在视觉假肢中对深度刺激编码进行人类在循环中的优化
Jacob Granley1, Tristan Fauvel2, Matthew Chalk3
1Department of Computer Science, University of California, Santa Barbara.
Advances in neural information processing systems
|July 10, 2024
概括
这项研究引入了一种结合深度学习和贝叶斯优化的新方法,以个性化神经假肢刺激,显著改善视觉假肢患者的恢复感官功能质量.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 神经假肢的目的是恢复感官功能,但往往会产生不自然的感觉.
- 由于植入物放置和个体感知变化,个性化刺激优化具有挑战性.
- 像贝叶斯优化和深度学习这样的当前优化方法在处理高维或患者特定数据方面存在局限性.
研究的目的:
- 开发一种新的,可行的方法来优化神经假体的患者特异性刺激参数.
- 克服现有方法在个性化神经修复器感官反方面的局限性.
- 改善使用视觉假肢的患者恢复视力的质量.
主要方法:
- 一个深度编码器网络经过训练,通过逆转电视觉感知的前向模型来产生最佳刺激.
- 采用偏好的贝叶斯优化策略,通过对对刺激比较来微调患者特定的参数.
- 这种方法在最先进的视觉假肢模型上得到了验证.
主要成果:
- 提出的方法迅速学习了个别患者个性化的刺激编码器.
- 在恢复视力的质量方面取得了显著的改善.
- 该方法证明了对杂的患者反和前模型中不准确性的稳定性.
结论:
- 结合深度学习和贝叶斯优化,为个性化神经假体刺激提供了可行的解决方案.
- 这种混合方法可以显著提高视觉假肢患者的感知体验.
- 该方法可能适用于更广泛的神经假肢技术.
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