766 research outputs found

    Computational role of eccentricity dependent cortical magnification

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    We develop a sampling extension of M-theory focused on invariance to scale and translation. Quite surprisingly, the theory predicts an architecture of early vision with increasing receptive field sizes and a high resolution fovea -- in agreement with data about the cortical magnification factor, V1 and the retina. From the slope of the inverse of the magnification factor, M-theory predicts a cortical "fovea" in V1 in the order of 4040 by 4040 basic units at each receptive field size -- corresponding to a foveola of size around 2626 minutes of arc at the highest resolution, 6\approx 6 degrees at the lowest resolution. It also predicts uniform scale invariance over a fixed range of scales independently of eccentricity, while translation invariance should depend linearly on spatial frequency. Bouma's law of crowding follows in the theory as an effect of cortical area-by-cortical area pooling; the Bouma constant is the value expected if the signature responsible for recognition in the crowding experiments originates in V2. From a broader perspective, the emerging picture suggests that visual recognition under natural conditions takes place by composing information from a set of fixations, with each fixation providing recognition from a space-scale image fragment -- that is an image patch represented at a set of increasing sizes and decreasing resolutions

    A model of bottom-up visual attention using cortical magnification

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    International audienceThe focus of visual attention has been argued to play a key role in object recognition. Many computational models of visual attention were proposed to estimate locations of eye fixations driven by bottom-up stimuli. Most of these models rely on pyramids consisting of multiple scaled versions of the visual scene. This design aims at capturing the fact that neural cells in higher visual areas tend to have larger receptive fields (RFs). On the other hand, very few models represent multi-scaling resulting from the eccentricity-dependent RF sizes within each visual layer, also known as the cortical magnification effect. In this paper, we demonstrate that using a cortical-magnification-like mechanism can lead to performant alternatives to pyramidal approaches in the context of attentional modeling. Moreover, we argue that introducing such a mechanism equips the proposed model with additional properties related to overt attention and distance-dependent saliency that are worth exploring

    Can retinal ganglion cell dipoles seed iso-orientation domains in the visual cortex?

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    It has been argued that the emergence of roughly periodic orientation preference maps (OPMs) in the primary visual cortex (V1) of carnivores and primates can be explained by a so-called statistical connectivity model. This model assumes that input to V1 neurons is dominated by feed-forward projections originating from a small set of retinal ganglion cells (RGCs). The typical spacing between adjacent cortical orientation columns preferring the same orientation then arises via Moir\'{e}-Interference between hexagonal ON/OFF RGC mosaics. While this Moir\'{e}-Interference critically depends on long-range hexagonal order within the RGC mosaics, a recent statistical analysis of RGC receptive field positions found no evidence for such long-range positional order. Hexagonal order may be only one of several ways to obtain spatially repetitive OPMs in the statistical connectivity model. Here, we investigate a more general requirement on the spatial structure of RGC mosaics that can seed the emergence of spatially repetitive cortical OPMs, namely that angular correlations between so-called RGC dipoles exhibit a spatial structure similar to that of OPM autocorrelation functions. Both in cat beta cell mosaics as well as primate parasol receptive field mosaics we find that RGC dipole angles are spatially uncorrelated. To help assess the level of these correlations, we introduce a novel point process that generates mosaics with realistic nearest neighbor statistics and a tunable degree of spatial correlations of dipole angles. Using this process, we show that given the size of available data sets, the presence of even weak angular correlations in the data is very unlikely. We conclude that the layout of ON/OFF ganglion cell mosaics lacks the spatial structure necessary to seed iso-orientation domains in the primary visual cortex.Comment: 9 figures + 1 Supplementary figure and 1 Supplementary tabl

