Summary Humans and other species continually perform microscopic eye movements, even when attending to a single point [1-3]. These movements, which include microscopic drifts and microsaccades, are under control of the oculomotor system [2, 4, 5], elicit strong responses throughout the visual system [6-11], and have been thought to serve important functions [12-16]. The influence of these fixational eye movements on the acquisition and neural processing of visual information remains unknown. Here, we show that during viewing of natural scenes, microscopic eye movements carry out a crucial information-processing step: they remove predictable correlations in natural scenes by equalizing the spatial power of the retinal image within the frequency range of ganglion cells' peak sensitivity. This transformation, which had been attributed to center-surround receptive field organization [17-19], occurs prior to any neural processing, and reveals a form of matching between the statistics of natural images and those of normal eye movements. We further show that the combined effect of microscopic eye movements and retinal receptive field organization is to convert spatial luminance discontinuities into synchronous firing events, thus beginning the process of edge extraction. In sum, our results show that microscopic eye movements are fundamental to two goals of early visual processing —redundancy reduction [20, 21] and feature extraction— and, thus, that neural representations are intrinsically sensory-motor from the very first processing stages.

During visual fixation, microscopic eye movements shift the image on the retina over a large number of photoreceptors. Although these movements have been investigated for almost a century, the amount of retinal image motion they create remains unclear. Currently available estimates rely on assumptions about the probability distributions of eye movements that have never been tested. Furthermore, these estimates were based on data collected with only a few, highly experienced and motivated observers and may not be representative of the instability of naive and inexperienced subjects in experiments that require steady fixation. In this study, we used a high-resolution eye-tracker to estimate the probability distributions of gaze position in a relatively large group of human observers, most of whom were untrained, while they were asked to maintain fixation at the center of a uniform field in the presence/absence of a fixation marker. In all subjects, the probability distribution of gaze position deviated from normality, the underlying assumption of most previous studies. The resulting fixational dispersion of gaze was much larger than previously reported and varied greatly across individuals. Unexpectedly, the precision by which different observers maintained fixation on the marker was best predicted by the properties of ocular drift rather than those of microsaccades. Our results show that, during fixation, the eyes move by larger amounts and at higher speeds than commonly assumed and highlight the importance of ocular drift in maintaining accurate fixation.

Sensory neurons adapt to changes in the natural statistics of their environments through processes such as gain control and firing threshold adjustment. It has been argued that neurons early in sensory pathways adapt according to information-theoretic criteria, perhaps maximising their coding efficiency or information rate. Here, we draw a distinction between how a neuron's preferred operating point is determined and how its preferred operating point is maintained through adaptation. We propose that a neuron's preferred operating point can be characterised by the probability density function (PDF) of its output spike rate, and that adaptation maintains an invariant output PDF, regardless of how this output PDF is initially set. Considering a sigmoidal transfer function for simplicity, we derive simple adaptation rules for a neuron with one sensory input that permit adaptation to the lower-order statistics of the input, independent of how the preferred operating point of the neuron is set. Thus, if the preferred operating point is, in fact, set according to information-theoretic criteria, then these rules nonetheless maintain a neuron at that point. Our approach generalises from the unimodal case to the multimodal case, for a neuron with inputs from distinct sensory channels, and we briefly consider this case too.

While looking at a point in the scene, humans continually perform smooth eye movements to compensate for involuntary head rotations. Since the optical nodal points of the eyes do not lie on the head rotation axes, this behavior yields useful 3-D information in the form of visual parallax. Here, we describe the replication of this behavior in a humanoid robot. We have developed a method for egocentric distance estimation based on the parallax that emerges during compensatory head/eye movements. This method was tested in a robotic platform equipped with an anthropomorphic neck and two binocular pan-tilt units specifically designed to reproduce the visual input signals experienced by humans. We show that this approach yields accurate and robust estimation of egocentric distance within the space nearby the agent. These results provide a further demonstration of how behavior facilitates the solution of complex perceptual problems.Index Terms-Active perception, behavior-based systems, biologically inspired robots, humanoid robots, 3-D vision.

Adaptation is a ubiquitous property of sensory neurons. Multisensory neurons, receiving convergent input from different sensory modalities, also likely exhibit adaptation. The responses of multisensory superior colliculus neurons have been extensively studied, but the impact of adaptation on these responses has not been examined. Multisensory neurons in the superior colliculus exhibit cross-modal enhancement, an often non-linear and non-additive increase in response when a stimulus in one modality is paired with a stimulus in a different modality. We examine the possible impact of adaptation on cross-modal enhancement within the framework of a simple model of adaptation for a neuron employing a saturating, logistic response function. We consider how adaptation to an input's mean and standard deviation affects cross-modal enhancement, and also how the statistical correlations between two different modalities influence cross-modal enhancement. We determine the optimal bimodal stimuli to present a bimodal neuron that evoke the largest changes in cross-modal enhancement under adaptation to input statistics. The model requires separate gains for each modality, unless the statistics specific to each modality have been standardised by prior adaptation in earlier, unisensory neurons. The model also predicts that increasing the correlation coefficient between two modalities reduces a multisensory neuron's overall gain.