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Found 10 papers, 3 papers with code
Beyond Sight: Probing Alignment Between Image Models and Blind V1
Neural activity in the visual cortex of blind humans persists in the absence of visual stimuli.
Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses
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.
Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition
In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems.
Adapting Brain-Like Neural Networks for Modeling Cortical Visual Prostheses
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
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
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.
Deep Learning-Based Perceptual Stimulus Encoder for Bionic Vision
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
Visual neuroprostheses are the only FDA-approved technology for the treatment of retinal degenerative blindness.
U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina
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.
Ranked #1 on
Fovea Detection
on REFUGE
Deep Learning--Based Scene Simplification for Bionic Vision
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.
