Biology subjects

Geisler, W. S.

Publications and source records attributed to Geisler, W. S..

8 recordsLinked to original sources

bioRxiv2026

Wide dynamic range of contrast-encoding in neural responses in macaque V1

The dynamic range of contrast-encoding in the early visual system has been investigated at both single-neuron and population levels in animals using oriented stimuli such as gratings and Gabor patches. However, contrast-encoding of unoriented stimuli such as Gaussians has been less explored, even though such stimuli can evoke a large population response. In studies that employ Gaussians, contrast response functions (CRFs) of neurons and neural populations in primary visual cortex are typically characterized across a limited range of Weber contrasts up to 100%, which may not adequately reflect the statistics of contrast in natural scenes. Indeed, our analysis shows that locations with contrasts far exceeding 100% Weber contrast are ubiquitous in natural scenes. Thus, our current understanding of contrast encoding remains incomplete in the context of natural environments. Here, we measured spiking activities of individual neurons using electrophysiology and population responses using calcium imaging in V1 of fixating macaques while Gaussian stimuli were presented over a wide range of Weber contrasts, up to 900%. Both the average spiking response and the average population response continued to increase robustly above 100% Weber contrast. Only a small minority of neurons saturate below 100% Weber contrast. These results demonstrate that the dynamic range of contrast-encoding in V1 is broader than previously assumed and aligns more closely with the statistics of contrast in natural scenes. Significance StatementThe dynamic range of contrast-encoding in V1 has typically been characterized using only a limited range of contrasts, often excluding the high contrasts commonly found in natural scenes. We measured contrast response functions of individual neurons and neural populations in V1 across a much wider range of contrasts. We found that the responses of the majority of neurons, as well as the overall population, increased robustly up to extremely high contrasts. These findings suggest that the dynamic range of contrast encoding in V1 extends well beyond the commonly tested range.

neuroscience↗

bioRxiv2026

Proximity as a Ground-Truth Proxy for Training Texture Discrimination and Segmentation

Perceptual systems in humans and many other animals are able to segment scenes into regions that are likely to be physically meaningful. This ability depends on having low-level mechanisms that can accurately categorize whether local image patches are samples from the same or different kinds of texture. We find that using spatial proximity as a proxy for same-different ground truth makes it possible to train accurate decision variables and bounds directly from arbitrary natural images with no feedback. We also find that performance can be further improved by using proximity as a ground truth for adjusting the final decision variables and bounds for the current image/scene. These surprising findings result from the simple fact that under a wide range of conditions proximity discrimination (near vs. far) and texture discrimination (same vs. different) have mathematically identical decision bounds if the same image features are used for both tasks. We used the decision variables and bounds trained on natural images as the initial steps in a hierarchical Bayesian observer (HBO) model of texture discrimination [9]. Given the relative simplicity of this HBO model, it did an excellent job of segmenting images having randomly shaped regions containing arbitrary natural textures. We suggest that the proximity proxy is something that natural selection could discover and exploit for any same-different task where the task-relevant stimulus features also vary systematically with distance in space and/or time. For example, natural selection could have created developmental learning/plasticity mechanisms that exploit the proximity proxy.

animal behavior and cognition↗

bioRxiv2026

Principles of Local and Global Grouping that Underlie Segmentation of Natural Texture Images

The human visual system segments images using both high-level recognition mechanisms and low-level mechanisms that are largely independent of specific prior experience. The low-level mechanisms are essential for initiating recognition processes, and for learning to recognize new materials, objects, and contexts. Here we describe a hierarchical Bayesian observer (HBO) model of texture segmentation that is biologically plausible, takes into account the statistics of natural scenes, and does not depend on prior experience. The HBO model consists of five steps: local similarity grouping with local normalization, mutual similarity grouping (local grouping is strengthened if the neighboring regions are similar to the same set of other regions), transitive grouping (good continuation), confidence grouping (neighboring regions far from the same-different decision boundary guide grouping of regions near the decision boundary), and region grouping (similarity grouping of the regions from the initial segmentation). We find that a local similarity grouping process, trained to maximize accuracy, predicts human texture discrimination accuracy. We then find that the four additional steps accurately segment images with randomly shaped regions containing arbitrary natural textures. The success of the model depends on all the steps, but especially on local-similarity and transitive grouping. We also find that the transitive grouping allows correct segmentation of non-stationary texture regions (e.g., textures slanted in depth). Further, we find that when illumination varies across the image, local normalization enables both correct texture segmentation and estimation of illumination change. Finally, we find that unlike our model large state-of-the-art deep networks often fail on these stimuli.

