Biology subjects

Brainard, D. H.

Publications and source records attributed to Brainard, D. H..

8 recordsLinked to original sources

bioRxiv2018

A computational observer model of spatial contrast sensitivity: Effects of wavefront-based optics, cone mosaic structure, and inference engine

We present a computational observer model of the human spatial contrast sensitivity (CSF) function based on the Image Systems EngineeringTools for Biology (ISETBio) simulation framework. We demonstrate that ISETBio-derived CSFs agree well with CSFs derived using traditional ideal observer approaches, when the mosaic, optics, and inference engine are matched. Further simulations extend earlier work by considering more realistic cone mosaics, more recent measurements of human physiological optics, and the effect of varying the inference engine used to link visual representations to psy-chohysical performance. Relative to earlier calculations, our simulations show that the spatial structure of realistic cone mosaics reduces upper bounds on performance at low spatial frequencies, whereas realistic optics derived from modern wavefront measurements lead to increased upper bounds high spatial frequencies. Finally, we demonstrate that the type of inference engine used has a substantial effect on the absolute level of predicted performance. Indeed, the performance gap between an ideal observer with exact knowledge of the relevant signals and human observers is greatly reduced when the inference engine has to learn aspects of the visual task. ISETBio-derived estimates of stimulus representations at different stages along the visual pathway provide a powerful tool for computing the limits of human performance.

neuroscience↗

bioRxiv2018

Pulses of melanopsin-directed contrast produce highly reliable pupil responses that are insensitive to a change in background radiance

PurposeTo measure the pupil response to pulses of melanopsin-directed contrast, and compare this response to those evoked by cone-directed contrast and spectrally-narrowband stimuli.\n\nMethods3-second unipolar pulses were used to elicit pupil responses in human subjects across 3 sessions. Thirty subjects were studied in Session 1, and most returned for Sessions 2 and 3. The stimuli of primary interest were \"silent substitution\" cone- and melanopsin-directed modulations. Red and blue narrowband pulses delivered using the post-illumination pupil response (PIPR) paradigm were also studied. Sessions 1 and 2 were identical, while Session 3 involved modulations around higher radiance backgrounds. The pupil responses were fit by a model whose parameters described response amplitude and temporal shape.\n\nResultsGroup average pupil responses for all stimuli overlapped extensively across Sessions 1 and 2, indicating high reproducibility. Model fits indicate that the response to melanopsin-directed contrast is prolonged relative to that elicited by cone-directed contrast. The group average cone- and melanopsin-directed pupil responses from Session 3 were highly similar to those from Sessions 1 and 2, suggesting that these responses are insensitive to background radiance over the range studied. The increase in radiance enhanced persistent pupil constriction to blue light.\n\nConclusionsThe group average pupil response to stimuli designed through silent substitution provides a reliable probe of the function of a melanopsin-mediated system in humans. As disruption of the melanopsin system may relate to clinical pathology, the reproducibility of response suggests that silent substitution pupillometry can test if melanopsin signals differ between clinical groups.

neuroscience↗

bioRxiv2018

Computational Luminance Constancy from Naturalistic Images

The human visual system supports stable percepts of object color even though the light that reflects from object surfaces varies significantly with the scene illumination. To understand the computations that support stable color perception, we study how estimating a target objects luminous reflectance factor (LRF; a measure of the light reflected from the object under a standard illuminant) depends on variation in key properties of naturalistic scenes. Specifically, we study how variation in target object reflectance, illumination spectra, and the reflectance of back-ground objects in a scene impact estimation of a target objects LRF. To do this, we applied supervised statistical learning methods to the simulated excitations of human cone photoreceptors, obtained from labeled naturalistic images. The naturalistic images were rendered with computer graphics. The illumination spectra of the light sources and the reflectance spectra of the surfaces in the scene were generated using statistical models of natural spectral variation. Optimally decoding target object LRF from the responses of a small learned set of task-specific linear receptive fields that operate on a contrast representation of the cone excitations yields estimates that are within 13% of the correct LRF. Our work provides a framework for evaluating how different sources of scene variability limit performance on luminance constancy.

neuroscience↗

bioRxiv2018

Neuronal population mechanisms of lightness perception

The way that humans and animals perceive the lightness of an object depends on its physical luminance as well as its surrounding context. While neuronal responses throughout the visual pathway are modulated by context, the relationship between neuronal responses and lightness perception is poorly understood. We searched for a neuronal mechanism of lightness by recording responses of neuronal populations in monkey primary visual cortex (V1) and area V4 to stimuli that produce a lightness illusion in humans, in which the lightness of a disk depends on the context in which it is embedded. We found that the way individual units encode the luminance (or equivalently for our stimuli, contrast) of the disk and its context is extremely heterogeneous. This motivated us to ask whether the population representation in either V1 or V4 satisfies three criteria: 1) disk luminance is represented with high fidelity, 2) the context surrounding the disk is also represented, and 3) the representations of disk luminance and context interact to create a representation of lightness that depends on these factors in a manner consistent with human psychophysical judgments of disk lightness. We found that populations of units in both V1 and V4 fulfill the first two criteria, but that we cannot conclude that the two types of information in either area interact in a manner that clearly predicts human psychophysical measurements: the interpretation of our population measurements depends on how subsequent areas read out lightness from the population responses.\n\nNew & NoteworthyA core question in visual neuroscience is how the brain extracts stable representations of object properties from the retinal image. We searched for a neuronal mechanism of lightness perception by determining whether the responses of neuronal populations in primary visual cortex and area V4 could account for a lightness illusion measured using human psychophysics. Our results suggest that comparing psychophysics with population recordings will yield insight into neuronal mechanisms underlying a variety of perceptual phenomena.

