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.

Spatial properties of neurons in the monkey striate cortex

1987 ◽  
Vol 231 (1263) ◽  
pp. 251-288 ◽  
Keyword(s):  
Receptive Field ◽  
Contrast Sensitivity ◽  
Visual Processing ◽  
Striate Cortex ◽  
Receptive Fields ◽  
Spatial Filter ◽  
Cortical Cells ◽  
Simple Cells ◽  
Gabor Function ◽  
Difference Of Gaussians

Contrast sensitivity as a function of spatial frequency was determined for 138 neurons in the foveal region of primate striate cortex. The accuracy of three models in describing these functions was assessed by the method of least squares. Models based on difference-of-Gaussians (DOG) functions were shown to be superior to those based on the Gabor function or the second differential of a Gaussian. In the most general case of the DOG models, each subregion of a simple cell’s receptive field was constructed from a single DOG function. All the models are compatible with the classical observation that the receptive fields of simple cells are made up of spatially discrete ‘on’ and ‘off’ regions. Although the DOG-based models have more free parameters, they can account better for the variety of shapes of spatial contrast sensitivity functions observed in cortical cells and, unlike other models, they provide a detailed description of the organization of subregions of the receptive field that is consistent with the physiological constraints imposed by earlier stages in the visual pathway. Despite the fact that the DOG-based models have spatially discrete components, the resulting amplitude spectra in the frequency domain describe complex cells just as well as simple cells. The superiority of the DOG-based models as a primary spatial filter is discussed in relation to popular models of visual processing that use the Gabor function or the second differential of a Gaussian.

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.

A Ring Model for Direction-Selective Simple Cells in the Visual Cortex

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 164-164
Author(s):  
T Hamada ◽  
K Kato ◽  
M Yamashima
Keyword(s):  
Visual Cortex ◽  
Experimental Studies ◽  
Receptive Fields ◽  
Lateral Geniculate Body ◽  
Neural Model ◽  
Cortical Cells ◽  
Simple Cells ◽  
Ring Model ◽  
Gabor Functions

Experimental studies have shown that (1) direction-selective simple cells in the visual cortex have spatiotemporally inseparable receptive fields, whose spatial profiles at a given time are described by Gabor functions: a sinusoid multiplied by a Gaussian, with a phase parameter; (2) among simple cells, the phases are distributed not merely at 0 and pi/2 as for sine and cosine Gabor functions, but uniformly between 0 and 2pi (DeAngelis et al, 1993 Journal of Neurophysiology69 1091 – 1117); (3) anatomically, these simple cells receive inputs more from other cortical cells than from the lateral geniculate body (LGN) (Ahmed et al, 1994 Journal of Comparative Neurology341 39 – 49). We accordingly propose here a neural model for the simple cells whose receptive fields are assumed to be of the same spatial position and orientation. In the model, several cortical cells are arranged in a ring with mutual excitatory and inhibitory connections, and receive afferent signals from lagged and nonlagged cells in LGN (Saul and Humphrey, 1990 Journal of Neurophysiology64 206 – 224). Computer simulation shows that the cortical cells have spatiotemporally inseparable receptive fields with spatial profiles described by Gabor functions, and are directionally selective to a moving grating. The cells are found to be arranged so that their Gabor phases vary regularly from 0 to 2pi with rotation along the ring. The connection among the cortical cells has a role of amplification as in the canonical microcircuit model (Douglas et al, 1989 Neural Computation1 480 – 488).

Patterns in the discharge of simple and complex visual cortical cells

1981 ◽  
Vol 212 (1188) ◽  
pp. 279-297 ◽  
Keyword(s):  
Spatial Frequency ◽  
Cortical Neurons ◽  
Linear Time ◽  
Cell Activity ◽  
Invariance Property ◽  
Statistical Structure ◽  
Cortical Cells ◽  
Simple Cells ◽  
Complex Cells ◽  
Visual Cortical

The activity of visual cortical neurons (area 17) was recorded in anaesthetized cats in response to sinusoidal drifting gratings. The statistical structure of the discharge of simple and complex cells has been studied as a function of the various parameters of a drifting grating: spatial frequency, orientation, drifting velocity and contrast. For simple cells it has been found that the interspike interval distributions in response to drifting gratings of various spatial frequencies differ only by a time scale factor. They can be reduced to a unique distribution by a linear time transformation. Variations in the spatial frequency of the grating induce variations in the mean firing rate of the cell but leave unchanged the statistical structure of the discharge. On the contrary, the statistical structure of simple cell activity changes when the contrast or the velocity of the stimulus is varied. For complex cells it has been found that the invariance property described above for simple cells is not valid. Complex cells present in their activity in response to visual stimuli two different firing patterns: spikes organized in clusters and spikes that do not show this organization (‘isolated spikes’). The clustered component is the only component of the complex cell discharge that is tuned for spatial frequency and orientation, while the isolated spike component is correlated with the contrast of the stimulus.

