Bionic Vision Lab

@bionicvisionlab.org

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👁️🧠🖥️🧪🤖 What would the world look like with a bionic eye? Interdisciplinary research group at UC Santa Barbara. PI: @mbeyeler.bsky.social‬ #BionicVision #Blindness #NeuroTech #VisionScience #CompNeuro #NeuroAI

Flyer advertising the poster. Text says: Catrina Coe. Behavioral Determinants of Bionic Vision Adoption in a U.S.-Based Cohort of Blind Adults Poster #0523 • 10:15am - 12:00pm MT • May 6th

Flyer advertising the two poster. Text says: Lily Turkstra. Perceptual Integration Window in Argus II: Millisecond Timing and Axon-Bundle Geometry Determine Two-Point Perception. Poster #0978 • 8:30am - 10:15am MT • May 5th.
2. Catrina Coe. Behavioral Determinants of Bionic Vision Adoption in a U.S.-Based Cohort of Blind Adults Poster #0523 • 10:15am - 12:00pm MT • May 6th

Flyer advertising the two accepted posters. Text says: UC SANTA BARBARA
BIONIC VISION LAB @ ARVO 2026. Followed by
1. Lily Turkstra. Perceptual Integration Window in Argus II: Millisecond Timing and Axon-Bundle Geometry Determine Two-Point Perception. Poster #0978 • 8:30am - 10:15am MT • May 5th.
2. Catrina Coe. Behavioral Determinants of Bionic Vision Adoption in a U.S.-Based Cohort of Blind Adults Poster #0523 • 10:15am - 12:00pm MT • May 6th

Bar plot comparing fuzzing strategies. y-axis: combined violation and diversity score. Each bar represents an average of normalized violations and normalized diversity score, equally weighted.
Our metrics are shown in green, with our two best VO-KMVP and VO-KMOC highlighted in dark green. Neuron coverage metrics are shown in purple, and basic metrics in red. Conventional testing (model test set with no mutations) is shown in blue. Our methods reach scores above 0.8, whereas conventional testing sits at 0.1

Schematic labeled human-in-the-loop optimization (HILO). It shows two stimuli on the left: pulse trains with varying stimulus amplitude over time. A participant has to choose which stimulus appears brighter. This feedback is used to inform a Gaussian process model that chooses the next stimulus pair, with the goal of finding the stimulus with the lowest overall charge to elicit perception

left: experimental setup showing an implantee with an introcortical prosthesis and example phosphenes described as a large filled circle, a half-moon, and a tiny dot.
right: schematic showing cross-temporal decoding of delay period activity. Over 406 trials, working memory content could be decoded during the delay period in 88.5% of delay period windows.

Table showing different network diagrams under test, trained either on MNIST or Fashion-MNIST, with either soft or hard winner-take-all (WTA) wiring. Synaptic weights showed either holistic or parts-based representations of images

Simulated responses of a bipolar cell mosaic to simulated electrical stimulation and the corresponding decoded phosphenes. Small phosphenes appear focal and colored, whereas larger phosphenes most often appear with a white-ish, yellow-ish tint.

Boxplots showing task accuracy for two experimental tasks—Letter Recognition and Motion Discrimination—grouped by five raster patterns: No Raster (blue), Checkerboard (orange), Vertical (green), Horizontal (brown), and Random (pink). Each colored boxplot shows the median, interquartile range, and individual participant data points.

In both tasks, Checkerboard and No Raster yield the highest median accuracy.

Horizontal and Random patterns perform the worst, with more variability and lower scores.

Significant pairwise differences (p < .05) are indicated by horizontal bars above the plots, showing that Checkerboard significantly outperforms Random and Horizontal in both tasks.

A dashed line at 0.125 marks chance-level performance (1 out of 8).

These results suggest Checkerboard rastering improves perceptual performance compared to conventional or unstructured patterns.

Diagram showing the four-step pipeline for simulating prosthetic vision in VR.
Step 1: A virtual camera captures the user’s view, guided by eye gaze. The image is converted to grayscale and blurred for preprocessing.
Step 2: The preprocessed image is mapped onto a simulated retinal implant with 100 electrodes. Electrodes are activated based on local image intensity and grouped into raster groups. Raster Group 1 is highlighted.
Step 3: Simulated perception is shown with and without rastering. Without rastering (top), all electrodes are active, producing a more complete but unrealistic percept. With rastering (bottom), only 20 electrodes are active per frame, resulting in a temporally fragmented percept. Phosphene shape depends on parameters for spatial spread (ρ) and elongation (λ).
Step 4: The rendered percept is updated with temporal effects and presented through a virtual reality headset.

Raster pattern configurations used in the study, shown as 10×10 electrode grids labeled with numbers 1 through 5, representing five sequentially activated timing groups.

1. Horizontal: Each row of electrodes belongs to one group, with activation proceeding top to bottom.

2. Vertical: Each column is a group, activated left to right.

3. Checkerboard: Electrode groups are arranged to maximize spatial separation, forming a checkerboard-like layout.

4. Random: Group assignments are randomly distributed across the grid, with no spatial structure. This pattern was re-randomized every five frames to test unstructured activation.
Each group is represented with different shades of gray and labeled numerically to indicate activation order.

Raster patterns in simulated prosthetic vision. On the left, a natural scene of a yellow car is shown, followed by its transformation into a prosthetic vision simulation using a 10×10 grid of electrodes (red dots). Below this, a zoomed-in example shows the resulting phosphene pattern. To comply with safety constraints, electrodes are divided into five spatial groups activated sequentially across ~220 milliseconds. Each row represents a different raster pattern: vertical (columns activated left to right), horizontal (rows top to bottom), checkerboard (spatially maximized separation), and random (reshuffled every five frames). For each pattern, five panels show how the scene is progressively built across the five raster groups. Vertical and horizontal patterns show strong directional streaking. Checkerboard shows more uniform activation and perceptual clarity. Random appears spatially noisy and inconsistent.