scholarly journals Comparison Among Some Models of Orientation Selectivity

2006 ◽  
Vol 96 (1) ◽  
pp. 404-419 ◽  
Author(s):  
Andrew F. Teich ◽  
Ning Qian
Keyword(s):  
Peak Shift ◽  
Orientation Tuning ◽  
Complex Spectrum ◽  
Complex Cell ◽  
Phase Information ◽  
Simple Complex ◽  
Simple Cells ◽  
Complex Cells ◽  
Spatial Phase ◽  
Opposite Contrast

Several models exist for explaining primary visual cortex (V1) orientation tuning. The modified feedforward model (MFM) and the recurrent model (RM) are major examples. We have implemented these two models, at the same level of detail, alongside a few newer variations, and thoroughly compared their receptive-field structures. We found that antiphase inhibition in the MFM enhances both spatial phase information and orientation tuning, producing well-tuned simple cells. This remains true for a newer version of the MFM that incorporates untuned complex-cell inhibition. In contrast, when the recurrent connections in the RM are strong enough to produce typical V1 orientation tuning, they also eliminate spatial phase information, making the cells complex. Introducing phase specificity into the connections of the RM (as done in an original version of the RM) can make the cells phase sensitive, but the cells show an incorrect 90° peak shift of orientation tuning under opposite contrast signs. An inhibition-dominant version of the RM can generate well-tuned cells across the simple–complex spectrum, but it predicts that the net effect of cortical interactions is to suppress feedforward excitation across all orientations in simple cells. Finally, adding antiphase inhibition used in the MFM into the RM produces a most general model. We call this new model the modified recurrent model (MRM) and show that this model can also produce well-tuned cells throughout the simple–complex spectrum. Unlike the inhibition-dominant RM, the MRM is consistent with data from cat V1, suggesting that the net effect of cortical interactions is to boost simple cell responses at the preferred orientation. These results suggest that the MFM is well suited for explaining orientation tuning in simple cells, whereas the standard RM is for complex cells. The assignment of the RM to complex cells also avoids conflicts between the RM and the experiments of cortical inactivation (done on simple cells) and the spatial-frequency dependency of orientation tuning (found in simple cells). Because orientation-tuned V1 cells show a continuum of simple- to complex-cell behavior, the MRM provides the best description of V1 data.

Responses of neurons in cat striate cortex to vernier offsets in reverse contrast stimuli

1995 ◽  
Vol 12 (5) ◽  
pp. 805-817 ◽  
Author(s):  
N.v. Swindale
Keyword(s):  
Striate Cortex ◽  
Tuning Curve ◽  
High Sensitivity ◽  
Orientation Tuning ◽  
Vernier Acuity ◽  
Simple Cells ◽  
Tuning Curves ◽  
Complex Cells ◽  
Simple And Complex Cells ◽  
Opposite Contrast

AbstractThis paper examines how the responses of cells in area 17 of the cat vary as a function of the vernier offset between a bright and a dark bar. The study was prompted by the finding that human vernier acuity is reduced for bars or edges of opposite contrast sign (Mather & Morgan, 1986; O'Shea & Mitchell, 1990). Both simple and complex cells showed V-shaped tuning curves for reverse contrast stimuli: i.e. response was minimum at alignment, and increased with increasing vernier offset. For vernier bars with the same contrast sign, γ-shaped tuning curves were found, as reported earlier (Swindale & Cynader, 1986). Sensitivity to offset was inversely correlated in the two paradigms. However, complex cells with high sensitivity to offsets in a normal vernier stimulus were significantly less sensitive to offsets in reverse contrast stimuli. A cell's response to a vernier stimulus in which both bars are bright can be predicted by the shape of its orientation tuning curve, if the vernier stimulus is approximated by a single bar with an orientation equal to that of a line joining the midpoints of the two component bars (Swindale & Cynader, 1986). This approximation did not hold for the reverse contrast condition: orientation tuning curves for compound barswere broad and shallow, rather than bimodal, with peaks up to 40 deg from the preferred orientation. Results from simple cells were compared with predictions made by a linear model of the receptive field. The model predicted the V-shaped tuning curves found for reverse contrast stimuli. It also predicted that absolute values of tuning slopes for vernier offsets in reverse contrast stimuli might sometimes be higher than with normal stimuli. This was observed in some simple cells. The model was unable to explain the shape of orientation tuning curves for compound bars, nor could it explain the breakdown of the equivalent orientation approximation.

