Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics

Spatial context in images induces perceptual phenomena associated with salience and modulates the responses of neurons in primary visual cortex (V1). However, the computational and ecological principles underlying contextual effects are incompletely understood. We introduce a model of natural images that includes grouping and segmentation of neighboring features based on their joint statistics, and we interpret the firing rates of V1 neurons as performing optimal recognition in this model. We show that this leads to a substantial generalization of divisive normalization, a computation that is ubiquitous in many neural areas and systems. A main novelty in our model is that the influence of the context on a target stimulus is determined by their degree of statistical dependence. We optimized the parameters of the model on natural image patches, and then simulated neural and perceptual responses on stimuli used in classical experiments. The model reproduces some rich and complex response patterns observed in V1, such as the contrast dependence, orientation tuning and spatial asymmetry of surround suppression, while also allowing for surround facilitation under conditions of weak stimulation. It also mimics the perceptual salience produced by simple displays, and leads to readily testable predictions. Our results provide a principled account of orientation-based contextual modulation in early vision and its sensitivity to the homogeneity and spatial arrangement of inputs, and lends statistical support to the theory that V1 computes visual salience

Correlated topographic analysis: estimating an ordering of correlated components

Abstract This paper describes a novel method, which we call correlated topographic analysis (CTA), to estimate non-Gaussian components and their ordering (topography). The method is inspired by a central motivation of recent variants of independent component analysis (ICA), namely, to make use of the residual statistical dependency which ICA cannot remove. We assume that components nearby on the topographic arrangement have both linear and energy correlations, while far-away components are statistically independent. We use these dependencies to fix the ordering of the components. We start by proposing the generative model for the components. Then, we derive an approximation of the likelihood based on the model. Furthermore, since gradient methods tend to get stuck in local optima, we propose a three-step optimization method which dramatically improves topographic estimation. Using simulated data, we show that CTA estimates an ordering of the components and generalizes a previous method in terms of topography estimation. Finally, to demonstrate that CTA is widely applicable, we learn topographic representations for three kinds of real data: natural images, outputs of simulated complex cells and text data

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Visual attention and flexible normalization pools

Attention to a spatial location or feature in a visual scene can modulate the responses of cortical neurons and affect perceptual biases in illusions. We add attention to a cortical model of spatial context based on a well-founded account of natural scene statistics. The cortical model amounts to a generalized form of divisive normalization, in which the surround is in the normalization pool of the center target only if they are considered statistically dependent. Here we propose that attention influences this computation by accentuating the neural unit activations at the attended location, and that the amount of attentional influence of the surround on the center thus depends on whether center and surround are deemed in the same normalization pool. The resulting form of model extends a recent divisive normalization model of attention (Reynolds & Heeger, 2009). We simulate cortical surround orientation experiments with attention and show that the flexible model is suitable for capturing additional data and makes nontrivial testable predictions

The impact on midlevel vision of statistically optimal divisive normalization in V1

The first two areas of the primate visual cortex (V1, V2) provide a paradigmatic example of hierarchical computation in the brain. However, neither the functional properties of V2 nor the interactions between the two areas are well understood. One key aspect is that the statistics of the inputs received by V2 depend on the nonlinear response properties of V1. Here, we focused on divisive normalization, a canonical nonlinear computation that is observed in many neural areas and modalities. We simulated V1 responses with (and without) different forms of surround normalization derived from statistical models of natural scenes, including canonical normalization and a statistically optimal extension that accounted for image nonhomogeneities. The statistics of the V1 population responses differed markedly across models. We then addressed how V2 receptive fields pool the responses of V1 model units with different tuning. We assumed this is achieved by learning without supervision a linear representation that removes correlations, which could be accomplished with principal component analysis. This approach revealed V2-like feature selectivity when we used the optimal normalization and, to a lesser extent, the canonical one but not in the absence of both. We compared the resulting two-stage models on two perceptual tasks; while models encompassing V1 surround normalization performed better at object recognition, only statistically optimal normalization provided systematic advantages in a task more closely matched to midlevel vision, namely figure/ground judgment. Our results suggest that experiments probing midlevel areas might benefit from using stimuli designed to engage the computations that characterize V1 optimality

Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing

A central hypothesis about early visual processing is that it represents inputs in a coordinate system matched to the statistics of natural scenes. Simple versions of this lead to Gabor-like receptive fields and divisive gain modulation from local surrounds; these have led to influential neural and psychological models of visual processing. However, these accounts are based on an incomplete view of the visual context surrounding each point. Here, we consider an approximate model of linear and non-linear correlations between the responses of spatially distributed Gabor-like receptive fields, which, when trained on an ensemble of natural scenes, unifies a range of spatial context effects. The full model accounts for neural surround data in primary visual cortex (V1), provides a statistical foundation for perceptual phenomena associated with Lis (2002) hypothesis that V1 builds a saliency map, and fits data on the tilt illusion

What the draughtman's hand tells the draughtman's eye : a sensorimotor account of drawing

In this paper we address the challenging problem of sensorimotor integration, with reference to eye-hand coordination of an artificial agent engaged in a natural drawing task. Under the assumption that eye-hand coupling influences observed movements, a motor continuity hypothesis is exploited to account for how gaze shifts are constrained by hand movements. A Bayesian model of such coupling is presented in the form of a novel Dynamic Bayesian Network, namely an Input\u2013Output Coupled Hidden Markov Model. Simulation results are compared to those obtained by eye-tracked human subjects involved in drawing experiments

The Bayesian Draughtsman : a model for visuomotor coordination in drawing

In this article we present a model of realistic drawing accounting for visuomotor coordination, namely the strategies adopted to coordinate the processes of eye and hand movement generation, during the drawing task. Starting from some background assumptions suggested by eye-tracking human subjects, we formulate a Bayesian model of drawing activity. The resulting graphical model is shaped in the form of a Dynamic Bayesian Network that combines features of both the Input\u2013Output Hidden Markov Model and the Coupled Hidden Markov Model, and provides an interesting insight on mechanisms for dynamic integration of visual and proprioceptive information