  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.
