Michael Beyeler

@mbeyeler.bsky.social

1170 Followers 491 Following

👁️🧠🖥️🧪🤖 Associate Professor in @ucsb-cs.bsky.social and Psychological & Brain Sciences at @ucsantabarbara.bsky.social. PI of @bionicvisionlab.org. #BionicVision #Blindness #LowVision #VisionScience #CompNeuro #NeuroTech #NeuroAI

[1-213] Control of electrically evoked neural activity in human visual cortex using deep learning. Thursday, March 12, 20:30 - 23:30

Two-panel schematic illustrating the BIRD framework. Left panel shows independent pre-training of a teacher and a student network on different datasets, each optimized with its own task loss. Right panel shows representation-structure distillation: selected intermediate layers from teacher and student are compared via a representation loss, which aligns the geometry of their internal activations while the student is still trained on its own task loss. A snowflake icon indicates the teacher is frozen. The diagram emphasizes that behavior is transferred by aligning internal representation structure rather than outputs or shared data.

Flyer for the UCSB CRML Agentic AI Summit 2026. Friday, January 23, 2026. 8:00 AM in Henley Hall 1010.

Keynote speakers: Sujith Ravi (VP GenAi, Oracle), Jiantao Jiao (Director AI, Nvidia), Murphy Niu (UCSB), Diyi Yang (Stanford), Daniel Martin (UCSB).

Industry talks: Ang Li (CEO, Simular), Zackary Glazewski (Founding AI Engineer, ChipAgents), Eser Kandogan (Principal Research Engineer, Megagon Labs)

AI faculty highlights: Eric Wang, Yuheng Bu, Michael Beyeler, James Preiss, Miguel Eckstein

Headshots, names, and talk titles for the 3 keynote speakers:
1. Fabian Sinz, University of Tübingen: Foundation models for mouse vision
2. Nina Miolane, UC Santa Barbara: Geometric approaches to neural activity prediction
3. Cris Niell University of Oregon: Visual processing in freely moving mice

Screenshot of SfN's grad school fair - highlighted is Booth 66

Three bar charts show how well different models predict perception of detection, brightness, and color. Using only the stimulation parameters performs worst. Including brain activity recordings—especially pre-stimulus activity—makes predictions much better across all three perceptual outcomes.

Figure showing the ability of different methods to reproduce target neural activity patterns and the limits of generating synthetic responses. Left: A target neural response (bottom-up heatmap) is compared to recorded responses produced by linear, inverse neural network, and gradient optimization methods. In this example, the inverse neural network gives the closest match (MSE 0.74) compared to linear (MSE 1.44) and gradient (MSE 1.49). Center: A bar plot of mean squared error across all methods shows inverse NN and gradient consistently outperform linear and dictionary approaches. Right: A scatterplot shows that prediction error increases with distance from the neural manifold; synthetic targets (red) have higher error than natural targets (blue), illustrating that the system best reproduces responses within the brain’s natural activity space.

Figure comparing methods for shaping neural activity to match a desired target response. Left: the target response is shown as a heatmap. Three methods—linear, inverse neural network, and gradient optimization—produce different stimulation patterns (top row) and recorded neural responses (bottom row). Gradient optimization and the inverse neural network yield recorded responses that more closely match the target, with much lower error (MSE 0.35 and 0.50) than the linear method (MSE 3.28). Right: a bar plot of mean squared error across methods shows both gradient and inverse NN outperform linear, dictionary, and 1-to-1 mapping, approaching the consistency of replaying the original stimulus.

Figure comparing predicted and true neural responses to electrical stimulation. Left panels show two example stimulation patterns (top), predicted neural responses by the forward neural network (middle), and the actual recorded responses (bottom). The predicted responses closely match the true responses. Right panels show bar plots comparing model performance across methods. The forward neural network (last bar) achieves the lowest error (MSE) and highest explained variance (R²), significantly outperforming dictionary-based, linear, and 1-to-1 mapping approaches.

Diagram of the experimental setup for measuring electrically evoked neural activity. A stimulation pattern is chosen across electrodes on a Utah array (left). Selected electrodes deliver 167 ms trains of 50 pulses at 300 Hz (middle left), sent via stimulator and amplifier into the visual cortex of a participant (middle). Neural signals are recorded before and after stimulation across all channels, producing multi-unit activity traces (MUAe). The difference between pre- and post-stimulation activity (ΔMUAe) is computed (middle right) and visualized as a heatmap across electrodes, showing localized increases in neural responses (right).

Diagram showing three ways to control brain activity with a visual prosthesis. The goal is to match a desired pattern of brain responses. One method uses a simple one-to-one mapping, another uses an inverse neural network, and a third uses gradient optimization. Each method produces a stimulation pattern, which is tested in both computer simulations and in the brain of a blind participant with an implant. The figure shows that the neural network and gradient methods reproduce the target brain activity more accurately than the simple mapping.

Schematic illustrating the phosphenes elicited by an intracortical prosthesis. A 96-channel Utah array is shown, stimulated with biphasic pulse trains. An arrow points to drawings of visual percepts elicited by electrical stimulation

Example stimulus from Byron's image dataset: an unobscured version (left) depicting a man on a bike approaching a woman trying to cross the bike lane; a simulation of peripheral vision loss (center), where the woman is clearly visible but the man on the bike is obscured; and a simulation of central vision loss (right), where the man on the bike is apparent but the woman is obscured

Michael Beyeler smiles while receiving the framed Plous Award at UC Santa Barbara. College of Letters & Science's Dean Shelly Gable presents the award in front of a slide thanking collaborators and funders, with photos of colleagues and logos from NIH and the Institute for Collaborative Biotechnologies. The audience watches the moment from their seats.

A vibrant depiction of a multifaceted life. The foreground shows a scientist sitting at a computer, working on futuristic visualizations related to bionic vision. Nearby, a cozy corner features a piano as the centerpiece, with a guitar leaning beside it. In the background, an outdoor scene blends mountains, a beach, and trees in vibrant autumn colors. A family moment is captured with a father, mother, and two sons enjoying time together. A bookshelf filled with science and technology books hints at intellectual pursuits. Subtle references to The Hitchhiker’s Guide to the Galaxy, including a Marvin figure and Deep Thought object, add a personal touch. The overall mood is warm, intellectual, and family-oriented.

This image features a panoramic view of the University of California, Santa Barbara (UCSB) during twilight. The sky is a gradient of colors, from a warm pink and orange near the horizon, blending into a deep blue as it extends upward. The campus is seen against this backdrop, with lights from buildings providing a gentle contrast to the dimming sky. Prominently, there's a tall, narrow tower with lights that could be windows or decorative elements, standing out against the sky. Trees and other vegetation are scattered throughout the campus, adding a touch of greenery. In the distance, a calm ocean can be seen, and further back, a faint outline of mountains or large hills on the horizon completes the serene landscape. The banner "UC SANTA BARBARA" is superimposed at the top of the image, identifying the location.