Fuzzy Logic-Based Spike Sorting

We've just received word that a manuscript authored by my graduate student Karthikeyan Balasubramanian will be published in the Journal of Neuroscience Methods. The paper, titled, "Fuzzy Logic-based Spike Sorting System" looks at how fuzzy logic can be used as an autonomous feature extraction algorithm for spike sorting. Its a pretty neat concept: spike features are measured and fuzzified, and then fuzzy logic is used to calculate a "fuzziness" index for each spike that identifies how similar that spike is to an ideal spike waveform. The fuzziness indicies can be clustered directly for a complete spike sorting solution. There are several advantages of our system. The first is that the fuzzy rules don't ever need to be modified, meaning that the system doesn't need any channel-by-channel calibration every day. Secondly, the sorter does not require that spikes be spatially aligned, as with principal component analysis. Spike alignment is computationally expensive. Finally, our system is computationally negligible to implement and can be built in an FPGA with hundreds of channels in parallel for a nice clean low-power solution.