发表机构
University of Cambridge; Koç University（剑桥大学; 科奇大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对分子神经接口在自身化学输入下估计内源性释放的问题，提出基线信息需求分析，证明递送与基线信息影响释放分类，并量化了基线参考对误差的显著降低作用。
AI 中文摘要
同时递送和感知同一种神经递质的分子神经接口，必须在其自身化学输入的背景下估计内源性释放。我们研究了两个共享饱和摄取和浓度变化测量的交换区域。已知的递送和交换能够识别驱动区域的表现摄取，但未知的静息浓度差异使得源区域的增量摄取存在不确定性。我们刻画了与理想记录一致的所有可接受源历史，并展示了递送和基线信息如何影响释放分类。一个多巴胺启发的示例产生了平均释放速率变化为-8.99和+1.31纳摩尔每秒的相同理想浓度变化记录。更强的递送改变了所有兼容源是否意味着抑制，而真实源保持不变。在高斯观测模型下，我们基于化学波动界定了判别能力。一个假设标准差为3.3纳摩尔的基线参考，在精确增益校准下，与错误的固定基线相比，将均方根误差从10.41降低到1.21纳摩尔每秒。在弱激发、增益误差或响应接近类别边界时，分类错误仍然很大。
英文摘要
Molecular neural interfaces that deliver and sense the same neurotransmitter must estimate endogenous release in the presence of their own chemical input. We study two exchanging regions with shared saturable uptake and concentration change measurements. Known delivery and exchange permit identification of apparent uptake at the driven region, but an unknown resting concentration difference leaves the source region's incremental uptake uncertain. We characterize all admissible source histories consistent with both ideal records and show how delivery and baseline information affect release classification. A dopamine-inspired example produces identical ideal concentration-change records for mean release-rate changes of -8.99 and +1.31 nanomolar per second. Stronger delivery changes whether all compatible sources imply suppression, with the true source held fixed. Under a Gaussian observation model, we bound discrimination based on chemical fluctuations. A baseline reference with an assumed standard deviation of 3.3 nanomolar reduces root-mean-square error from 10.41 to 1.21 nanomolar per second compared with an incorrect fixed baseline under exact gain calibration. Classification errors remain substantial with weak excitation, gain errors or responses near the category boundaries.
Comments6 pages, 3 figures, submitted to the 2027 IEEE International Conference on Communications (ICC 2027)
