Simple Cells In Visual Cortex Research Articles

A robust contour detection operator with combined push-pull inhibition and surround suppression

Contour detection is a salient operation in many computer vision applications as it extracts features that are important for distinguishing objects in scenes. It is believed to be a primary role of simple cells in visual cortex of the mammalian brain. Many of such cells receive push-pull inhibition or surround suppression. We propose a computational model that exhibits a combination of these two phenomena. It is based on two existing models, which have been proven to be very effective for contour detection. In particular, we introduce a brain-inspired contour operator that combines push-pull and surround inhibition. It turns out that this combination results in a more effective contour detector, which suppresses texture while keeping the strongest responses to lines and edges, when compared to existing models. The proposed model consists of a Combination of Receptive Field (or CORF) model with push-pull inhibition, extended with surround suppression. We demonstrate the effectiveness of the proposed approach on the RuG and Berkeley benchmark data sets of 40 and 500 images, respectively. The proposed push-pull CORF operator with surround suppression outperforms the one without suppression with high statistical significance.

An Energy Computing Method Inspired from Visual Cognitive Function for Dynamic Behavioural Detection in Video Frames

The brain visual cortical simple cells have strong response to notable edges with directivity and contrast of light and dark, as well as the non-classical receptive fields of the neurons in visual cortex that have inhibition function to small light-spot stimulation. Because of this property, human vision system contrast sensitivity tends to dynamic videos. This paper, based on biological visual features, constructs an energy-computing model for dynamic video behaviors analysis, and designs computing methods for strengthening selectivity to directions of edges and inhibiting energy of non-significant areas in the images. The experiment is conducted on 30,000 frames of dynamic behaviors in video and shows 90% accuracy, which proves that the proposed method is capable to simulate the function of visual cortex simple cells, i.e. the enhancement to directional selection, and the inhabitation function of non-classical receptive fields, as well as extract energy features of dynamic behaviors in video. This contributes a choice for computer image processing and improves the understanding of machine vision.

Double-gabor filters are independent components of small translation-invariant image patches.

The analysis of natural images with independent component analysis (ICA) yields localized bandpass Gabor-type filters similar to receptive fields of simple cells in visual cortex. We applied ICA on a subset of patches called position-centered patches, selected for forming a translation-invariant representation of small patches. The resulting filters were qualitatively different in two respects. One novel feature was the emergence of filters we call double-Gabor filters. In contrast to Gabor functions that are modulated in one direction, double-Gabor filters are sinusoidally modulated in two orthogonal directions. In addition the filters were more extended in space and frequency compared to standard ICA filters and better matched the distribution in experimental recordings from neurons in primary visual cortex. We further found a dual role for double-Gabor filters as edge and texture detectors, which could have engineering applications.

Spatial frequency bandwidth of surround suppression tuning curves.

The contrast detection threshold of a grating located in the periphery is increased if a surrounding grating of the same frequency and orientation is present. This inhibition between center and surround has been termed surround suppression. In this work we measured the spatial frequency bandwidth of surround suppression in the periphery for different spatial frequencies (0.5, 1.1, 3, and 5 cycles/deg) of a sinusoidal grating (target) surrounded by a grating with different spatial frequencies (surround). Using a Bayesian adaptive staircase, we measured contrast detection thresholds in an 8AFC detection task in which the target (grating with a 2.3-deg Butterworth window) could appear in one of eight possible positions at 4° eccentricity. The target was surrounded by a grating (with a 18° Butterworth window) with the same or an orthogonal orientation. In each session we fixed the spatial frequency of the target and changed the spatial frequency and the orientation of the surround. When the surround was orthogonal to the target, the thresholds were similar to those obtained without surround, independent of the surrounding spatial frequency. However, when the target and surround had the same orientation and spatial frequency, the contrast threshold was increased by a factor ranging from 3 to 6 across subjects. This suppression reduced rapidly as the spatial frequency of the surround moved away from that of the target. The bandwidth of the suppressive effect depended on spatial frequency, declining from 2.9 octaves at 0.5 c/deg to 1 octave for frequencies above 3 c/deg. This is consistent with the bandwidth of individual simple cells in visual cortex and of spatial frequency channels measured psychophysically, both of which decline with increasing spatial frequency. This suggests that surround suppression may be due to relatively precise inhibition by cells with the same tuning as the target.

