The visual pathway involves complex networks of cells and regions which
contribute to the encoding and processing of visual information. While some
aspects of visual perception are understood, there are still many unanswered
questions regarding the exact mechanisms of visual encoding and the
organization of visual information along the pathway. This chapter discusses
the importance of visual perception and the challenges associated with
understanding how visual information is encoded and represented in the brain.
Furthermore, this chapter introduces the concept of neuroprostheses: devices
designed to enhance or replace bodily functions, and highlights the importance
of constructing computational models of the visual pathway in the
implementation of such devices. A number of such models, employing the use of
deep learning models, are outlined, and their value to understanding visual
coding and natural vision is discussed.

视觉途径、视觉感知、视觉编码、神经假肢和计算模型是重要研究领域。该文章介绍了视觉感知的重要性，讨论了视觉信息编码和大脑中信息组织的机制，以及如何使用计算模型来实现神经假肢并提高对于视觉编码和自然视觉的理解。

深度学习用于视觉神经假肢

Deep Learning for Visual Neuroprosthesis

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 response,
making personalized stimulus optimization a key challenge. Bayesian
optimization could be used to optimize patient-specific stimulation parameters
with limited noisy observations, but is not feasible for high-dimensional
stimuli. Alternatively, deep learning models can optimize stimulus encoding
strategies, but typically assume perfect knowledge of patient-specific
variations. Here we propose a novel, practically feasible approach that
overcomes both of these fundamental limitations. First, a deep encoder network
is trained to produce optimal stimuli for any individual patient by inverting a
forward model mapping electrical stimuli to visual percepts. Second, a
preferential Bayesian optimization strategy utilizes this encoder to optimize
patient-specific parameters for a new patient, using a minimal number of
pairwise comparisons between candidate stimuli. We demonstrate the viability of
this approach on a novel, state-of-the-art visual prosthesis model. 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.
Overall, our results suggest that combining the strengths of deep learning and
Bayesian optimization could significantly improve the perceptual experience of
patients fitted with visual prostheses and may prove a viable solution for a
range of neuroprosthetic technologies.

通过深度学习模型优化电刺激编码策略并结合贝叶斯优化，提高视觉前沿技术在神经假肢领域中的个性化感知效果。