在具有深度学习的视觉假肢中塑造神经活动
Domingos Castro1,2, David B Grayden3,4, Hamish Meffin3,4
1Neuroengineering and Computational Neuroscience Lab, i3S-Institute for Research and Innovation in Health, University of Porto, Porto, Portugal.
Journal of neural engineering
|July 10, 2024
概括
人工神经网络 (ANN) 为视网膜假肢中神经活动塑造 (NAS) 提供了一个无模型的解决方案. 与传统方法相比,这种方法通过创建更清晰的视网膜激活来增强视觉感知.
科学领域:
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
- 人工智能的人工智能
背景情况:
- 视网膜假体在视觉感知上面临限制,原因是电流从相邻的电极传播,减少单极刺激的空间分辨率.
- 使用同时多极刺激的神经活动塑造 (NAS) 可以通过减弱过度刺激的传播来改善对神经激活模式的控制.
研究的目的:
- 提出和验证在视网膜假肢中用于神经活动塑造 (NAS) 的无模型人工神经网络 (ANN) 方法.
- 开发一种高效和个性化的视网膜刺激方法,以改善视觉假肢的结果.
主要方法:
- 开发了一种两阶段的ANN系统:在植入物数据上训练的测量预测网络 (MPN) 来预测视网膜反应,以及在自然图像上训练的刺激发生器网络.
- 刺激生成器网络利用MPN学习反向模型来确定高效的多极刺激模式.
- 验证是在使用现实的视网膜反应模型进行的.
主要成果:
- 与传统单极刺激相比,基于ANN的NAS方法显示了更明显的视网膜激活.
- 该ANN策略取得了与分析模型反转 (AMI) 相当的结果,但在模型无知和计算上更高效 (数量三级).
结论:
- 这种基于ANN的新协议可以实现高效和个性化的视网膜刺激.
- 这种方法有可能显著改善视网膜假体用户的视觉体验和生活质量.
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