scholarly journals Binocular Spatial Phase Tuning Characteristics of Neurons in the Macaque Striate Cortex

1997 ◽  
Vol 78 (1) ◽  
pp. 351-365 ◽  
Author(s):  
Earl L. Smith ◽  
Yuzo M. Chino ◽  
Jinren Ni ◽  
William H. Ridder ◽  
M.L.J. Crawford
Keyword(s):  
Spatial Frequency ◽  
Striate Cortex ◽  
Orientation Tuning ◽  
Absolute Position ◽  
Simple Cells ◽  
Complex Cells ◽  
Spatial Phase ◽  
Binocular Interactions ◽  
Phase Tuning ◽  
Phase Sensitive

Smith, Earl L., III, Yuzo M. Chino, Jinren Ni, William H. Ridder III, and M.L.J. Crawford. Binocular spatial phase tuning characteristics of neurons in the macaque striate cortex. J. Neurophysiol. 78: 351–365, 1997. We employed microelectrode recording techniques to study the sensitivity of individual neurons in the striate cortex of anesthetized and paralyzed monkeys to relative interocular image disparities and to determine the effects of basic stimulus parameters on these cortical binocular interactions. The visual stimuli were drifting sine wave gratings. After the optimal stimulus orientation, spatial frequency, and direction of stimulus movement were found, the cells' disparity tuning characteristics were determined by measuring responses as a function of the relative interocular spatial phase of dichoptic grating pairs. No attempts were made to assess absolute position disparities or horizontal disparities relative to the horopter. The majority (∼70%) of simple cells were highly sensitive to interocular spatial phase disparities, particularly neurons with balanced ocular dominances. Simple cells typically demonstrated binocular facilitation at the optimal phase disparity and binocular suppression for disparities 180° away. Fewer complex cells were phase selective (∼40%); however, the range of disparity selectivity in phase-sensitive complex cells was comparable with that for simple cells. Binocular interactions in non-phase-sensitive complex cells were evidenced by binocular response amplitudes that differed from responses to monocular stimulation. The degree of disparity tuning was independent of a cell's optimal orientation or the degree of direction tuning. However, disparity-sensitive cells tended to have narrow orientation tuning functions and the degree of disparity tuning was greatest for the optimal stimulus orientations. Rotating the stimulus for one eye 90° from the optimal orientation usually eliminated binocular interactions. The effects of phase disparities on the binocular response amplitude were also greatest at the optimal spatial frequency. Thus a cell's sensitivity to absolute position disparities reflects its spatial tuning characteristics, with cells sensitive to high spatial frequencies being capable of signaling very small changes in image disparity. On the other hand, stimulus contrast had relatively little effect on a cell's disparity tuning, because response saturation occurred at the same contrast level for all relative interocular phase disparities. Thus, as with orientation tuning, a cell's optimal disparity and the degree of disparity selectivity were invariant with contrast. Overall, the results show that sensitivity to interocular spatial phase disparities is a common property of striate neurons. A cell's disparity tuning characteristics appear to largely reflect its monocular receptive field properties and the interocular balance between excitatory and inhibitory inputs. However, distinct functional classes of cortical neurons could not be discriminated on the basis of disparity sensitivity alone.

