使用感知反来优化皮质神经假体视力的贝叶斯式优化
Burcu Küçükoğlu1, Leili Soo2, David Leeftink1
1Department of Machine Learning and Neural Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
Journal of neural engineering
|July 2, 2025
概括
贝叶斯优化通过使用患者的反来提高光感知,有效地找到神经假体视觉的最佳电刺激模式. 这种方法加速了对盲人有效基生成的研究.
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
- 计算机科学 计算机科学
背景情况:
- 皮质神经假肢视力旨在通过电刺激在盲人中恢复光感知 (素).
- 在众多电极上优化刺激参数是具有挑战性的,因为搜索空间广.
- 目前的方法依赖于患者的反,这对于参数发现可能是低效的.
研究的目的:
- 调查贝叶斯优化 (BO) 在有效识别最佳电刺激协议对色素感知的有效性.
- 将BO与随机生成 (RG) 进行比较,以优化基于患者反的刺激参数.
- 展示一种系统的方法来发现皮质神经假肢中有效的刺激模式.
主要方法:
- 采用基于信任区域的贝叶斯优化算法来优化40个电极的刺激参数.
- 利用患者的感知评分 (利克尔特尺度) 作为反来指导BO过程.
- 对比BO驱动的刺激协议生成与随机生成在一个有96通道微电极阵列的患者.
主要成果:
- 与随机生成相比,贝叶斯优化导致患者评级逐渐趋同,以提高感知质量.
- 博发现的协议越来越倾向于更高的总电流,与患者偏好更明亮的烯相关.
- 在之前的研究中发现的有效电极被优先选择并通过BO算法分配更高的电流.
结论:
- 贝叶斯优化显著提高了发现神经假体视觉最佳刺激参数的效率.
- 这种方法利用患者的反来系统地改进刺激方案,改善素感知.
- 这些发现支持BO在加速视觉假肢研究和开发方面的临床实用性.
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