Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jan 9, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.5K

关闭循环神经刺激的学习时间基础向量.

Matthew J Bryan, Felix Schwock, Azadeh Yazdan-Shahmorad

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括

    我们开发了一种新的时间基础功能模型 (TBFM),用于预测神经对刺激的反应. 这种人工智能模型是高效的,准确的,适用于神经疾病的闭环大脑刺激.

    相关概念视频

    您也可能阅读

    相关文章

    通过共同作者、期刊和引用图与本文相关的文章。

    排序
    Same author

    Hierarchical Active Inference Using Successor Representations.

    Neural computation·2026
    Same author

    Smart Dura: a functional artificial dura for multi-modal neural recording and modulation.

    Microsystems & nanoengineering·2026
    Same author

    A large-scale optogenetic neurophysiology platform for improving accessibility in non-human primate behavioral experiments.

    Nature communications·2026
    Same author

    Multimodal Optical Imaging and Modulation with Simultaneous Electrophysiology Through Smart Dura in Non-Human Primates.

    Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
    Same author

    Culturally-attuned AI: Implicit learning of altruistic cultural values through inverse reinforcement learning.

    PloS one·2025
    Same author

    Predictive coding with spiking neural networks: A survey.

    Neural networks : the official journal of the International Neural Network Society·2025

    科学领域:

    • 计算神经科学是一种神经科学.
    • 人工智能在医学中的应用
    • 神经技术的神经技术

    背景情况:

    • 准确预测神经反应对于有效的大脑刺激至关重要.
    • 现有的模型往往缺乏实时闭环应用所需的效率和适应性.

    研究的目的:

    • 介绍一个新的时间基础功能模型 (TBFM) 用于空间时间神经反应预测.
    • 启用基于模型的控制技术,用于闭环神经刺激.
    • 通过优化效率和延迟来证明模型的临床相关性.

    主要方法:

    • 开发了一个TBFM框架,学习基于时间的功能.
    • 将TBFM应用于非人类灵长类动物光遗传刺激实验中的微电皮图 (μECog) 数据.
    • 与复杂的非线性动态系统模型和线性状态空间模型 (LSSM) 进行TBFM性能比较.

    主要成果:

    • TBFMs的准确性与复杂的非线性模型相美,并超过了LSSMs.
    • 需要最小的数据收集 (<20分钟) 和培训时间 (<5分钟).
    • 在模拟的闭环实验中,成功地证明了将神经活动塑造为所需的模式.

    结论:

    • TBFMs提供了一种高效和准确的方法来建模神经对刺激的反应.
    • 该模型在样本效率,训练时间和延迟方面的优化弥合了对人工智能驱动的闭环刺激疗法的差距.
    • 这一框架有可能开发用于神经疾病的新型治疗方法.

    更多相关视频

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.8K
    An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
    10:51

    An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

    Published on: March 10, 2011

    14.2K

    相关实验视频

    Last Updated: Jan 9, 2026

    Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
    05:19

    Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

    Published on: November 12, 2019

    7.5K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.8K
    An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
    10:51

    An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

    Published on: March 10, 2011

    14.2K