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. Synthesis results reveal 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：电生理记录小鼠大脑中的神经活动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领域是从小脑中的浦肯野细胞获取记录，以理解脑损伤和运动功能丧失。然而，目前此类实验的设置不允许小鼠自由移动，因此无法捕捉其自然行为，因为动物头部级和采集设备之间通过有线连接。在这项工作中，我们提出了一种轻量级的神经元尖峰检测和分类架构，利用浦肯野细胞的独特特征，实时丢弃稀疏神经数据中的不必要信息。这使得（压缩后的）数据能够轻松存储在头部级上的可移动存储设备中，从而消除了对导线的需求。综合结果显示，整体分类准确率超过95%，同时实现了小巧的外形设计，允许实验期间小鼠自由移动。此外，该设计的节能特性以及使用STT-RAM（自旋转移矩磁随机存取存储器）作为可移动存储，使得头部级能够轻松依靠微型电池运行约4天。