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Wilson S Geisler

Publications and source records attributed to Wilson S Geisler.

13 recordsLinked to original sources
PubMed2006

Optimal decoding of correlated neural population responses in the primate visual cortex.

Even the simplest environmental stimuli elicit responses in large populations of neurons in early sensory cortical areas. How these distributed responses are read out by subsequent processing stages to mediate behavior remains unknown. Here we used voltage-sensitive dye imaging to measure directly population responses in the primary visual cortex (V1) of monkeys performing a demanding visual detection task. We then evaluated the ability of different decoding rules to detect the target from the measured neural responses. We found that small visual targets elicit widespread responses in V1, and that response variability at distant sites is highly correlated. These correlations render most previously proposed decoding rules inefficient relative to one that uses spatially antagonistic center-surround summation. This optimal decoder consistently outperformed the monkey in the detection task, demonstrating the sensitivity of our techniques. Overall, our results suggest an unexpected role for inhibitory mechanisms in efficient decoding of neural population responses.

Algorithms↗
PubMed2006

Visual search: the role of peripheral information measured using gaze-contingent displays.

Two of the factors limiting progress in understanding the mechanisms of visual search are the difficulty of controlling and manipulating the retinal stimulus when the eyes are free to move and the lack of an ideal observer theory for fixation selection during search. Recently, we developed a method to precisely control retinal stimulation with gaze-contingent displays (J. S. Perry & W. S. Geisler, 2002), and we derived a theory of optimal eye movements in visual search (J. Najemnik & W. S. Geisler, 2005). Here, we report a parametric study of visual search for sine-wave targets added to spatial noise backgrounds that have spectral characteristics similar to natural images (the amplitude spectrum of the noise falls inversely with spatial frequency). Search time, search accuracy, and eye fixations were measured as a function of target spatial frequency, 1/f noise contrast, and the resolution falloff of the display from the point of fixation. The results are systematic and similar for the two observers. We find that many aspects of search performance and eye movement pattern are similar to those of an ideal searcher that has the same falloff in resolution with retinal eccentricity as the human visual system.

Artifacts↗
PubMed2006

Local luminance and contrast in natural images.

Within natural images there is substantial spatial variation in both local contrast and local luminance. Understanding the statistics of these variations is important for understanding the dynamics of receptive field stimulation that occur under natural viewing conditions and for understanding the requirements for effective luminance and contrast gain control. Local luminance and contrast were measured in a large set of calibrated 12-bit gray-scale natural images, for a number of analysis patch sizes. For each image and patch size we measured the range of contrast, the range of luminance, the correlation in contrast and luminance as a function of the distance between patches, and the correlation between contrast and luminance within patches. The same analyses were also performed on hand segmented regions containing only "sky", "ground", "foliage", or "backlit foliage". Within the typical image, the 95% range (2.5-97.5 percentile) for both local luminance and local contrast is somewhat greater than a factor of 10. The correlation in contrast and the correlation in luminance diminish rapidly with distance, and the typical correlation between luminance and contrast within patches is small (e.g., -0.2 compared to -0.8 for 1/f noise). We show that eye movements are frequently large enough that there will be little correlation in the contrast or luminance on a receptive field from one fixation to the next, and thus rapid contrast and luminance gain control are essential. The low correlation between local luminance and contrast implies that efficient contrast gain control mechanisms can operate largely independently of luminance gain control mechanisms.

Contrast Sensitivity↗
PubMed2005

Independence of luminance and contrast in natural scenes and in the early visual system.

The early visual system is endowed with adaptive mechanisms that rapidly adjust gain and integration time based on the local luminance (mean intensity) and contrast (standard deviation of intensity relative to the mean). Here we show that these mechanisms are matched to the statistics of the environment. First, we measured the joint distribution of luminance and contrast in patches selected from natural images and found that luminance and contrast were statistically independent of each other. This independence did not hold for artificial images with matched spectral characteristics. Second, we characterized the effects of the adaptive mechanisms in lateral geniculate nucleus (LGN), the direct recipient of retinal outputs. We found that luminance gain control had the same effect at all contrasts and that contrast gain control had the same effect at all mean luminances. Thus, the adaptive mechanisms for luminance and contrast operate independently, reflecting the very independence encountered in natural images.

Action Potentials↗
PubMed2005

Optimal eye movement strategies in visual search.