Computing with Self-Excitatory Cliques: A Model and an Application to Hyperacuity-Scale Computation in Visual Cortex

1999 ◽  
Vol 11 (1) ◽  
pp. 21-66 ◽  
Author(s):  
Douglas A. Miller ◽  
Steven W. Zucker
Keyword(s):  
Visual Processing ◽  
Network Architecture ◽  
Early Stage ◽  
Cortical Cell ◽  
Pyramidal Cells ◽  
Direct Calculation ◽  
Formal Theory ◽  
Cortical Cells ◽  
Cell Counts ◽  
Spatiotemporal Response

We present a model of visual computation based on tightly inter-connected cliques of pyramidal cells. It leads to a formal theory of cell assemblies, a specific relationship between correlated firing patterns and abstract functionality, and a direct calculation relating estimates of cortical cell counts to orientation hyperacuity. Our network architecture is unique in that (1) it supports a mode of computation that is both reliable and efficent; (2) the current-spike relations are modeled as an analog dynamical system in which the requisite computations can take place on the time scale required for an early stage of visual processing; and (3) the dynamics are triggered by the spatiotemporal response of cortical cells. This final point could explain why moving stimuli improve vernier sensitivity.

scholarly journals Depth in convolutional neural networks solves scene segmentation

2019 ◽  
Author(s):  
N Seijdel ◽  
N Tsakmakidis ◽  
EHF De Haan ◽  
SM Bohte ◽  
HS Scholte
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Visual Processing ◽  
Human Performance ◽  
Object Identification ◽  
Image Features ◽  
Background Information ◽  
Natural Scenes ◽  
Scene Segmentation ◽  
Deep Convolutional Neural Networks

AbstractFeedforward deep convolutional neural networks (DCNNs) are, under specific conditions, matching and even surpassing human performance in object recognition in natural scenes. This performance suggests that the analysis of a loose collection of image features could support the recognition of natural object categories, without dedicated systems to solve specific visual subtasks. Research in humans however suggests that while feedforward activity may suffice for sparse scenes with isolated objects, additional visual operations (‘routines’) that aid the recognition process (e.g. segmentation or grouping) are needed for more complex scenes. Linking human visual processing to performance of DCNNs with increasing depth, we here explored if, how, and when object information is differentiated from the backgrounds they appear on. To this end, we controlled the information in both objects and backgrounds, as well as the relationship between them by adding noise, manipulating background congruence and systematically occluding parts of the image. Results indicate that with an increase in network depth, there is an increase in the distinction between object- and background information. For more shallow networks, results indicated a benefit of training on segmented objects. Overall, these results indicate that, de facto, scene segmentation can be performed by a network of sufficient depth. We conclude that the human brain could perform scene segmentation in the context of object identification without an explicit mechanism, by selecting or “binding” features that belong to the object and ignoring other features, in a manner similar to a very deep convolutional neural network.

scholarly journals PENGEMBANGAN MEDIA ANIMASI BERBASIS MACROMEDIA FLASH PADA PELAJARAN FISIKA ALAT OPTIK

2013 ◽  
Vol 1 (2) ◽  
pp. 116
Author(s):  
Aris Doyan ◽  
Gunawan Gunawan ◽  
Bq Azmi Syukroyanti
Keyword(s):  
Learning Environment ◽  
Research And Development ◽  
Learning Process ◽  
Image Features ◽  
Media Content ◽  
Optical Media ◽  
The Media

Media is an important role in the learning process which will give effect to the understanding of the concepts and the learning result of students. Expected with the development of animation media based macromedia flash containing music and image features can provide a learning environment that is different from the usual. The purpose of this research is to develop an optical media-based animation tools of Macromedia Flash on the subjects of Physics Optical. The method used in this research is a method Research and development (R & D). The results show the development of media-based animation Macromedia Flash is needed creativity that generated media interest. The contents in the media based on the assessment of the three experts said optical media content animation tools are very good and worth using.

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