Direction selectivity of complex cells in a comparison with simple cells

1975 ◽  
Vol 38 (6) ◽  
pp. 1524-1540 ◽  
Author(s):  
A. W. Goodwin ◽  
G. H. Henry
Keyword(s):  
Spontaneous Activity ◽  
Visual Information ◽  
Striate Cortex ◽  
Cell Types ◽  
Direction Selectivity ◽  
Complex Cell ◽  
Simple Cells ◽  
Complex Cells ◽  
Sequential Processing

Following our earlier study on direction selectivity in simple cells (5), the present findings on complex cells made it possible to compare the direction selectivity in the two types of striate cell. Common properties were found in the dimension of the smallest stimulus displacement giving a direction-selective response and in the role of inhibition in suppressing the response as the stimulus moved in the nonpreferred direction. However, the effectiveness of this inhibition varied in the two cell types since it suppressed both driven and spontaneous activity in the simple cell, but only driven firing in the complex cell. It is argued that direction selectivity must enter the response before the complex cell if the inhibition responsible for it's generation fails to influence the spontaneous activity of the cell. The consequences of this finding are considered in the terms of parallel or sequential processing of visual information in striate cortex.

Spatial Phase and the Temporal Structure of the Response to Gratings in V1

1998 ◽  
Vol 80 (2) ◽  
pp. 554-571 ◽  
Author(s):  
Jonathan D. Victor ◽  
Keith P. Purpura
Keyword(s):  
Spatial Frequency ◽  
Spike Train ◽  
Temporal Structure ◽  
Spike Timing ◽  
Dependent Manner ◽  
Data Set ◽  
Simple Cells ◽  
Complex Cells ◽  
Spatial Phase ◽  
Contrast Information

Victor, Jonathan D. and Keith P. Purpura. Spatial phase and the temporal structure of the response to gratings in V1. J. Neurophysiol. 80: 554–571, 1998. We recorded single-unit activity of 25 units in the parafoveal representation of macaque V1 to transient appearance of sinusoidal gratings. Gratings were systematically varied in spatial phase and in one or two of the following: contrast, spatial frequency, and orientation. Individual responses were compared based on spike counts, and also according to metrics sensitive to spike timing. For each metric, the extent of stimulus-dependent clustering of individual responses was assessed via the transmitted information, H. In nearly all data sets, stimulus-dependent clustering was maximal for metrics sensitive to the temporal pattern of spikes, typically with a precision of 25–50 ms. To focus on the interaction of spatial phase with other stimulus attributes, each data set was analyzed in two ways. In the “pooled phases” approach, the phase of the stimulus was ignored in the assessment of clustering, to yield an index H pooled. In the “individual phases” approach, clustering was calculated separately for each spatial phase and then averaged across spatial phases to yield an index H indiv. H pooled expresses the extent to which a spike train represents contrast, spatial frequency, or orientation in a manner which is not confounded by spatial phase (phase-independent representation), whereas H indiv expresses the extent to which a spike train represents one of these attributes, provided spatial phase is fixed (phase-dependent representation). Here, representation means that a stimulus attribute has a reproducible and systematic influence on individual responses, not a neural mechanism for decoding this influence. During the initial 100 ms of the response, contrast was represented in a phase-dependent manner by simple cells but primarily in a phase-independent manner by complex cells. As the response evolved, simple cell responses acquired phase-independent contrast information, whereas complex cells acquired phase-dependent contrast information. Simple cells represented orientation and spatial frequency in a primarily phase-dependent manner, but also they contained some phase-independent information in their initial response segment. Complex cells showed primarily phase-independent representation of orientation but primarily phase-dependent representation of spatial frequency. Joint representation of two attributes (contrast and spatial frequency, contrast and orientation, spatial frequency and orientation) was primarily phase dependent for simple cells, and primarily phase independent for complex cells. In simple and complex cells, the variability in the number of spikes elicited on each response was substantially greater than the expectations of a Poisson process. Although some of this variation could be attributed to the dependence of the response on the spatial phase of the grating, variability was still markedly greater than Poisson when the contribution of spatial phase to response variance was removed.