    A Neural Model of Motion Processing and Visual Navigation by Cortical Area MST

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    Cells in the dorsal medial superior temporal cortex (MSTd) process optic flow generated by self-motion during visually-guided navigation. A neural model shows how interactions between well-known neural mechanisms (log polar cortical magnification, Gaussian motion-sensitive receptive fields, spatial pooling of motion-sensitive signals, and subtractive extraretinal eye movement signals) lead to emergent properties that quantitatively simulate neurophysiological data about MSTd cell properties and psychophysical data about human navigation. Model cells match MSTd neuron responses to optic flow stimuli placed in different parts of the visual field, including position invariance, tuning curves, preferred spiral directions, direction reversals, average response curves, and preferred locations for stimulus motion centers. The model shows how the preferred motion direction of the most active MSTd cells can explain human judgments of self-motion direction (heading), without using complex heading templates. The model explains when extraretinal eye movement signals are needed for accurate heading perception, and when retinal input is sufficient, and how heading judgments depend on scene layouts and rotation rates.Defense Research Projects Agency (N00014-92-J-4015); Office of Naval Research (N00014-92-J-1309, N00014-95-1-0409, N00014-95-1-0657, N00014-91-J-4100, N0014-94-I-0597); Air Force Office of Scientific Research (F49620-92-J-0334)

    The Peri-Saccadic Perception of Objects and Space

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    Eye movements affect object localization and object recognition. Around saccade onset, briefly flashed stimuli appear compressed towards the saccade target, receptive fields dynamically change position, and the recognition of objects near the saccade target is improved. These effects have been attributed to different mechanisms. We provide a unifying account of peri-saccadic perception explaining all three phenomena by a quantitative computational approach simulating cortical cell responses on the population level. Contrary to the common view of spatial attention as a spotlight, our model suggests that oculomotor feedback alters the receptive field structure in multiple visual areas at an intermediate level of the cortical hierarchy to dynamically recruit cells for processing a relevant part of the visual field. The compression of visual space occurs at the expense of this locally enhanced processing capacity

    Modeling Magnification and Anisotropy in the Primate Foveal Confluence

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    A basic organizational principle of the primate visual system is that it maps the visual environment repeatedly and retinotopically onto cortex. Simple algebraic models can be used to describe the projection from visual space to cortical space not only for V1, but also for the complex of areas V1, V2 and V3. Typically a conformal (angle-preserving) projection ensuring local isotropy is regarded as ideal and primate visual cortex is often regarded as an approximation of this ideal. However, empirical data show systematic deviations from this ideal that are especially relevant in the foveal projection. The aims of this study were to map the nature of anisotropy predicted by existing models, to investigate the optimization targets faced by different types of retino-cortical maps, and finally to propose a novel map that better models empirical data than other candidates. The retino-cortical map can be optimized towards a space-conserving homogenous representation or a quasi-conformal mapping. The latter would require a significantly enlarged representation of specific parts of the cortical maps. In particular it would require significant enlargement of parafoveal V2 and V3 which is not supported by empirical data. Further, the recently published principal layout of the foveal singularity cannot be explained by existing models. We suggest a new model that accurately describes foveal data, minimizing cortical surface area in the periphery but suggesting that local isotropy dominates the most foveal part at the expense of additional cortical surface. The foveal confluence is an important example of the detailed trade-offs between the compromises required for the mapping of environmental space to a complex of neighboring cortical areas. Our models demonstrate that the organization follows clear morphogenetic principles that are essential for our understanding of foveal vision in daily life

    Do Deep Neural Networks Suffer from Crowding?

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    Crowding is a visual effect suffered by humans, in which an object that can be recognized in isolation can no longer be recognized when other objects, called flankers, are placed close to it. In this work, we study the effect of crowding in artificial Deep Neural Networks for object recognition. We analyze both standard deep convolutional neural networks (DCNNs) as well as a new version of DCNNs which is 1) multi-scale and 2) with size of the convolution filters change depending on the eccentricity wrt to the center of fixation. Such networks, that we call eccentricity-dependent, are a computational model of the feedforward path of the primate visual cortex. Our results reveal that the eccentricity-dependent model, trained on target objects in isolation, can recognize such targets in the presence of flankers, if the targets are near the center of the image, whereas DCNNs cannot. Also, for all tested networks, when trained on targets in isolation, we find that recognition accuracy of the networks decreases the closer the flankers are to the target and the more flankers there are. We find that visual similarity between the target and flankers also plays a role and that pooling in early layers of the network leads to more crowding. Additionally, we show that incorporating the flankers into the images of the training set does not improve performance with crowding.Comment: CBMM mem

