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

Model Cortical Association Fields Account for the Time Course and Dependence on Target Complexity of Human Contour Perception

Can lateral connectivity in the primary visual cortex account for the time dependence and intrinsic task difficulty of human contour detection? To answer this question, we created a synthetic image set that prevents sole reliance on either low-level visual features or high-level context for the detection of target objects. Rendered images consist of smoothly varying, globally aligned contour fragments (amoebas) distributed among groups of randomly rotated fragments (clutter). The time course and accuracy of amoeba detection by humans was measured using a two-alternative forced choice protocol with self-reported confidence and variable image presentation time (20-200 ms), followed by an image mask optimized so as to interrupt visual processing. Measured psychometric functions were well fit by sigmoidal functions with exponential time constants of 30-91 ms, depending on amoeba complexity. Key aspects of the psychophysical experiments were accounted for by a computational network model, in which simulated responses across retinotopic arrays of orientation-selective elements were modulated by cortical association fields, represented as multiplicative kernels computed from the differences in pairwise edge statistics between target and distractor images. Comparing the experimental and the computational results suggests that each iteration of the lateral interactions takes at least ms of cortical processing time. Our results provide evidence that cortical association fields between orientation selective elements in early visual areas can account for important temporal and task-dependent aspects of the psychometric curves characterizing human contour perception, with the remaining discrepancies postulated to arise from the influence of higher cortical areas

Modeling visual cortical contrast adaptation effects

We demonstrate a detailed visual cortical circuit which exhibits robust contrast adaptation properties, consistent with physiological observations in V1. The adaptation mechanism we employ is activity-dependent synaptic depression at thalamocortical and local intra-cortical synapses. Model contrast response functions (CRF) shift so that cells remain maximally responsive tochanges around the recent average stimulus contrast level. Hysteresis e ects for both stimulus contrast and orientation are achieved; orientation hysteresis is weaker, and depends exclusively on intracortical adaptation. Following stimulation of the receptive eld (RF) surround, RFs dynamically expand to \ ll in " for the missing stimulation in the RF center; in our model this expansion results from adaptation of local inhibitory synapses, triggered by excitation from long range horizontal projections. All adaptation effects are achieved using the same synaptic depression mechanism at both thalamocortical and intracortical synapses

A Unified Neural Network Model for the Self-organization of Topographic Receptive Fields and Lateral Interaction

A self-organizing neural network model for the simultaneous development of topographic receptive fields and lateral interactions in cortical maps is presented. Both afferent and lateral connections adapt by the same Hebbian mechanism in a purely local and unsupervised learning process. Afferent input weights of each neuron self-organize into hill-shaped profiles, receptive fields organize topographically across the network, and unique lateral interaction profiles develop for each neuron. The resulting self-organized structure remains in a dynamic and continuously-adapting equilibrium with the input. The model can be seen as a generalization of previous self-organizing models of the visual cortex, and provides a general computational framework for experiments on receptive field development and cortical plasticity. The model also serves to point out general limits on activity-dependent self-organization: when multiple inputs are presented simultaneously, the receptive field centers need ..

Cortical Synchronization And Perceptual Salience

We present a striate-cortical model which proposes a direct relationship between cellular synchronization and perceptual salience. The model focuses on the role of the longrange horizontal connections between oriented simple cells in striate cortex and is able to account for current physiological and psychophysical results on contour salience. We demonstrate that horizontal connections between realistically-modeled multi-compartment pyramidal cells and interneurons can generate robust context-dependent synchronization. Closed contours induce better synchronization in the network than open contours, and closure thus increases perceptual salience, as observed psychophysically by Kovács and Julesz. This result is a general topological property of synchronization. The model supports a temporal synchronization solution to the binding problem, in that changes in synchronization are directly linked to changes in visual perception. INTRODUCTION Recent theoretical and experimental studies sugg..

It looks easy! Heuristics for combinatorial optimization problems.

Human performance on instances of computationally intractable optimization problems, such as the travelling salesperson problem (TSP), can be excellent. We have proposed a boundary-following heuristic to account for this finding. We report three experiments with TSPs where the capacity to employ this heuristic was varied. In Experiment 1, participants free to use the heuristic produced solutions significantly closer to optimal than did those prevented from doing so. Experiments 2 and 3 together replicated this finding in larger problems and demonstrated that a potential confound had no effect. In all three experiments, performance was closely matched by a boundary-following model. The results implicate global rather than purely local processes. Humans may have access to simple, perceptually based, heuristics that are suited to some combinatorial optimization tasks