this paper, we propose a Bayesian theory of hierarchical cortical computation based both on (a) the mathematical and computational ideas of computer vision and pattern the- ory and on (b) recent neurophysiological experimental evidence. We ,2 have proposed that Grenander's pattern theory 3 could potentially model the brain as a generafive model in such a way that feedback serves to disambiguate and 'explain away' the earlier representa- tion. The Helmholtz machine 4, 5 was an excellent step towards approximating this proposal, with feedback implementing priors. Its development, however, was rather limited, dealing only with binary images. Moreover, its feedback mechanisms were engaged only during the learning of the feedforward connections but not during perceptual inference, though the Gibbs sampling process for inference can potentially be interpreted as top-down feedback disambiguating low level representations? Rao and Ballard's predictive coding/Kalman filter model 6 did integrate generafive feedback in the perceptual inference process, but it was primarily a linear model and thus severely limited in practical utility. The data-driven Markov Chain Monte Carlo approach of Zhu and colleagues 7, 8 might be the most successful recent application of this proposal in solving real and difficult computer vision problems using generafive models, though its connection to the visual cortex has not been explored. Here, we bring in a powerful and widely applicable paradigm from artificial intelligence and computer vision to propose some new ideas about the algorithms of visual cortical process- ing and the nature of representations in the visual cortex. We will review some of our and others' neurophysiological experimental data to lend support to these ideas

The statistical properties of natural images suggest an optimal form of nonlinear decomposition, in which the image is decomposed using a set of linear filters at a variety of positions, scales and orientations, and these linear responses are then rectified and divided by a weighted sum of rectified responses of nearby filters. Such divisive normalization models have become widely used in modeling steady-state responses of neurons in primary visual cortex. In addition to providing a surprisingly good characterization of "typical" neurons, the statistically optimal version of the model is consistent with unusual changes in tuning properties of these neurons at different contrast levels. These results suggest that the nonlinear response properties of cortical neurons are not an accident of biophysical implementation, but serve an important functional role.

In the classical feed-forward, modular view of visual processing, the primary visual cortex (area V1) is a module that serves to extract local features such as edges and bars. Representation and recognition of objects are thought to be functions of higher extrastriate cortical areas. This paper presents neurophysiological data that show the later part of V1 neurons' responses reflecting higher order perceptual computations related to Ullman's (Cognition 1984;18:97 -- 159) visual routines and Marr's (Vision NJ: Freeman 1982) full primal sketch, 2 1 2 D sketch and 3D model. Based on theoretical reasoning and the experimental evidence, we propose a possible reinterpretation of the functional role of V1. In this framework, because of V1 neurons' precise encoding of orientation and spatial information, higher level perceptual computations and representations that involve high resolution details, fine geometry and spatial precision would necessarily involve V1 and be reflected in the later...

Nature and interaction of signals from the receptive field center and surround in macaque V1 neurons. J Neurophysiol 88: 2530–2546, 2002; 10.1152/jn.00692.2001. Information is integrated across the visual field to transform local features into a global percept. We now know that V1 neurons provide more spatial integration than originally thought due to the existence of their nonclassical inhibitory surrounds. To understand spatial integration in the visual cortex, we have studied the nature and extent of center and surround influences on neuronal response. We used drifting sinusoidal gratings in circular and annular apertures to estimate the sizes of the receptive field’s excitatory center and suppressive surround. We used combinations of stimuli inside and outside the receptive field to explore the nature of the surround influence on the receptive field center as a function of the relative and absolute contrast of stimuli in the two regions. We conclude that the interaction is best explained as a divisive modulation of response gain

the properties of salient contours. These interactions are manifest at the cellular level in the facilitation by lines lying outside of the classical receptive field when presented simultaneously with a collinear line segment lying inside the receptive field center (Kapadia et al., 1995). Attention may be directed either toward a location in space or toward particular objects or stimulus configurations. Object-oriented attention is related to percep-tual learning, in that improvement in the discriminability of visual stimulus attributes is intimately related to the configuration within which the discriminated feature is presented. The specificity for visual field location of perceptual learning seen in psychophysical studies suggests involvement of early stages in visual cortical processing. The further specificity for stimulus configu-ration suggests a possible interaction between top-down influences and these early levels (Shiu and Pashler,

We propose a biologically motivated computational step, called nonclassical receptive field (non-CRF) inhibition, more generally surround inhibition or suppression, to improve contour detection in machine vision. Non-CRF inhibition is exhibited by 80 % of the orientation-selective neurons in the primary visual cortex of monkeys and has been demonstrated to influence the visual perception of man as well. The essence of this mechanism is that the response of an edge detector in a certain point is suppressed by the responses of the operator in the region outside the area of operator support. We combine classical edge detection with two types of inhibitory mechanism, isotropic and anisotropic inhibition, both of which have counterparts in biology. For edge detection, we also use a biologically motivated method (the Gabor energy operator). The resulting operator responds strongly to isolated lines, edges, and contours, but exhibits a weaker or no response to edges that make part of texture. We use natural images with associated ground truth contour maps to assess the performance of the proposed operator regarding the detection of contours while suppressing texture edges. The results show that our method enhances contour detection in cluttered visual scenes more effectively than classical edge detectors used in machine vision (Canny edge detector). Therefore, the proposed operator is more useful for contour-based object recognition tasks, such as shape comparison, than traditional edge detectors, which do not distinguish between contour and texture edges. Traditional edge detection algorithms can, however, also be extended with surround suppression. Next to the advancement of contour detection in machine vision, this study contributes to the understanding of inhibitory mechanisms in biology.

A detailed neural model is presented of how the laminar circuits of visual cortical areas V1 and V2 implement context-sensitive binding processes such as perceptual grouping and attention. The model proposes how specific laminar circuits allow the responses of visual cortical neurons to be determined not only by the stimuli within their classical receptive fields, but also to be strongly influenced by stimuli in the extra-classical surround. This context-sensitive visual processing can greatly enhance the analysis of visual scenes, especially those containing targets that are low contrast, partially occluded, or crowded by distractors. We show how interactions of feedforward, feedback and horizontal circuitry can implement several types of contextual processing simultaneously, using shared laminar circuits. In particular, we present computer simulations which suggest how top-down attention and preattentive perceptual grouping, two processes that are fundamental for visual binding, can interact, with attentional enhancement selectively propagating along groupings of both real and illusory contours, thereby showing how attention can selectively enhance object representations. These simulations also illustrate how attention may have a stronger facilitatory

Abstract not found

TABLE OF CONTENTS 2.1. Visual areas are defined by receptive field tuning properties 2.2. Combining the distributed information

Understanding the functional role of neurons and neural systems is a primary goal of systems neuroscience. A longstanding hypothesis states that sensory systems are matched to the statistical properties of the signals to which they are exposed [e.g., 4, 6]. In particular, Barlow has proposed that the role of early sensory systems is to remove redundancy in the sensory input, resulting in a set of neural responses that are statistically independent. Variants of this hypothesis have been formulated by a number of other authors [e.g., 2, 52] (see [47] for a review). The basic version assumes a fixed environmental model, but Barlow and Foldiak later augmented the theory by suggesting that adaptation in neural systems might be thought of as an adjustment to remove redundancies in the responses to recently presented stimuli [8, 7].