    Models of learning in the visual system: dependence on retinal eccentricity

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    In the primary visual cortex of primates relatively more space is devoted to the representation of the central visual field in comparison to the representation of the peripheral visual field. Experimentally testable theories about the factors and mechanisms which may have determined this inhomogeneous mapping may provide valuable insights into general processing principles in the visual system. Therefore, I investigated to which visual situations this inhomogeneous representation of the visual field is well adapted, and which mechanisms could support its refinement and stabilization during individual development. Furthermore, I studied possible functional consequences of the inhomogeneous representation for visual processing at central and peripheral locations of the visual field. Vision plays an important role during navigation. Thus, visual processing should be well adapted to self-motion. Therefore, I assumed that spatially inhomogeneous retinal velocity distributions, caused by static objects during self-motion along the direction of gaze, are transformed on average into spatially homogeneous cortical velocity distributions. This would have the advantage that the cortical mechanisms, concerned with the processing of self-motion, can be identical in their spatial and temporal properties across the representation of the whole visual field. This is the case if the arrangement of objects relative to the observer corresponds to an ellipsoid with the observer in its center. I used the resulting flow field to train a network model of pulse coding neurons with a Hebbian learning rule. The distribution of the learned receptive fields is in agreement with the inhomogeneous cortical representation of the visual field. These results suggest that self motion may have played an important role in the evolution of the visual system and that the inhomogeneous cortical representation of the visual field can be refined and stabilized by Hebbian learning mechanisms during ontogenesis under natural viewing conditions. In addition to the processing of self-motion, an important task of the visual system is the grouping and segregation of local features within a visual scene into coherent objects. Therefore, I asked how the corresponding mechanisms depend on the represented position of the visual field. It is assumed that neuronal connections within the primary visual cortex subserve this grouping process. These connections develop after eye-opening in dependence on the visual input. How does the lateral connectivity depend on the represented position of the visual field? With increasing eccentricity, primary cortical receptive fields become larger and the cortical magnification of the visual field declines. Therefore, I investigated the spatial statistics of real-world scenes with respect to the spatial filter-properties of cortical neurons at different locations of the visual field. I show that correlations between collinearly arranged filters of the same size and orientation increase with increasing filter size. However, in distances relative to the size of the filters, collinear correlations decline more steeply with increasing distance for larger filters. This provides evidence against a homogeneous cortical connectivity across the whole visual field with respect to the coding of spatial object properties. Two major retino-cortical pathways are the magnocellular (M) and the parvocellular (P) pathways. While neurons along the M-pathway display temporal bandpass characteristics, neurons along the P-pathway show temporal lowpass characteristics. The ratio of P- to M-cells is not constant across the whole visual field, but declines with increasing retinal eccentricity. Therefore, I investigated how the different temporal response-properties of neurons of the M- and the P-pathways influence self-organization in the visual cortex, and discussed possible consequences for the coding of visual objects at different locations of the visual field. Specifically, I studied the influence of stimulus-motion on the self-organization of lateral connections in a network-model of spiking neurons with Hebbian learning. Low stimulus velocities lead to horizontal connections well adapted to the coding of the spatial structure within the visual input, while higher stimulus velocities lead to connections which subserve the coding of the stimulus movement direction. This suggests that the temporal lowpass properties of P-neurons subserve the coding of spatial stimulus attributes (form) in the visual cortex, while the temporal bandpass properties of M-neurons support the coding of spatio-temporal stimulus attributes (movement direction). Hence, the central representation of the visual field may be well adapted to the encoding of spatial object properties due to the strong contribution of P-neurons. The peripheral representation may be better adapted to the processing of motion
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