卷积神经网络揭示了后壁皮层皮层中达到-抓取编码的特性
Davide Borra1, Matteo Filippini2, Mauro Ursino3
1Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Cesena Campus, Cesena, 47522, Italy.
Computers in biology and medicine
|March 16, 2024
概括
可解释的人工智能从神经信号中解码了握力类型. 研究人员使用卷积神经网络分析后额叶皮层V6A活动,揭示神经元如何在运动准备和执行过程中编码掌握复杂性.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
背景情况:
- 深度神经网络 (DNN) 用于解码来自神经信号的运动状态,用于大脑-计算机接口.
- 对DNN决策的有限的神经生理学解释阻碍了解码器的可靠性和对神经编码的理解.
研究的目的:
- 应用可解释的人工智能 (XAI) 来解码从单个神经元记录的触摸到抓取动作.
- 揭示抓握编码的空间和时间神经属性,在后壁面区域V6A.
- 提高基于DNN的神经解码器的透明度和可靠性.
主要方法:
- 利用卷积神经网络 (CNN) 与解释技术相结合.
- 分析了从后壁面区域V6A的单个神经元记录,这些记录是在达到到抓取任务期间进行的.
- 解码了5种不同的握力类型,并确定了有影响力的细胞和时间点.
主要成果:
- 美国有线电视新闻网准确地解码了5种不同的握力类型.
- 解释技术确定了关键神经元和时间段,用于抓握预测.
- 抓编码在动作准备过程中启动,并在执行过程中达到顶峰.
- 背部V6A神经元编码了先进的掌握,而腹部V6A神经元编码了初始的掌握,显示出与技能水平的线性趋势.
结论:
- 该XAI方法增强了对V6A.握力编码的理解.
- 该方法揭示了神经属性,没有先验假设.
- 这种方法可以被通用化,用于研究其他运动和认知任务中的神经相关性.
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