽  
Vol 38 ◽  
pp. S72
Author(s):  
M Miyashita
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Simple Cells ◽  
Spatial Phase ◽  
Cortical Columns ◽  
Model Study ◽  
Phase Properties

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

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)

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 Orientation Tuning of Input Conductance, Excitation, and Inhibition in Cat Primary Visual Cortex

2000 ◽  
Vol 84 (2) ◽  
pp. 909-926 ◽  
Author(s):  
Jeffrey S. Anderson ◽  
Matteo Carandini ◽  
David Ferster
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Stimulation ◽  
Gain Control ◽  
Orientation Tuning ◽  
Visual Responses ◽  
Stimulus Contrast ◽  
Simple Cells ◽  
Conductance Changes ◽  
Input Conductance

The input conductance of cells in the cat primary visual cortex (V1) has been shown recently to grow substantially during visual stimulation. Because increasing conductance can have a divisive effect on the synaptic input, theoretical proposals have ascribed to it specific functions. According to the veto model, conductance increases would serve to sharpen orientation tuning by increasing most at off-optimal orientations. According to the normalization model, conductance increases would control the cell's gain, by being independent of stimulus orientation and by growing with stimulus contrast. We set out to test these proposals and to determine the visual properties and possible synaptic origin of the conductance increases. We recorded the membrane potential of cat V1 cells while injecting steady currents and presenting drifting grating patterns of varying contrast and orientation. Input conductance grew with stimulus contrast by 20–300%, generally more in simple cells (40–300%) than in complex cells (20–120%), and in simple cells was strongly modulated in time. Conductance was invariably maximal for stimuli of the preferred orientation. Thus conductance changes contribute to a gain control mechanism, but the strength of this gain control does not depend uniquely on contrast. By assuming that the conductance changes are entirely synaptic, we further derived the excitatory and inhibitory synaptic conductances underlying the visual responses. In simple cells, these conductances were often arranged in push-pull: excitation increased when inhibition decreased and vice versa. Excitation and inhibition had similar preferred orientations and did not appear to differ in tuning width, suggesting that the intracortical synaptic inputs to simple cells of cat V1 originate from cells with similar orientation tuning. This finding is at odds with models where orientation tuning in simple cells is achieved by inhibition at off-optimal orientations or sharpened by inhibition that is more broadly tuned than excitation.

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