Neural Activity Shaping in Visual Prostheses with Deep Learning

Leite de Castro, D.; Grayden, D. B.; Meffin, H.; Spencer, M. J.

doi:10.1101/2023.12.20.572123

Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.12.20.572123

Neural Activity Shaping in Visual Prostheses with Deep Learning

Leite de Castro, D.·Grayden, D. B.·Meffin, H.·Spencer, M. J.

View original source ↗Export metadata ↓

Abstract

ObjectiveThe visual perception provided by retinal prostheses is poor and limited to images constructed of phosphenes generated by the electrodes. One limiting factor has been the conventional strategy used to encode the target image into a stimulation pattern. Under this strategy, if the electrode density is high, the current spread of neighbouring unipolar stimuli overlaps, leading to blurred images. Simultaneous multipolar stimulation guided by the measured neural responses can attenuate excessive spread of excitation and allows for a more precise electrical input to the retina. However, it is far from trivial to predict what multipolar stimulus pattern will elicit the desired retinal response for a given target image. Here, we propose to solve this problem using an Artificial Neural Network (ANN) that could be trained with data acquired from the implant itself. ApproachOur method consists of two ANNs trained sequentially. The Measurement Predictor Network (MPN) is trained on data from the implant and is used to predict how the retina responds to multipolar stimulation by learning the forward model. The Stimulus Generator Network (STG) is trained on a large dataset of natural images and uses the trained MPN to determine efficient multipolar stimulus patterns by learning the inverse model. We validate our method in silico using a realistic model of retinal response to multipolar stimulation. Main ResultsWe show that the simulated retinal activations elicited with our ANN-based approach are considerably sharper when compared with the conventional method used in existing devices. The SGN finds multipolar stimulation patterns that are tuned to a specific retina, thus providing patient-specific stimuli. Also, due to its small computational cost, the SGN can output stimulation patterns at a very high rate. SignificanceOur novel protocol opens the door to personalized multipolar retinal stimulation, which may improve the visual experience and quality of life of retinal prosthesis users.

Source connections

Published article ↗

Explore related subjects

bioengineering Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Leite de Castro, D., Grayden, D. B., Meffin, H., Spencer, M. J.. 2023-12-21. Neural Activity Shaping in Visual Prostheses with Deep Learning. https://doi.org/10.1101/2023.12.20.572123

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Comparative study of chlorophyll measurement in Physcomitrium patens moss using a conventional microscope adapted for combined 2D+1D imaging and spectral analysis

Imaging spectroscopy often requires expensive and complex equipment. Here we show a simple procedure for attaching a standard miniature fiber spectrometer to a conventional microscope, allowing easy integration of 2D imaging with 1D high-resolution spectral measurements. This combination provides much of the benefit of a full imaging spectrometer without the large equipment investment, and we provide instructions for modifying microscopes to this setup and the present measurements of living cells that demonstrate their performance. Using this setup, we compare the quantitative measurement of chlorophyll concentration in Physcomitrium patens moss using color imaging and spectral sampling.

bioengineering↗

bioRxiv2026

De novo designed single-domain antibodies protect against lethal cobra venom neurotoxicity in vivo

Generative protein design can now rapidly produce de novo binders with high affinity and functional activity against a wide range of targets, including lethal snake venom toxins. However, so far most reported successes rely on new-to-nature scaffolds with limited therapeutic precedent. Single-domain antibodies (VHHs) offer a clinically validated alternative scaffold that can bind and neutralize long-chain -neurotoxins, which are some of the most lethal components in snake venoms. Here we compare three recently established de novo design models with VHH-design capabilities (Germinal, RFantibody, and BoltzGen) for their ability to generate VHHs against the neurotoxin -cobratoxin from the monocled cobra (Naja kaouthia). Using standardized model inputs and evaluation criteria based on AlphaFold3 interface confidence (ipTM) and RMSD self-consistency, we find that Germinal was the only method to generate designs passing stringent in silico criteria for experimental testing. We therefore performed a larger Germinal design campaign employing three different VHH frameworks and experimentally validated 46 designs in vitro. Of these, 42 expressed as soluble proteins and we identified four binding hits derived from two of the three tested frameworks. Of the four binders, two lead candidates were further characterized and demonstrated high affinity (KDs of 4.1 nM and 10.8 nM), monomeric behavior and low polyreactivity, indicating favorable biophysical and developability properties, as well as functional toxin neutralization in vitro. To assess their therapeutic potential we investigated their ability to protect against -cobratoxin toxicity in vivo. Both candidates fully protected mice after -cobratoxin challenge, with 100% survival compared to a lethal control. One candidate also retained notable neutralization capacity against whole venom of Naja kaouthia with a survival of 56%, while the other protected 22% when tested in a rescue setting. Together, we demonstrate that de novo VHH design can generate high affinity single-domain antibodies with in vivo protection against lethal cobra venom neurotoxicity, and provide practical insights into method- and framework-dependent performance.

bioengineering↗

bioRxiv2026

Simple Feedback for Complex Movement: Capturing Whole-Limb Reorganization during Single-IMU Gait Retraining

Clinical gait retraining typically relies on multi-sensor arrays and high-dimensional feedback displays, imposing setup and interpretation burdens that limit routine clinical deployment. We developed a single-IMU visual biofeedback system that delivers real-time feedback of Lower Limb Trajectory Error (LLTE), a composite kinematic error metric integrating knee position and shank angle across the stance phase. Twenty able-bodied adults walked on a treadmill under two visual biofeedback targets (flexed-knee, extended-knee) while receiving either corrected (n=10) or uncorrected (n=8) feedback, where the correction accounted for limb orientation at initial contact. LLTE and stance-phase knee kinematics adapted consistently under the flexed-knee target for both feedback groups, with feedback formulation moderating the temporal trajectory of change. Adaptation toward the extended-knee target was limited, likely because participants were already operating near terminal knee extension and because the scalar error metric provided limited directional information for correction. Ankle range of motion (ROM) changed significantly across the stance phase under both target conditions, while hip ROM did not. Multiscale multivariate sample entropy (MSMVSE) increased monotonically with time scale across all conditions, with no statistically distinguishable difference between corrected and uncorrected feedback. These results suggest that single-IMU LLTE biofeedback can modify gait mechanics and that adaptation was expressed across multiple lower-limb segments rather than through changes at a single joint.

bioengineering↗