  Electrophysiological techniques have improved substantially over the past
years to the point that neuroprosthetics applications are becoming viable. This
evolution has been fuelled by the advancement of implantable microelectrode
technologies that have followed their own version of Moore's scaling law.
Similarly to electronics, however, excessive data-rates and strained power
budgets require the development of more efficient computation paradigms for
handling neural data in-situ, in particular the computationally heavy task of
events classification. Here, we demonstrate how the intrinsic analogue
programmability of memristive devices can be exploited to perform
spike-sorting. We then show how combining memristors with standard logic
enables efficient in-silico template matching. Leveraging the physical
properties of nanoscale memristors allows us to implement ultra-compact
analogue circuits for neural signal processing at the power cost of digital.
