Fuzzy logic-based spike sorting system

We present a new method for autonomous real-time spike sorting using a fuzzy logic inference engine. The engine assigns each detected event a ‘spikiness index’ from zero to one that quantifies the extent to which the detected event is like an ideal spike. Spikes can then be sorted by simply clustering the spikiness indices. The sorter is defined in terms of natural language rules that, once defined, are static and thus require no user intervention or calibration. The sorter was tested using extracellular recordings from three animals: a macaque, an owl monkey and a rat. Simulation results show that the fuzzy sorter performed equal to or better than the benchmark principal component analysis (PCA) based sorter. Importantly, there was no degradation in fuzzy sorter performance when the spikes were not temporally aligned prior to sorting. In contrast, PCA sorter performance dropped by 27% when sorting unaligned spikes. Since the fuzzy sorter is computationally trivial and requires no spike alignment, it is suitable for scaling into large numbers of parallel channels where computational overhead and the need for operator intervention would preclude other spike sorters.

► An autonomous real-time spike sorting system based on Fuzzy Logic technique.
► Fuzzy inferences use five extracted features to characterize a spike and a single coefficient is assigned.
► The fuzzy spike sorter is shift-independence and immune to both temporal and alignment noises.
► Definition of the fuzzy rules are constant across multiple channels and does not vary with time.
► Algorithm is computationally trivial and is easily hardware realizable.