Space-time spectra of complex cell filters in the macaque monkey: A comparison of results obtained with pseudowhite noise and grating stimuli

1994 ◽  
Vol 11 (4) ◽  
pp. 805-821 ◽  
Author(s):  
James P. Gaska ◽  
Lowell D. Jacobson ◽  
Hai-Wen Chen ◽  
Daniel A. Pollen
Keyword(s):  
Spatial Frequency ◽  
Macaque Monkey ◽  
Spike Rate ◽  
Second Order ◽  
Orientation Tuning ◽  
Complex Cell ◽  
Interaction Function ◽  
Tuning Curves ◽  
Complex Cells ◽  
Order Kernel

AbstractWhite noise stimuli were used to estimate second-order kernels for complex cells in cortical area VI of the macaque monkey, and drifting grating stimuli were presented to the same sample of neurons to obtain orientation and spatial-frequency tuning curves. Using these data, we quantified how well second-order kernels predict the normalized tuning of the average response of complex cells to drifting gratings.The estimated second-order kernel of each complex cell was transformed into an interaction function defined over all spatial and temporal lags without regard to absolute position or delay. The Fourier transform of each interaction function was then computed to obtain an interaction spectrum. For a cell that is well modeled by a second-order system, the cell’s interaction spectrum is proportional to the tuning of its average spike rate to drifting gratings. This result was used to obtain spatial-frequency and orientation tuning predictions for each cell based on its second-order kernel. From the spatial-frequency and orientation tuning curves, we computed peaks and bandwidths, and an index for directional selectivity.We found that the predictions derived from second-order kernels provide an accurate description of the change in the average spike rate of complex cells to single drifting sine–wave gratings. These findings are consistent with a model for complex cells that has a quadratic spectral energy operator at its core but are inconsistent with a spectral amplitude model.

Self-Organization of Binocular Disparity Tuning by Reciprocal Corticogeniculate Interactions

1998 ◽  
Vol 10 (2) ◽  
pp. 199-215 ◽  
Author(s):  
Alexander Grunewald ◽  
Stephen Grossberg
Keyword(s):  
Learning Process ◽  
Visual Processing ◽  
Neural Model ◽  
Image Features ◽  
Cortical Cells ◽  
Simple Cells ◽  
Complex Cells ◽  
Matching Process ◽  
Tuning Width ◽  
Opposite Contrast

This article develops a neural model of how sharp disparity tuning can arise through experience-dependent development of cortical complex cells. This learning process clarifies how complex cells can binocularly match left and right eye image features with the same contrast polarity, yet also pool signals with opposite contrast polarities. Antagonistic rebounds between LGN ON and OFF cells and cortical simple cells sensitive to opposite contrast polarities enable anticorrelated simple cells to learn to activate a shared set of complex cells. Feedback from binocularly tuned cortical cells to monocular LGN cells is proposed to carry out a matching process that dynamically stabilizes the learning process. This feedback represents a type of matching process that is elaborated at higher visual processing areas into a volitionally controllable type of attention. We show stable learning when both of these properties hold. Learning adjusts the initially coarsely tuned disparity preference to match the disparities present in the environment, and the tuning width decreases to yield high disparity selectivity, which enables the model to quickly detect image disparities. Learning is impaired in the absence of either antagonistic rebounds or corticogeniculate feedback. The model also helps to explain psychophysical and neurobiological data about adult 3-D vision.

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.

Internal Spatial Organization of Receptive Fields of Complex Cells in the Early Visual Cortex

2007 ◽  
Vol 98 (3) ◽  
pp. 1194-1212 ◽  
Author(s):  
Kota S. Sasaki ◽  
Izumi Ohzawa
Keyword(s):  
Visual Cortex ◽  
Minimal Model ◽  
Spatial Organization ◽  
Receptive Fields ◽  
Simple Cell ◽  
Complex Cell ◽  
Linear Filters ◽  
Simple Cells ◽  
Complex Cells ◽  
Early Visual Cortex

