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Jacob Granley

3 accepted papers

2023

Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture

NeurIPS 2023poster

Convolutional neural networks (CNNs) have recently emerged as promising models of the ventral visual stream, despite their lack of biological specificity. While current state-of-the-art models of the primary visual cortex (V1) have surfaced from training with adversarial examples and extensively aug…

Cited by 8SourcePDFScholar
2023

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

NeurIPS 2023poster

Neuroprostheses show potential in restoring lost sensory function and enhancing human capabilities, but the sensations produced by current devices often seem unnatural or distorted. Exact placement of implants and differences in individual perception lead to significant variations in stimulus respon…

2022

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

NeurIPS 2022accept

Sensory neuroprostheses are emerging as a promising technology to restore lost sensory function or augment human capabilities. However, sensations elicited by current devices often appear artificial and distorted. Although current models can predict the neural or perceptual response to an electrical…

Cited by 24SourcePDFScholar