Towards a memristor-based spike-sorting platform

We present a new approach for performing spike-sorting through a memristor-based, neural-signal processing platform. We have previously shown that the inherent threshold property of the memristor allows spike-detection through nonvolatile resistive state transition. Here, a test memristive device is subjected to a neural recording and the periodically recorded resistive state changes are mapped to the amplitude of the spiking events. It is found that the resistive state changes can be differentiated into clusters, where each cluster can be mapped to a range of spiking events in the input neural waveform, thus indicating the address of source neuron.