The receptive fields of complex cells in the early visual cortex are economically modeled by combining outputs of a quadrature pair of linear filters. For actual complex cells, such a minimal model may be insufficient because many more simple cells are thought to make up a complex cell receptive field. To examine the minimalist model physiologically, we analyzed spatial relationships between the internal structure (subunits) and the overall receptive fields of individual complex cells by a two-stimulus interaction technique. The receptive fields of complex cells are more circular and only slightly larger than their subunits in size. In addition, complex cell subunits occupy spatial extents similar to those of simple cell receptive fields. Therefore in these respects, the minimalist schema is a fair approximation to actual complex cells. However, there are violations against the minimal model. Simple cell receptive fields have significantly fewer subregions than complex cell subunits and, in general, simple cell receptive fields are elongated more horizontally than vertically. This bias is absent in complex cell subunits and receptive fields. Thus simple cells cannot be equated to individual complex cell subunits and spatial pooling of simple cells may occur anisotropically to constitute a complex cell subunit. Moreover, when linear filters for complex cell subunits are examined separately for bright and dark responses, there are significant imbalances and position displacements between them. This suggests that actual complex cell receptive fields are constructed by a richer combination of linear filters than proposed by the minimalist model.

Oblique Effect: A Neural Basis in the Visual Cortex

2003 ◽  
Vol 90 (1) ◽  
pp. 204-217 ◽  
Author(s):  
Baowang Li ◽  
Matthew R. Peterson ◽  
Ralph D. Freeman
Keyword(s):  
Striate Cortex ◽  
Oblique Effect ◽  
Orientation Tuning ◽  
Neural Basis ◽  
Linear Filters ◽  
Simple Cells ◽  
Tuning Curves ◽  
Complex Cells ◽  
Behavioral Studies ◽  
Spatial Frequencies

The details of oriented visual stimuli are better resolved when they are horizontal or vertical rather than oblique. This “oblique effect” has been confirmed in numerous behavioral studies in humans and to some extent in animals. However, investigations of its neural basis have produced mixed and inconclusive results, presumably due in part to limited sample sizes. We have used a database to analyze a population of 4,418 cells in the cat's striate cortex to determine possible differences as a function of orientation. We find that both the numbers of cells and the widths of orientation tuning vary as a function of preferred orientation. Specifically, more cells prefer horizontal and vertical orientations compared with oblique angles. The largest population of cells is activated by orientations close to horizontal. In addition, orientation tuning widths are most narrow for cells preferring horizontal orientations. These findings are most prominent for simple cells tuned to high spatial frequencies. Complex cells and simple cells tuned to low spatial frequencies do not exhibit these anisotropies. For a subset of simple cells from our population ( n = 104), we examined the relative contributions of linear and nonlinear mechanisms in shaping orientation tuning curves. We find that linear contributions alone do not account for the narrower tuning widths at horizontal orientations. By modeling simple cells as linear filters followed by static expansive nonlinearities, our analysis indicates that horizontally tuned cells have a greater nonlinear component than those tuned to other orientations. This suggests that intracortical mechanisms play a major role in shaping the oblique effect.

scholarly journals Simultaneous development and periodic clustering of simple and complex cells in visual cortex

2019 ◽  
Author(s):  
Gwangsu Kim ◽  
Jaeson Jang ◽  
Se-Bum Paik
Keyword(s):  
Visual Cortex ◽  
Periodic Variation ◽  
Orientation Tuning ◽  
List Type ◽  
Simple Complex ◽  
Single Class ◽  
Complex Cells ◽  
Orientation Maps ◽  
Simple And Complex Cells ◽  
Retinal Afferents

AbstractNeurons in the primary visual cortex (V1) are often classified as simple or complex cells, but it is debated whether they are discrete hierarchical classes of neurons developing sequentially, or if they represent a continuum of variation within a single class of cells developing simultaneously. Herein, we show that simple and complex cells may arise simultaneously from the universal process of retinal development. From analysis of the cortical receptive fields in cats, we show evidence that simple and complex cells originate from the periodic variation of ON-OFF segregation in the feedforward projection of retinal mosaics, by which they organize into periodic clusters in V1. Our key prediction that clusters of simple and complex cells correlate topographically with orientation maps was confirmed by data in cats. Our results suggest that simple and complex cells are not two distinct neural populations but arise from common retinal afferents, simultaneous with orientation tuning.HighlightsSimple and complex cells arise simultaneously from retinal afferents.Simple/complex cells are organized into periodic clusters across visual cortex.Simple/complex clusters are topographically correlated with orientation maps.Development of clustered cells in V1 is explained by the Paik-Ringach model.

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