Search Results for author: Michael Beyeler

Found 9 papers, 3 papers with code

Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses

1 code implementation NeurIPS 2023 Jacob Granley, Tristan Fauvel, Matthew Chalk, Michael Beyeler

We show that our approach quickly learns a personalized stimulus encoder, leads to dramatic improvements in the quality of restored vision, and is robust to noisy patient feedback and misspecifications in the underlying forward model.

Bayesian Optimization

Adapting Brain-Like Neural Networks for Modeling Cortical Visual Prostheses

no code implementations27 Sep 2022 Jacob Granley, Alexander Riedel, Michael Beyeler

Overall, this is an essential first step towards building brain-like models of electrical stimulation, which may not just improve the quality of vision provided by cortical prostheses but could also further our understanding of the neural code of vision.

Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses

no code implementations26 May 2022 Jacob Granley, Lucas Relic, Michael Beyeler

Sensory neuroprostheses are emerging as a promising technology to restore lost sensory function or augment human capabilities.

Efficient visual object representation using a biologically plausible spike-latency code and winner-take-all inhibition

no code implementations20 May 2022 Melani Sanchez-Garcia, Tushar Chauhan, Benoit R. Cottereau, Michael Beyeler

Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory.

Object Object Recognition

Deep Learning-Based Perceptual Stimulus Encoder for Bionic Vision

no code implementations10 Mar 2022 Lucas Relic, BoWen Zhang, Yi-Lin Tuan, Michael Beyeler

Retinal implants have the potential to treat incurable blindness, yet the quality of the artificial vision they produce is still rudimentary.

Greedy Optimization of Electrode Arrangement for Epiretinal Prostheses

no code implementations4 Mar 2022 Ashley Bruce, Michael Beyeler

Visual neuroprostheses are the only FDA-approved technology for the treatment of retinal degenerative blindness.

Dictionary Learning

U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina

1 code implementation9 Jul 2021 Shuyun Tang, Ziming Qi, Jacob Granley, Michael Beyeler

The network consists of a novel bottleneck attention block that combines and refines self-attention, channel attention, and relative-position attention to highlight retinal abnormalities that may be important for fovea and OD segmentation in the degenerated retina.

Fovea Detection Optic Disc Detection +2

Deep Learning--Based Scene Simplification for Bionic Vision

1 code implementation30 Jan 2021 Nicole Han, Sudhanshu Srivastava, Aiwen Xu, Devi Klein, Michael Beyeler

Retinal degenerative diseases cause profound visual impairment in more than 10 million people worldwide, and retinal prostheses are being developed to restore vision to these individuals.

Monocular Depth Estimation Scene Understanding +1

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