AI 中文总结
该研究设计基于Hindmarsh-Rose模型的实时闭环协议，通过计算Victor-Purpura距离适应神经变异性，在两项验证实验中有效驱动神经动力学至期望状态，为神经编码研究提供了更优实验方法。
AI 中文摘要
理解神经系统中的时间编码对于解码脑通信及推进神经信息处理知识至关重要。神经活动常通过具有与特定功能关联的典型时间结构的锋电位序列传递信息，但这些序列会受到神经动力学引入的变异性影响。实时闭环刺激是通过自适应控制研究时间编码的有力方法。本研究评估闭环协议如何适应这种变异性，以驱动神经动力学向期望状态发展：该协议计算Victor-Purpura距离，量化神经系统产生的锋电位序列与触发模式的相似性；若判定神经序列与触发模式相似，则对系统施加刺激，这既便于分析系统响应是否一致，也有助于识别可视为同一功能时间编码实例的不同锋电位序列。我们采用Hindmarsh-Rose模型设计两项验证实验：（i）检测时间编码并施加刺激以产生短暂的穿插爆发；（ii）检测混沌活动中的爆发起始，随后施加抑制性刺激使其规整化。我们逐步注入高斯噪声以增加变异性，结果显示该协议对变异性具有高度适应性，且能有效达成目标动力学。本论文结果表明，自适应闭环刺激可增强在实际变异性条件下研究神经编码的实验方法。
英文摘要
Understanding temporal coding in neural systems is essential for decoding brain communication and advancing knowledge of neural information processing. Neural activity often conveys information through spike sequences with stereotypical temporal structures linked to specific functions. However, these sequences are subject to variability introduced by neural dynamics. Real-time closed-loop stimulation is a powerful approach to study temporal coding through adaptive control. In this work, we evaluate how a closed-loop protocol adapts to this variability to drive neural dynamics toward a desired state. It computes the Victor-Purpura distance to quantify similarity between spike sequences generated by the neural system and a triggering pattern. If the protocol determines that a neural sequence is similar to the trigger pattern, it applies stimulation to the system. This allows for an analysis of whether the system's responses are consistent and facilitates the identification of varying spike sequences that can be considered instances of the same functional temporal code. We designed two validation experiments using the Hindmarsh-Rose model: (i) detection of a temporal code and delivery of stimulation to produce brief interspersed bursts, and (ii) detection of burst onset in chaotic activity followed by inhibitory stimulation to regularize it. Gaussian noise was progressively injected to increase variability. The protocol exhibited high degree of adaptability to variability and was effective in achieving the target dynamics. The results reported in this paper suggest that adaptive closed-loop stimulation can enhance experimental methodologies for studying neural coding under realistic variability conditions.