To perform visual search, humans, like many mammals, encode a large field of view with retinas having variable spatial resolution, and then use high-speed eye movements to direct the highest-resolution region, the fovea, towards potential target locations. Good search performance is essential for survival, and hence mammals may have evolved efficient strategies for selecting fixation locations. Here we address two questions: what are the optimal eye movement strategies for a foveated visual system faced with the problem of finding a target in a cluttered environment, and do humans employ optimal eye movement strategies during a search? We derive the ideal bayesian observer for search tasks in which a target is embedded at an unknown location within a random background that has the spectral characteristics of natural scenes. Our ideal searcher uses precise knowledge about the statistics of the scenes in which the target is embedded, and about its own visual system, to make eye movements that gain the most information about target location. We find that humans achieve nearly optimal search performance, even though humans integrate information poorly across fixations. Analysis of the ideal searcher reveals that there is little benefit from perfect integration across fixations--much more important is efficient processing of information on each fixation. Apparently, evolution has exploited this fact to achieve efficient eye movement strategies with minimal neural resources devoted to memory.

Brain↗
PubMed2005

Grouping local orientation and direction signals to extract spatial contours: empirical tests of "association field" models of contour integration.

Over the last decade or so a great deal of psychophysical research has attempted to delineate the principles by which local orientations and motions are combined across space to facilitate the detection of simple spatial contours. This has led to the development of "association field" models of contour detection which suggest that the strength of linking between neighbouring elements in an image, is determined by the degree to which they aligned along smooth (first-order) curves. To test this assumption we used a path detection paradigm to compare the ability of observers to identify the presence of contours defined by either spatial orientation, motion direction or by specific combinations of both types of visual attribute. The relative alignment of the local orientations and/or directions with respect to the axis of the depicted contour was systematically varied. For orientation-defined contours detection was best when the elements were aligned along (parallel with) the contour axis, approached chance levels for obliquely oriented elements and then improved for elements that were orthogonal to the contour axis (i.e., performance was a U-shaped function of degree of orientation misalignment). This pattern of results was found for both straight and curved contours and is not readily explicable in terms of current association field theories. For motion-defined contours, however, performance simply deteriorated as the relative directions of the constituent path elements were progressively misaligned with respect to the contour. Thus the rules by which local orientations are linked to define spatial contours are qualitatively different from those used for linking local directions and each may be mediated by distinct visual mechanisms. When both orientation and motion cues were simultaneously available, contour detection performance was generally enhanced, in a manner that is consistent with probability summation. We suggest that association field models of orientation linking may need to be extended in light of the present findings.

Cues↗
PubMed2005

Contrast statistics for foveated visual systems: fixation selection by minimizing contrast entropy.

The human visual system combines a wide field of view with a high-resolution fovea and uses eye, head, and body movements to direct the fovea to potentially relevant locations in the visual scene. This strategy is sensible for a visual system with limited neural resources. However, for this strategy to be effective, the visual system needs sophisticated central mechanisms that efficiently exploit the varying spatial resolution of the retina. To gain insight into some of the design requirements of these central mechanisms, we have analyzed the effects of variable spatial resolution on local contrast in 300 calibrated natural images. Specifically, for each retinal eccentricity (which produces a certain effective level of blur), and for each value of local contrast observed at that eccentricity, we measured the probability distribution of the local contrast in the unblurred image. These conditional probability distributions can be regarded as posterior probability distributions for the "true" unblurred contrast, given an observed contrast at a given eccentricity. We find that these conditional probability distributions are adequately described by a few simple formulas. To explore how these statistics might be exploited by central perceptual mechanisms, we consider the task of selecting successive fixation points, where the goal on each fixation is to maximize total contrast information gained about the image (i.e., minimize total contrast uncertainty). We derive an entropy minimization algorithm and find that it performs optimally at reducing total contrast uncertainty and that it also works well at reducing the mean squared error between the original image and the image reconstructed from the multiple fixations. Our results show that measurements of local contrast alone could efficiently drive the scan paths of the eye when the goal is to gain as much information about the spatial structure of a scene as possible.

Algorithms↗
PubMed2004

Visual cortex neurons of monkeys and cats: temporal dynamics of the spatial frequency response function.

We measured the responses of striate cortex neurons as a function of spatial frequency on a fine time scale, over the course of an interval that is comparable to the duration of a single fixation (200 ms). Stationary gratings were flashed on for 200 ms and then off for 300 ms; the responses were analyzed at sequential 1-ms intervals. We found that 1) the preferred spatial frequency shifts through time from low frequencies to high frequencies, 2) the latency of the response increases as a function of spatial frequency, and 3) the poststimulus time histograms (PSTHs) are relatively shape-invariant across spatial frequency. The dynamic shifts in preferred spatial frequency appear to be a simple consequence of the latency shifts and the transient nature of the PSTH. The effects of these dynamic shifts on the coding of spatial frequency information are examined within the context of several different temporal integration strategies, and pattern-detection performance is determined as a function of the interval of integration, following response onset. The findings are considered within the context of related investigations as well as a number of functional issues: motion selectivity in depth, "coarse-to-fine" processing, direction selectivity, latency as a code for stimulus attributes, and behavioral response latency. Finally, we demonstrate that the results are qualitatively consistent with a simple feedforward model, similar to the one originally proposed in 1962 by Hubel and Wiesel, that incorporates measured differences in the response latencies and the receptive field sizes of different lateral geniculate nucleus inputs.

