基于霍普夫分岔器件的抗噪神经元锋电位检测
Noise-Resilient Detection of Neuronal Spikes by a Hopf-Bifurcation Device
- Instituto de Tecnología Química (ITQ), Consejo Superior de Investigaciones Científicas- Universitat Politècnica de València（化工技术研究所，西班牙国家研究委员会-巴伦西亚理工大学）
- Dept. Biomedical, Metabolic and Neural Sciences, Univ. of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学生物医学、代谢与神经科学系）
- International School for Advanced Studies (SISSA)（高级研究国际学院）
- National Interuniversity Consortium of Materials Science and Technology (INSTM)（国家大学材料科学与技术联盟）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究基于霍普夫分岔的半导体NDR器件，实现了低信噪比下的异步信号检测，可用于神经元锋电位检测，为神经假体提供了抗噪信号处理的硬件方案。
AI中文摘要：
在传感、通信和电生理学领域，检测噪声中的微弱瞬态信号是一项长期存在的挑战。本文展示了一种基于半导体负微分电阻（NDR）器件的物理弱信号检测器，该器件在霍普夫分岔附近工作。持续时间超过该动力学系统响应时间的相干输入可驱动系统在静止态和振荡态之间转变，而更快的随机波动则被大幅抑制。这种非线性变换将微弱的模拟阈值穿越转化为全有或全无的电压锋电位，从而提供无需参考时钟的异步信号检测。利用调制光伏信号，我们演示了在输入信号噪声振幅比低至1/500时，成功检测到100 Hz的微弱频率分量，并在输出频谱中可靠地恢复该分量。随后，我们将相同原理应用于神经元多细胞胞外记录。经过标准带通滤波后，原始微电极信号经NDR动力学变换，增强了神经元锋电位与背景波动的区分度。由此得到的检测锋电位时间与传统锋电位检测流程获得的时间高度吻合。这些结果表明，经分岔工程设计的NDR动力学可作为一种紧凑的硬件方法，用于抗噪信号区分和基于事件的模数转换，将对神经假体装置具有实用价值。
英文摘要：
Detecting weak transient signals in noise is a persistent challenge in sensing, communication, and electrophysiology. Here, we demonstrate a physical weak-signal detector based on a semiconductor negative differential resistance (NDR) device operated near a Hopf bifurcation. A coherent input that persists over the response time of the dynamical system can drive a transition between quiescent and oscillatory states, whereas faster stochastic fluctuations are largely suppressed. This nonlinear transformation converts a weak analog threshold crossing into all-or-none voltage spikes and therefore provides asynchronous signal detection without a reference clock. Using a modulated photovoltaic signal, we detect a weak frequency component of 100 Hz as a demonstration, at an input signal-to-noise amplitude ratio as low as 1/500, and reliably recover it in the output spectrum. We then apply the same principle to neuronal multisite extracellular recordings. After standard band-pass filtering, the raw microelectrode signal is transformed by the NDR dynamics, enhancing the distinction between neuronal spikes and background fluctuations. The resulting detected spike times agree closely with those obtained using a traditional spike-detection pipeline. These results establish bifurcation-engineered NDR dynamics as a compact hardware approach to noise-resilient signal discrimination and event-based analog-to-digital conversion that will be useful for neuroprosthetic devices.
