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在神经工程应用中使用灵长类前额叶皮层的经济价值信号.

Tevin C Rouse1, Shira M Lupkin2, Vincent B McGinty1

  • 1Center for Molecular and Behavioral Neuroscience, Rutgers University-Newark, Newark, NJ, United States of America.

Journal of neural engineering
|September 25, 2025
PubMed
概括

研究人员开发了使用认知信号进行决策的脑机界面 (BMI). 这些自适应神经解码器能够以超过70%的准确度预测选择,帮助神经工程应用中的目标导向行为.

科学领域:

  • 神经科学是一个神经科学.
  • 认知科学 认知科学
  • 机器学习 机器学习

背景情况:

  • 大脑机器接口 (BMI) 传统上使用运动/感觉信号.
  • 抽象的认知信号为神经工程提供了尚未开发的潜力.
  • 经济价值是决策的关键认知构造.

研究的目的:

  • 在BMI背景下探索与经济价值相关的神经信号的使用.
  • 开发基于深度学习的神经解码器,用于预测基于价值的决策任务中的选择.
  • 通过强化学习实现适应性解码器,用于多步骤决策.

主要方法:

  • 从非人类灵长类动物的轨道前皮层收集了多变量时间序列数据.
  • 开发了深度学习的神经解码器来预测选择.
  • 采用了基于强化学习的适应性解码器培训方法.

主要成果:

  • 使用主观价值信号预测子选择的平均准确率达到了>70%.
  • 即使在客观上平等的选择选项中,也表现出高于机会的准确性.
  • 显示的解码器架构可以执行与选择相关的动作和动作序列.
  • 开发了一个神经预测模型,预测选择的时间提前300 ms.
关键词:
大脑 机器界面在决策过程中做出决定.深度学习是一种深度学习.

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结论:

  • 使用者偏好信息的神经工程设备利用认知信号是可行的.
  • 将抽象的认知信号与运动/感官数据相结合可能会提高准确性.
  • 未来的系统可能需要对最小的用户输入场景进行信任测量.