Symmetry Breakdown in the ON and OFF Pathways of the Retina at Night: Functional Implications

Several recent studies have shown that the ON and OFF channels of the visual system are not simple mirror images of each other, that their response characteristics are asymmetric (Chichilnisky and Kalmar, 2002; Sagdullaev and McCall, 2005). How the asymmetries bear on visual processing is not well understood. Here, we show that ON and OFF ganglion cells show a strong asymmetry in their temporal adaptation to photopic (day) and scotopic (night) conditions and that the asymmetry confers a functional advantage. Under photopic conditions, the ON and OFF ganglion cells show similar temporal characteristics. Under scotopic conditions, the two cell classes diverge-ON cells shift their tuning to low temporal frequencies, whereas OFF cells continue to respond to high. This difference in processing corresponds to an asymmetry in the natural world, one produced by the Poisson nature of photon capture and persists over a broad range of light levels. This work characterizes a previously unknown divergence in the ON and OFF pathways and its utility to visual processing. Furthermore, the results have implications for downstream circuitry and thus offer new constraints for models of downstream processing, since ganglion cells serve as building blocks for circuits in higher brain areas. For example, if simple cells in visual cortex rely on complementary interactions between the two pathways, such as push-pull interactions (Alonso et al., 2001; Hirsch, 2003), their receptive fields may be radically different under scotopic conditions, when the ON and OFF pathways are out of sync.

Functional alignment of feedback effects from visual cortex to thalamus

Following from the classical work of Hubel and Wiesel, it has been recognized that the orientation and the on- and off-zones of receptive fields of layer 4 simple cells in the visual cortex are linked to the spatial alignment and properties of the cells in the visual thalamus that relay the retinal input. Here we present evidence showing that the orientation and the on- and off-zones of receptive fields of layer 6 simple cells in cat visual cortex that provide feedback to the thalamus are similarly linked to the alignment and properties of the receptive fields of the thalamic cells they contact. However, the pattern of influence linked to on- and off-zones is phase-reversed. This has important functional implications.

Haphazard wiring of simple receptive fields and orientation columns in visual cortex.

The receptive fields of simple cells in visual cortex are composed of elongated on and off subregions. This spatial arrangement is widely thought to be responsible for the generation of orientation selectivity. Neurons with similar orientation preferences cluster in "columns" that tile the cortical surface and form a map of orientation selectivity. It has been proposed that simple cell receptive fields are constructed by the selective pooling of geniculate receptive fields aligned in space. A recent analysis of monosynaptic connections between geniculate and cortical neurons appears to reveal the existence of "wiring rules" that are in accordance with the classical model. The precise origin of the orientation map is unknown, but both genetic and activity-dependent processes are thought to contribute. Here, we put forward the hypothesis that statistical sampling from the retinal ganglion cell mosaic may contribute to the generation of simple cells and provide a blueprint for orientation columns. Results from computer simulations show that the "haphazard wiring" model is consistent with data on the probability of monosynaptic connections and generates orientation columns and maps resembling those found in the cortex. The haphazard wiring hypothesis could be tested by measuring the correlation between the orientation map and the structure of the retinal ganglion cell mosaic of the contralateral eye.

Methods for first-order kernel estimation: simple-cell receptive fields from responses to natural scenes

Recent studies have recovered receptive-field maps of simple cells in visual cortex from their responses to natural scene stimuli. Natural scenes have many theoretical and practical advantages over traditional, artificial stimuli; however, the receptive-field estimation methods are more complex than for white-noise stimuli. Here, we describe and justify several of these methods-spectral correction of the reverse correlation estimate, direct least-squares solution, iterative least-squares algorithms and regularized least-squares solutions. We investigate the pros and cons of the different methods, and evaluate them in a head-to-head comparison for simulated simple-cell data. This shows that, at least for quasilinear simulated simple cells, a regularized solution ('reginv') is most efficient, requiring fewer stimulus presentations for high-resolution reconstruction of the first-order kernel. We also investigate several practical issues that determine the success of this kind of experiment-the effects of neuronal nonlinearities, response variability and the choice of stimulus regime.

