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相关实验视频

Updated: Jun 8, 2025

Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States
06:25

Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States

Published on: January 19, 2024

925

从使用深度学习的神经元活动的双光子成像解码多肢运动.

Seungbin Park1, Megan Lipton1, Maria C Dadarlat1

  • 1Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47906, United States of America.

Journal of neural engineering
|November 7, 2024
PubMed
概括

深度学习从缓慢的光学大脑记录中解码多肢运动. 大脑机器接口 (BMI) 的这一进步使用了一种新的循环神经网络来解释神经活动以控制假肢.

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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
  • 机器学习 机器学习

背景情况:

  • 大脑机器接口 (BMI) 旨在在神经损伤后恢复功能.
  • 从神经活动解码运动意图对BMI至关重要.
  • 双光子 (2p) 成像提供高分辨率的神经记录,但面临的挑战是缓慢的采样率来解码快速运动.

研究的目的:

  • 应用深度学习来从2p成像数据解码多肢运动.
  • 为了克服将缓慢的光学成像数据与快速行为联系起来的挑战.
  • 建立用于使用光学BMI控制多个肢体的基础.

主要方法:

  • 开发了一个循环编码器-解码器网络 (LSTM-encdec),输出比输入更长.
  • 应用了LSTM-encdec模型来分析从跑步小鼠的传感运动区域的2p成像数据.
  • 使用可解释性测量来验证解码准确性.

主要成果:

  • LSTM-encdec模型准确地解码了所有四个肢体 (对侧和侧前后肢体) 的信息.
  • 解码是从单个皮质半球记录的成像数据中实现的.
  • 该方法证明了解码复杂,多肢运动的可行性.

结论:

  • 深度学习,特别是LSTM-encdec网络,可以有效地从缓慢的2p成像数据中解码多肢运动.
  • 这种方法提高了光学成像对大脑机器接口的实用性.
  • 这些发现为开发下一代能够控制多个肢体的光学BMI铺平了道路.
关键词:
大脑 机器界面深度学习是一种深度学习.多肢体是一种多肢体.神经解码的神经解码光学大脑 机器界面传感器运动器传感器两光子成像技术的二光子成像

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