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Electrophysiological techniques have improved substantially over the pastyears to the point that neuroprosthetics applications are becoming viable. Thisevolution has been fuelled by the advancement of implantable microelectrodetechnologies that have followed their own version of Moore's scaling law.Similarly to electronics, however, excessive data-rates and strained powerbudgets require the development of more efficient computation paradigms forhandling neural data in-situ, in particular the computationally heavy task ofevents classification. Here, we demonstrate how the intrinsic analogueprogrammability of memristive devices can be exploited to performspike-sorting. We then show how combining memristors with standard logicenables efficient in-silico template matching. Leveraging the physicalproperties of nanoscale memristors allows us to implement ultra-compactanalogue circuits for neural signal processing at the power cost of digital.

Spike sorting using non-volatile metal-oxide memristors