Receptive Fields and Orientation Selectivity of SimpleCells in Visual Cortex

We present a three-layer feedforward visual pathway model consisting of retina, LGN and cortex for development of orientation selectivity in layer 4 simple cells. Receptive field (RF) development in the model, is determined by diffusive cooperation and resource limited competition guided axonal growth and retraction in geniculocortical pathway. The simulated cortical RFs resemble experimental RFs. In the feed forward model when only the linear contribution from LGN was considered we obtained a mean hwhh (half width at half the maximum response) of 58°. The mean hwhh improved to 37° when nonlinearity in spiking mechanism was incorporated. The hwhh in the cells improved by another 9° when a push-pull effect was included. The tuning in the simulated cells matches reported tuning in layer 4 simple cells.

Methods for first-order kernel estimation: simple-cell receptive fields from responses to natural scenes

Recent studies have recovered receptive-field maps of simple cells in visual cortex from their responses to natural scene stimuli. Natural scenes have many theoretical and practical advantages over traditional, artificial stimuli; however, the receptive-field estimation methods are more complex than for white-noise stimuli. Here, we describe and justify several of these methods—spectral correction of the reverse correlation estimate, direct least-squares solution, iterative least-squares algorithms and regularized least-squares solutions. We investigate the pros and cons of the different methods, and evaluate them in a head-to-head comparison for simulated simple-cell data. This shows that, at least for quasilinear simulated simple cells, a regularized solution (‘reginv’) is most efficient, requiring fewer stimulus presentations for high-resolution reconstruction of the first-order kernel. We also investigate several practical issues that determine the success of this kind of experiment—the effects of neuronal nonlinearities, response variability and the choice of stimulus regime.

A neural network model on self-organizing emergence of simple-cell receptive field with orientation selectivity in visual cortex

In order to probe into the self-organizing emergence of simple cell orientation selectivity, we tried to construct a neural network model that consists of LGN neurons and simple cells in visual cortex and obeys the Hebbian learning rule. We investigated the neural coding and representation of simple cells to a natural image by means of this model. The results show that the structures of their receptive fields are determined by the preferred orientation selectivity of simple cells. However, they are also decided by the emergence of self-organization in the unsupervision learning process. This kind of orientation selectivity results from dynamic self-organization based on the interactions between LGN and cortex.

Signatures of natural image statistics in cortical simple cell receptive fields

Receptive fields of simple cells in visual cortex in cats are known to be shaped like Gabor functions with respect to retinal coordinates. As shown by Miller (J. Neurosci. 14 (1994) 409), oriented receptive fields may emerge prenatally from spontaneous and unspecific retinal activity. On the other hand, a striking resemblance of components of natural images to simple cell receptive field has been reported by several authors. We demonstrate that it is possible to differentiate between the spontaneous pattern formation scenario and postnatal stimulus-driven self-organization of receptive fields. Analyzing Gabor-fits of computationally generated receptive fields of simple model neurons we conclude that natural-scene stimuli are essential for the explanation of certain features of experimentally measured receptive fields.

Statistics of lateral geniculate nucleus (LGN) activity determine the segregation of ON/OFF subfields for simple cells in visual cortex

The receptive fields for simple cells in visual cortex show a strong preference for edges of a particular orientation and display adjacent excitatory and inhibitory subfields. These subfields are projections from ON-center and OFF-center lateral geniculate nucleus cells, respectively. Here we present a single-cell model using ON and OFF channels, a natural scene environment, and synaptic modification according to the Bienenstock, Cooper, and Munro (BCM) theory. Our results indicate that lateral geniculate nucleus cells must act predominantly in the linear region around the level of spontaneous activity, to lead to the observed segregation of ON/OFF subfields.

The "independent components" of natural scenes are edge filters.

