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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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一个概念验证尖端基于神经形态的大脑与计算机接口.

E B Dijkema, C M A Pennartz, U Olcese

    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

    这项研究展示了一种神经形态的大脑与计算机接口 (BCI),能够近乎实时地处理神经尖端事件. 这项技术对开发用于神经修复的先进闭环BCI充满希望.

    科学领域:

    • 神经科学是一个神经科学.
    • 计算机工程 计算机工程
    • 生物医学工程 生物医学工程

    背景情况:

    • 闭环脑电脑接口 (BCI) 提供了神经损伤后功能恢复的潜力.
    • 高的时空精度对于临床有效的BCI系统至关重要.
    • 目前的BCI通常需要广泛的神经信号预处理.

    研究的目的:

    • 为了展示一个概念验证的神经形态BCI系统.
    • 在神经形态硬件上使用尖端神经网络 (SNN) 几乎实时处理神经尖端事件.
    • 评估系统的延迟和闭环应用程序的处理能力.

    主要方法:

    • 开发了一个系统来获取神经信号,并向SpinNaker硬件上的SNN传输峰值事件.
    • 利用来自小鼠视觉皮层的体内记录和模拟的神经波形进行评估.
    • 从尖端检测到SNN输出尖端的往返延迟测量.

    主要成果:

    • 在没有隐藏SNN层的情况下,实现了4.69ms (±1.70ms) 的平均往返延迟.
    • 每一个额外的隐藏层都会增加大约3.65毫秒的延迟时间.
    • 几乎实时地成功处理神经尖峰,适合闭环干预.

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    结论:

    • 神经形态SNN可以快速处理神经信号,形成闭环BCI的基础.
    • 这种方法有可能绕过受损的神经通路.
    • 未来的工作包括为增强的神经假肢设备实施刺激协议.