Numerous neurochemicals have been implicated in the modulation of brain function, making them appealing analytes for sensors and diagnostics. However, it is a grand challenge to selectively measure multiple neurochemicals simultaneously in vivo because of their great variations in concentrations, dynamic nature, and composition. Herein, we present a deep learning-based voltammetric sensing platform for the highly selective and simultaneous analysis of three neurochemicals in a living animal brain. The system features a carbon fiber electrode capable of capturing the mixed dynamics of a neurotransmitter, neuromodulator, and ions. Then a powerful deep neural network is employed to resolve individual chemical and spatial-temporal information. With this, a single electrochemical measurement reveals an interplaying concentration changes of dopamine, ascorbate, and ions in living rat brain, which is unobtainable with existing analytical methodologies. Our strategy provides a powerful means to expedite research in neuroscience and empower sensing-aided diagnostic applications.

中文翻译：

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活体动物大脑中伏安传感的深度学习

许多神经化学物质与大脑功能的调节有关，使它们成为传感器和诊断的有吸引力的分析物。然而，在体内同时选择性地测量多种神经化学物质是一项巨大的挑战，因为它们在浓度、动态性质和组成方面的巨大变化。在此，我们提出了一种基于深度学习的伏安传感平台，用于对活体动物大脑中的三种神经化学物质进行高度选择性和同步分析。该系统采用碳纤维电极，能够捕捉神经递质、神经调节剂和离子的混合动力学。然后采用强大的深度神经网络来解析个体化学和时空信息。有了这个，单一的电化学测量揭示了多巴胺的相互作用浓度变化，活大鼠大脑中的抗坏血酸和离子，这是现有分析方法无法获得的。我们的战略提供了一种强有力的手段来加快神经科学的研究并增强传感辅助诊断应用的能力。