It has previously been suggested that neurons with line and edge selectivities found in primary visual cortex of cats and monkeys form a sparse, distributed representation of natural scenes, and it has been reasoned that such responses should emerge from an unsupervised learning algorithm that attempts to find a factorial code of independent visual features. We show here that a new unsupervised learning algorithm based on information maximization, a nonlinear "infomax" network, when applied to an ensemble of natural scenes produces sets of visual filters that are localized and oriented. Some of these filters are Gabor-like and resemble those produced by the sparseness-maximization network. In addition, the outputs of these filters are as independent as possible, since this infomax network performs Independent Components Analysis or ICA, for sparse (super-gaussian) component distributions. We compare the resulting ICA filters and their associated basis functions, with other decorrelating filters produced by Principal Components Analysis (PCA) and zero-phase whitening filters (ZCA). The ICA filters have more sparsely distributed (kurtotic) outputs on natural scenes. They also resemble the receptive fields of simple cells in visual cortex, which suggests that these neurons form a natural, information-theoretic coordinate system for natural images.

Retinal constraints on orientation specificity in cat visual cortex

Most retinal ganglion cells (Levick and Thibos, 1982) and cortical cells (Leventhal, 1983; Leventhal et al., 1984) subserving peripheral vision respond best to stimuli that are oriented radially, i.e., like the spokes of a wheel with the area centralis at the hub. We have extended this work by comparing directly the distributions of orientations represented in topographically corresponding regions of retina and visual cortex. Both central and peripheral regions were studied. The relations between the orientations of neighboring ganglion cells and the manner in which the overrepresentation of radial orientations is accommodated in the functional architecture of visual cortex were also studied. Our results are based on an analysis of the orientations of the dendritic fields of 1296 ganglion cells throughout the retina and the preferred orientations of 1389 cells located in retinotopically corresponding regions of cortical areas 17, 18, and 19 in the cat. We find that horizontal and vertical orientations are overrepresented in regions of both retina and visual cortex subserving the central 5 degrees of vision. The distributions of the orientations of retinal ganglion cells and cortical cells subserving the horizontal, vertical, and diagonal meridians outside the area centralis differ significantly. The distribution of the preferred orientations of the S (simple) cells in areas 17, 18 and 19 subserving a given part of the retina corresponds to the distribution of the dendritic field orientations of the ganglion cells in that part of retina. The distribution of the preferred orientations of C (complex) cells with narrow receptive fields in area 17 but not C cells with wide receptive fields in areas 17, 18, or 19 subserving a given part of the retina matches the distribution of the orientations of the ganglion cells in that part of retina. The orientations of all of the alpha-cells in 5-9 mm2 patches of retina along the horizontal, vertical, and oblique meridians were determined. A comparison of the orientations of neighboring cells indicates that other than a mutual tendency to be oriented radially, ganglion cells with similar orientations are not clustered in the retina. Reconstructions of electrode penetrations into regions of visual cortex representing peripheral retina indicate that columns subserving radial orientations are wider than those subserving nonradial orientations. Our results provide evidence that the distribution of the preferred orientations of simple cells in visual cortex subserving any region of the visual field matches the distribution of the orientations of the ganglion cells subserving the same region of the visual field.(ABSTRACT TRUNCATED AT 400 WORDS)

A functional model of the wiring of the simple cells of visual cortex.

A computer model of the simple cells in the mammalian visual cortex was constructed. The model cells received inputs from a great number of isopolar centre/surround cells assumed to be located in the lateral geniculate nucleus (LGN). The distribution of input to the model simple cells was either inhibitory/excitatory or inhibitory/excitatory/inhibitory. Such arrangements produced receptive fields containing four or five consecutively antagonistic subfields. Responses produced by the model cells to different types of stimuli (periodical as well as nonperiodical) were obtained and compared to responses of living cells reported from various laboratories under comparable stimulus conditions. In all the situations tested, the responses of the model cells corresponded qualitatively very well to those of living cells. It was seen that the same wiring mechanism was able to account for orientation selectivity, spatial frequency filtering, various phase relationships between stimulus and response, subfield orientational selectivity, and slight end-inhibition. Furthermore, the receptive fields of the model simple cells closely resemble Gabor functions.