Artificial Vision Across the Visual Pathway

Visual Prostheses Brain Stimulation Computational Neuroscience Visual Perception Psychophysics TMS EEG

Artificial vision depends on where in the visual system we intervene. Stimulation of the retina, LGN, and visual cortex can all produce visual percepts, but each target comes with different anatomy, circuitry, and constraints. We study those differences and build models that connect stimulation to neural responses and perception.

What We Study

We combine computational modeling with psychophysics and neurostimulation. In the retina and LGN, we use known anatomy and physiology to predict the percepts produced by electrical stimulation. In cortex, we study phosphene perception and ask which stimulation-evoked responses propagate through the visual system. Across these systems, we are interested in which biological details actually matter for predicting what someone will see.

Current Directions

  • Retina. Models of how retinal circuitry, electrode geometry, and stimulation parameters affect the spatial, temporal, and chromatic properties of artificial vision.
  • LGN. Models of phosphene perception that account for the organization of visual representations in the thalamus.
  • Cortex. Psychophysics and neurostimulation experiments on phosphene perception, individual variability, and the spread of stimulation-evoked activity.
  • Computational models. Predictive models connecting stimulation, anatomy, neural activity, and perception.
  • Psychophysics. Quantitative test batteries for characterizing artificial visual percepts across participants and stimulation methods.

Team

Leads

Emily M. JoycePhD Student Hannah L. StonePhD Student Eirini SchoinasResearch Assistant

Affiliates

Karolina HuangResearch Assistant Ryan KlopfensteinResearch Assistant Samantha LundResearch Assistant

Principal Investigator

Michael BeyelerAssociate Professor

Collaborator

Eduardo FernándezUniversidad Miguel Hernández, Spain

Funding

Avatar

DP2-LM014268: Towards a Smart Bionic Eye: AI-Powered Artificial Vision for the Treatment of Incurable Blindness
PI: Michael Beyeler (UCSB)

September 2022 - August 2027
Common Fund, Office of the Director (OD); National Library of Medicine (NLM)
National Institutes of Health (NIH)

Publications

We developed a data-driven neural control framework for a visual cortical prosthesis in a blind human, showing that deep learning can synthesize efficient, stable stimulation patterns that reliably evoke percepts and outperform conventional calibration methods.

We present SymbolSight, a computational framework that selects symbol-to-letter mappings to minimize confusion among frequently adjacent letters. Using simulated prosthetic vision (SPV) and a neural proxy observer, we estimate pairwise symbol confusability and optimize assignments using language-specific bigram statistics.

We introduce a computational virtual patient (CVP) pipeline that integrates anatomically grounded phosphene simulation with task-optimized deep neural networks to forecast patient perceptual capabilities across diverse prosthetic designs and tasks.

We introduce two computational models designed to accurately predict phosphene fading and persistence under varying stimulus conditions, cross-validated on behavioral data reported by nine users of the Argus II Retinal Prosthesis System.

We present a series of analyses on the shared representations between evoked neural activity in the primary visual cortex of a blind human with an intracortical visual prosthesis, and latent visual representations computed in deep neural networks.

We retrospectively analyzed phosphene shape data collected form three Argus II patients to investigate which neuroanatomical and stimulus parameters predict paired-phosphene appearance and whether phospehenes add up linearly.

We present explainable artificial intelligence (XAI) models fit on a large longitudinal dataset that can predict perceptual thresholds on individual Argus II electrodes over time.

We show that a neurologically-inspired decoding of CNN activations produces qualitatively accurate phosphenes, comparable to phosphenes reported by real patients.

We optimize electrode arrangement of epiretinal implants to maximize visual subfield coverage.

We explored the causes of high thresholds and poor spatial resolution within the Argus II epiretinal implant.

We present a phenomenological model that predicts phosphene appearance as a function of stimulus amplitude, frequency, and pulse duration.

We present an explainable artificial intelligence (XAI) model fit on a large longitudinal dataset that can predict electrode deactivation in Argus II.

We systematically explored the space of possible implant configurations to make recommendations for optimal intraocular positioning of Argus II.

We show that the perceptual experience of retinal implant users can be accurately predicted using a computational model that simulates each individual patient’s retinal ganglion axon pathways.

pulse2percept is an open-source Python simulation framework used to predict the perceptual experience of retinal prosthesis patients across a wide range of implant configurations.

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