The classical model of visual processing in cortex is a hierarchy of increasingly sophisticated representations, extending in a natural way the model of simple to complex cells of Hubel and Wiesel. Somewhat surprisingly, little quantitative modeling has been done in the last 15 years to explore the biological feasibility of this class of models to explain higher level visual processing, such as object recognition. We describe a new hierarchical model that accounts well for this complex visual task, is consistent with several recent physiological experiments in inferotemporal cortex and makes testable predictions. The model is based on a novel MAX-like operation on the inputs to certain cortical neurons which may have a general role in cortical function.

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The ventral pathway in visual cortex is responsible for the perception of shape. Area V4 is an important intermediate stage in this pathway, and provides the major input to the final stages in inferotemporal cortex. The role of V4 in processing shape information is not yet clear. We studied V4 responses to contour features (angles and curves), which many theorists have proposed as intermediate shape primitives. We used a large parametric set of contour features to test the responses of 152 V4 cells in two awake macaque monkeys. Most cells responded better to contour features than to edges or bars, and about one-third exhibited systematic tuning for contour features. In particular, many cells were selective for contour feature orientation, responding to angles and curves pointing in a particular direction. There was a strong bias toward convex (as opposed to concave) features, implying a neural basis for the well-known perceptual dominance of convexity. Our results suggest that V4 processes information about contour features as a step toward complex shape recognition.

Recent neurophysiological studies have shown that primary visual cortex, or V1, does more than passively process image features using the feedforward filters suggested by Hubel and Wiesel. It also uses horizontal interactions to group features preattentively into object representations, and feedback interactions to selectively attend to these groupings. All neocortical areas, including V1, are organized into layered circuits. We present a neural model showing how the layered circuits in areas V1 and V2 enable feedforward, horizontal, and feedback interactions to complete perceptual groupings over positions that do not receive contrastive visual inputs, even while attention can only modulate or prime positions that do not receive such inputs. Recent neurophysiological data about how grouping and attention occur and interact in V1 are simulated and explained, and testable predictions are made. These simulations show how attention can selectively propagate along an object grouping and protect it from competitive masking, and how contextual stimuli can enhance or suppress groupings in a contrast-sensitive manner.

Address correspondence to T.P. The usual description of visual processing in cortex is an extension of the simple to complex hi-erarchy postulated by Hubel and Wiesel — a feedforward sequence of more and more complex and invariant features. The capability of this class of models to perform higher level visual processing such as viewpoint-invariant object recognition in cluttered scenes has been questioned in recent years by several researchers, who in turn proposed an alternative class of models based on the synchro-nization of large assemblies of cells, within and across cortical areas. The main implicit argument for this novel and controversial view was the assumption that hierarchical models cannot deal with the computational requirements of high level vision and suffer from the so-called “binding problem”. We review the present situation and discuss theoretical and experimental evidence showing that the perceived weaknesses of hierarchical models are not true. In particular, we show that recognition of multiple objects in cluttered scenes, arguably among the most difficult tasks in vision, can be done in a hierarchical feedforward model. 1

Attentional selection of an object for recognition is often modeled using all-or-nothing switching of neuronal connection pathways from the attended region of the retinal input to the recognition units.

Numerous theories of neural processing, often motivated by experimental observations, have explored the computational properties of neural codes based on the precise or relative occurrence of spikes in a spike train. Spiking neuron models and theories however, as well as their experimental counterparts, have generally been limited to the simulation or observation of isolated neurons, isolated spike trains, or reduced neural populations. Such theories would therefore seem inappropriate to capture the properties of a neural code relying on temporal spike patterns distributed across large neuronal populations. Here we report a range of computer simulations and theoretical considerations that were designed to explore the possibilities of such a code and its relevance for visual processing. In a single, unified framework where the relation between stimulus saliency and spike asynchrony plays the central role, we describe how the ventral stream of the visual system could process natural input scenes and extract meaningful information, both rapidly and reliably. The first wave of spikes generated in the retina in response to a visual stimulation carries information explicitly in its spatio-temporal structure. This spike wave, propagating through a hierarchy of visual areas, is regenerated at each processing stage, where its temporal structure can be modified by (i) the selectivity of the cortical neurons, (ii) lateral interactions and (iii) top-down attentional influences from higher order cortical areas. The concept of temporal asynchrony within a wave of single spikes allows a unique theoretical framework to address the fundamental and complementary notions of neural information coding and representation, visual saliency and attention. 1.

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Recent neurophysiological experiments have led to a promising “biased competition hypothesis” of the neural basis of attention. According to this hypothesis, attention appears as a sometimes non-linear property that results from a top-down biassing effect that influences the competitive and cooperative interactions that work both within cortical areas and between cortical areas. In this paper we describe a detailed dynamical analysis of the synaptic and neuronal spiking mechanisms underlying biased competition. We perform a detailed analysis of the dynamical capabilities of the system by exploring the stationary attractors in the parameter space via a mean field reduction consistent with the underlying synaptic and spiking dynamics. The nonstationary dynamical behaviour, as measured in neuronal recording experiments, is studied via an integrate-and-fire model with realistic dynamics. This elucidates the role of cooperation and competition in the dynamics of biased competition; and shows why feedback connections between cortical areas need optimally to be weaker by a factor of approximately 2.5 than the feedforward connections in an attentional network. We modelled the interaction between top-down attention and bottom up stimulus contrast effects

Selective visual attention to objects and locations depends both on deliberate behavioral goals that regulate even early visual representations (goal-directed influences) and on autonomous neural responses to sensory input (stimulus-driven influences). In this chapter, I argue that deliberate goal-directed attentional strategies are always constrained by involuntary, ”hard-wired computations, and that an appropriate research strategy is to delineate the nature of the interactions imposed by these constraints. To illustrate the inter-action between goal-directed and stimulus-driven attentional control, four domains of visual selection are reviewed. First, selection by location is both spatially and temporally limited, reflecting in part early visual representations of the scene. Second, selection by feature is an available attentional strategy, but it appears to be mediated by location, and feature salience alone does not govern the deployment of attention. Third, early visual seg-mentation processes that parse a scene into perceptual object representations enable object-based selection, but they also enforce selection of entire objects, and not just isolated features. And fourth, the appearance of a new perceptual object captures attention in a stimulus-driven fashion, but even this is subject to some top-down attentional control.