animal behavior and cognition↗

bioRxiv2024

Target Identification Under High Levels of Amplitude, Size, Orientation and Background Uncertainty

Many natural tasks require the visual system to classify image patches accurately into target categories, including the category of no target. Natural target categories often involve high levels of within-category variability (uncertainty), making it challenging to uncover the underlying computational mechanisms. Here, we describe these tasks as identification from a set of exhaustive, mutually exclusive target categories, each partitioned into mutually exclusive subcategories. We derive the optimal decision rule and present a computational method to simulate performance for moderately large and complex tasks. We focus on the detection of an additive wavelet target in white noise with five dimensions of stimulus uncertainty: target amplitude, orientation, scale, background contrast, and spatial pattern. We compare the performance of the ideal observer with various heuristic observers. We find that a properly normalized heuristic MAX observer (SNN-MAX) approximates optimal performance. We also find that a convolutional neural network trained on this task approaches but does not reach optimal performance, even with extensive training. We measured human performance on a task with three of these dimensions of uncertainty (orientation, scale, and background pattern). Results show that the pattern of hits and correct rejections for the ideal and SNN-MAX observers (but not a simple MAX observer) aligns with the data. Additionally, we measured performance under low uncertainty (without scale and orientation uncertainty) and found that the effect of uncertainty on the performance is smaller than any of the models predicted. This smaller-than-expected effect can largely be explained by including biologically plausible levels of intrinsic position uncertainty. PrecisWe describe target identification tasks in terms of mutually exclusive categories and subcategories and derive the optimal decision rule. Simulations of ideal and heuristic observers were compared to human data under high and low levels of extrinsic uncertainty.

neuroscience↗

bioRxiv2023

Neural Correlates of Perceptual Similarity Masking in Primate V1

Visual detection is a fundamental natural task. Detection becomes more challenging as the similarity between the target and the background in which it is embedded increases, a phenomenon termed "similarity masking". To test the hypothesis that V1 contributes to similarity masking, we used voltage sensitive dye imaging (VSDI) to measure V1 population responses while macaque monkeys performed a detection task under varying levels of target-background similarity. Paradoxically, we find that during an initial transient phase, V1 responses to the target are enhanced, rather than suppressed, by target-background similarity. This effect reverses in the second phase of the response, so that in this phase V1 signals are positively correlated with the behavioral effect of similarity. Finally, we show that a simple model with delayed divisive normalization can qualitatively account for our findings. Overall, our results support the hypothesis that a nonlinear gain control mechanism in V1 contributes to perceptual similarity masking.

neuroscience↗

bioRxiv2021

Stereo Slant Discrimination of Planar 3D Surfaces: Standard vs. Planar Cross-Correlation

Binocular stereo cues are important for discriminating 3D surface orientation, especially at near distances. We devised a single-interval task where observers discriminated the slant of a densely textured planar test surface relative to a textured planar surround reference surface. Although surfaces were rendered with correct perspective, the stimuli were designed so that the binocular cues dominated performance. Slant discrimination performance was measured as a function of the reference slant and the level of uncorrelated white noise added to the test-plane images in the left and right eye. We compared human performance with an approximate ideal observer (planar cross correlation, PCC) and two sub-ideal observers. The PCC observer uses the image in one eye and back projection to predict the test image in the other eye for all possible slants, tilts, and distances. The estimated slant, tilt, and distance are determined by the prediction that most closely matches the measured image in the other eye. The first sub-ideal observer (local PCC, LPCC) applies planar cross correlation over local neighborhoods and then pools estimates across the test plane. The second sub-optimal observer (standard cross correlation, SCC), uses only positional disparity information. We find that the ideal observer (PCC) and the first sub-ideal observer (LPCC) outperform the second sub-ideal observer (SCC), demonstrating the benefits of structural disparities. We also find that all three model observers can account for human performance, if two free parameters are included: a fixed small level of internal estimation noise, and a fixed overall efficiency scalar on slant discriminability. PrecisWe measured human stereo slant discrimination thresholds for accurately-rendered textured surfaces designed so that performance is dominated by binocular-disparity cues. We compared human performance with an approximate ideal observer and two sub-ideal observers.