neuroscience↗

bioRxiv2018

The perception of color and material in natural tasks

Perceived object color and material properties help us to select and interact with objects. Because there is no simple mapping between the pattern of an objects image on the retina and its physical reflectance, our perception of color and material are made more useful through sophisticated visual computations. A long-standing goal in vision science is to describe how these computations work, particularly as they act to stabilize perceived color and material against variation in scene factors extrinsic to object surface properties, such as the illumination. If we take seriously the notion that perceived color and material are useful because they help guide behavior in natural tasks, then we need experimental that measure and models that describe how they are used in such tasks. To this end, we have developed selection-based methods and accompanying perceptual models for studying perceived object color and material. This focused review highlights key aspects of our work. It includes a discussion of future directions and challenges, as well as an outline of a computational observer model that incorporates early, known, stages of visual processing and that clarifies how early vision shapes selection performance.

neuroscience↗

bioRxiv2018

Spatial summation in the human fovea: the effect of optical aberrations and fixational eye movements

Psychophysical inferences about the neural mechanisms supporting spatial vision can be undermined by uncertainties introduced by optical aberrations and fixational eye movements, particularly in fovea where the neuronal grain of the visual system is fine. We examined the effect of these pre-neural factors on photopic spatial summation in the human fovea using a custom adaptive optics scanning light ophthalmoscope that provided control over optical aberrations and retinal stimulus motion. Consistent with previous results, Riccos area of complete summation encompassed multiple photoreceptors when measured with ordinary amounts of ocular aberrations and retinal stimulus motion. When both factors were minimized experimentally, summation areas were essentially unchanged, suggesting that foveal spatial summation is limited by post-receptoral neural pooling. We compared our behavioral data to predictions generated with a physiologically-inspired front-end model of the visual system, and were able to capture the shape of the summation curves obtained with and without pre-retinal factors using a single post-receptoral summing filter of fixed spatial extent. Given our data and modeling, neurons in the magnocellular visual pathway, such as parasol ganglion cells, provide a candidate neural correlate of Riccos area in the central fovea.

neuroscience↗

bioRxiv2017

Simulation of visual perception and learning with a retinal prosthesis

The nature of artificial vision with a retinal prosthesis, and the degree to which the brain can adapt to the unnatural input from such a device, are poorly understood. Therefore, the development of current and future devices may be aided by theory and simulations that help to infer and understand what prosthesis patients see. A biologically-informed, extensible computational framework is presented here to predict visual perception and the potential effect of learning with a subretinal prosthesis. The framework relies on optimal linear reconstruction of the stimulus from retinal responses to infer the visual information available to the patient. A simulation of the physiological optics of the eye and light responses of the major retinal neurons was used to calculate the optimal linear transformation for reconstructing natural images from retinal activity. The result was then used to reconstruct the visual stimulus during the artificial activation expected from a subretinal prosthesis in a degenerated retina, as a proxy for inferred visual perception. Several simple observations reveal the potential utility of such a simulation framework. The inferred perception obtained with prosthesis activation was substantially degraded compared to the inferred perception obtained with normal retinal responses, as expected given the limited resolution and lack of cell type specificity of the prosthesis. Consistent with clinical findings and the importance of cell type specificity, reconstruction using only ON cells, and not OFF cells, was substantially more accurate. Finally, when reconstruction was re-optimized for prosthesis stimulation, simulating the greatest potential for learning by the patient, the accuracy of inferred perception was much closer to that of healthy vision. The reconstruction approach thus provides a more complete method for exploring the potential for treating blindness with retinal prostheses than has been available previously. It may also be useful for interpreting patient data in clinical trials, and for improving prosthesis design.

neuroscience↗

bioRxiv2017

The Human Visual Cortex Response To Melanopsin-Directed Stimulation Is Accompanied By A Distinct Perceptual Experience

The photopigment melanopsin supports reflexive visual functions in people, such as pupil constriction and circadian photoentrainment. What contribution melanopsin makes to conscious visual perception is less studied. We devised a stimulus that targeted melanopsin separately from the cones using pulsed (3 s) spectral modulations around a photopic background. Pupil-lometry confirmed that the melanopsin stimulus drives a retinal mechanism distinct from luminance. In each of four subjects, a functional MRI response in area V1 was found. This response scaled with melanopic contrast and was not easily explained by imprecision in the silencing of the cones. Twenty additional subjects then observed melanopsin pulses and provided a structured rating of the perceptual experience. Melanopsin stimulation was described as an unpleasant, blurry, minimal brightening that quickly faded. We conclude that isolated stimulation of melanopsin is likely associated with a response within the cortical visual pathway and with an evoked conscious percept.

neuroscience↗