Action Potentials↗
PubMed2004

Contour grouping: closure effects are explained by good continuation and proximity.

Previous experimental studies have provided evidence that closed contours are easier to detect than open contours in random-element displays, and previous theoretical studies have shown that these effects might be explained by an active neural mechanism (e.g., a "reverberating neural circuit") sensitive to closure. To test this hypothesis, detection thresholds were measured in five experiments designed to control for the effects of uncertainty, eccentricity, and element density. In four of the experiments, we found that closed contours were no easier to detect than open contours, and in the remaining experiment the effects were consistent with the predictions of probability summation. Thus, we could find no evidence for an active neural mechanism that enhances detectability of closed contours more than open contours, although some form of closure mechanism may play a significant role in image interpretation.

Form Perception↗
PubMed2002

Bayesian natural selection and the evolution of perceptual systems.

In recent years, there has been much interest in characterizing statistical properties of natural stimuli in order to better understand the design of perceptual systems. A fruitful approach has been to compare the processing of natural stimuli in real perceptual systems with that of ideal observers derived within the framework of Bayesian statistical decision theory. While this form of optimization theory has provided a deeper understanding of the information contained in natural stimuli as well as of the computational principles employed in perceptual systems, it does not directly consider the process of natural selection, which is ultimately responsible for design. Here we propose a formal framework for analysing how the statistics of natural stimuli and the process of natural selection interact to determine the design of perceptual systems. The framework consists of two complementary components. The first is a maximum fitness ideal observer, a standard Bayesian ideal observer with a utility function appropriate for natural selection. The second component is a formal version of natural selection based upon Bayesian statistical decision theory. Maximum fitness ideal observers and Bayesian natural selection are demonstrated in several examples. We suggest that the Bayesian approach is appropriate not only for the study of perceptual systems but also for the study of many other systems in biology.

Alleles↗
PubMed2002

Visual cortex neurons of monkeys and cats: temporal dynamics of the contrast response function.

Cortical neurons display two fundamental nonlinear response characteristics: contrast-set gain control (also termed contrast normalization) and response expansion (also termed half-squaring). These nonlinearities could play an important role in forming and maintaining stimulus selectivity during natural viewing, but only if they operate well within the time frame of a single fixation. To analyze the temporal dynamics of these nonlinearities, we measured the responses of individual neurons, recorded from the primary visual cortex of monkeys and cats, as a function of the contrast of transient stationary gratings that were presented for a brief interval (200 ms). We then examined 1) the temporal response profile (i.e., the post stimulus time histogram) as a function of contrast and 2) the contrast response function throughout the course of the temporal response. We found that the shape and complexity of the temporal response profile varies considerably from cell to cell. However, within a given cell, the shape remains relatively invariant as a function of contrast and appears to be simply scaled and shifted. Stated quantitatively, approximately 95% of the variation in the temporal responses as a function of contrast could be accounted for by scaling and shifting the average poststimulus time histogram. Equivalently, we found that the overall shape of the contrast response function (measured every 2 ms) remains relatively invariant from the onset through the entire temporal response. Further, the contrast-set gain control and the response expansion are fully expressed within the first 10 ms after the onset of the response. Stated quantitatively, the same, scaled Naka-Rushton equation (with the same half-saturation contrast and expansive response exponent) provides a good fit to the contrast response function from the first 10 ms through the last 10 ms of the temporal response. Based upon these measurements, it appears as though the two nonlinear properties, contrast-set gain control and response expansion, are present in full strength, virtually instantaneously, at the onset of the response. This observation suggests that response expansion and contrast-set gain control can influence the performance of visual cortex neurons very early in a single fixation, based on the contrast within that fixation. In the DISCUSSION, we consider the implications of the results within the context of 1) slower types of contrast gain control, 2) discrimination performance, 3) drifting steady-state measurements, 4) functional models that incorporate response expansion and contrast normalization, and 5) structural models of the biochemical and biophysical neural mechanisms.

Animals↗
PubMed2000

Perceptual organization of two-dimensional patterns.

The perceptual organization of image patterns is considered from 2 standpoints. First, a theoretical framework is presented from which computational models of perceptual organization can be constructed and tested. Second, a specific computational model for perceptual organization of line images is described. In this model, input images are first processed by a dense array of neurons that have properties consistent with recent analyses of single-neuron responses in primary visual cortex. Then, complex image structure is discovered by interleaved pattern-matching and grouping processes constrained by a generalized uniqueness principle. A series of 3-pattern grouping experiments was performed to test a restricted version of the model and to estimate critical parameters. Using the estimated parameters, an extended version of the model was tested by generating predictions for a series of "textbook" perceptual organization demonstrations.

Humans↗