animal behavior and cognition↗

bioRxiv2021

Similar neural and perceptual masking effects of low-power optogenetic stimulation in primate V1

Can direct stimulation of primate V1 substitute for a visual stimulus and mimic its perceptual effect? To address this question, we developed an optical-genetic toolkit to "read" neural population responses using widefield calcium imaging, while simultaneously using optogenetics to "write" neural responses into V1 of behaving macaques. We focused on the phenomenon of visual masking, where detection of a dim target is significantly reduced by a co-localized medium-brightness pedestal. Using our toolkit, we tested whether V1 optogenetic stimulation can recapitulate the perceptual masking effect of a visual pedestal. We find that, similar to a visual pedestal, low-power optostimulation can significantly reduce visual detection sensitivity, that a sublinear interaction between visual and optogenetic evoked V1 responses could account for this perceptual effect, and that these neural and behavioral effects are spatially selective. Our toolkit and results open the door for further exploration of perceptual substitutions by direct stimulation of sensory cortex.

neuroscience↗

bioRxiv2020

Similar masking effects of natural backgrounds on detection performances in humans, macaques, and macaque-V1 population responses

Visual systems evolve to process the stimuli that arise in the organisms natural environment and hence to fully understand the neural computations in the visual system it is important to measure behavioral and neural responses to natural visual stimuli. Here we measured psychometric and neurometric functions and thresholds in the macaque monkey for detection of a windowed sine-wave target in uniform backgrounds and in natural backgrounds of various contrasts. The neurometric functions and neurometric thresholds were obtained by near-optimal decoding of voltage-sensitive-dye-imaging (VSDI) responses at the retinotopic scale in primary visual cortex (V1). The results were compared with previous human psychophysical measurements made under the same conditions. We found that human and macaque behavioral thresholds followed the generalized Webers law as function of contrast, and that both the slopes and the intercepts of the threshold functions match each other up to a single scale factor. We also found that the neurometric thresholds followed the generalized Webers law and that the neurometric slopes and intercepts matched the behavioral slopes and intercepts up to a single scale factor. We conclude that human and macaque ability to detect targets in natural backgrounds are affected in the same way by background contrast, that these effects are consistent with population decoding at the retinotopic scale by down-stream circuits, and that the macaque monkey is an appropriate animal model for gaining an understanding of the neural mechanisms in humans for detecting targets in natural backgrounds. Finally, we discuss limitations of the current study and potential next steps. New & NoteworthyWe measured macaque detection performance in natural images and compared their performance to the detection sensitivity of neurophysiological responses recorded in the primary visual cortex (V1), and to the performance of human subjects. We found that (i) human and macaque behavioral performances are in quantitative agreement, (ii) are consistent with near-optimal decoding of V1 population responses. SignificanceNatural selection guarantees that neural computations will be matched to the task-relevant natural stimuli in the organisms environment, and thus it is crucial to measure behavioral and neural responses to natural stimuli. We measured the ability of macaque monkeys to detect targets in natural images and compared their performance to neurophysiological responses recorded in the macaques primary visual cortex (V1), and to the performance of humans under the same conditions. We found that (i) human and macaque behavioral performance are in quantitative agreement, (ii) are consistent with near-optimal population decoding of V1 neural responses, and (iii) that the macaque monkey is an appropriate animal model for gaining understanding of the neural mechanisms in humans for detecting targets in natural backgrounds.

neuroscience↗