Electrophysiological recordings of neural activity in a mouse's brain are very popular among neuroscientists for understanding brain function. One particular area of interest is acquiring recordings from the Purkinje cells in the cerebellum in order to understand brain injuries and the loss of motor functions. However, current setups for such experiments do not allow the mouse to move freely and, thus, do not capture its natural behaviour since they have a wired connection between the animal's head stage and an acquisition device. In this work, we propose a lightweight neuronal-spike detection and classification architecture that leverages on the unique characteristics of the Purkinje cells to discard unneeded information from the sparse neural data in real time. This allows the (condensed) data to be easily stored on a removable storage device on the head stage, alleviating the need for wires. Our proposed implementation shows a >95% overall classification accuracy while still resulting in a small-form-factor design, which allows for the free movement of mice during experiments. Moreover, the power-efficient nature of the design and the usage of STT-RAM (Spin Transfer Torque Magnetic Random Access Memory) as the removable storage allows the head stage to easily operate on a tiny battery for up to approximately 4 days.

翻译：小鼠大脑神经活动的电生理记录在神经科学家中非常流行，用于理解脑功能。其中一个特别感兴趣的研究领域是获取小脑浦肯野细胞的记录，以理解脑损伤和运动功能丧失。然而，当前此类实验的装置不允许小鼠自由移动，因此由于动物头部级和采集设备之间存在有线连接，无法捕捉其自然行为。在本文中，我们提出了一种轻量级的神经元尖峰检测与分类架构，该架构利用浦肯野细胞的独特特性，实时丢弃稀疏神经数据中的不必要信息。这使得（压缩后的）数据能够轻松存储于头部级的可移动存储设备上，从而消除了对导线的需求。我们提出的实现方案在保持小尺寸设计的同时，实现了超过95%的整体分类准确率，这使得小鼠在实验期间能够自由移动。此外，该设计的节能特性以及采用STT-RAM（自旋转移矩磁随机存储器）作为可移动存储介质，使得头部级仅需一枚微型电池即可轻松运行长达约4天。