Toward Automated Spike Sorting via Ground Truth Neural Recordings

PROJECTSUMMARY Scaling extracellular electrophysiology to higher channel counts is hindered by the burden of data handling storageand especially preprocessinge gspike sortingThe burden of spike sorting can in principle be reduced through a combination of high density multielectrode arrayprobetechnology and algorithm optimization to yield a spike sorting method that is both highly accurate and fully automatedWith a known good spike sorting method in handthe algorithm can be baked into the data stream as early as possible to allow for automatic data sorting and a massive reduction in data rate to downstream storage and processingHoweverit takes an investment of considerable resources to implement this sort of large scale real time processingand great confidence to throw away raw data and keeponlyprocesseddataAccuracy and automation of spike sorting increases with the spatial density of recording sitesNeural activity recorded from high density probes can serve as a data corpus for testing the accuracy of spike sorting algorithmsHoweverto quantify spike sorting performance for comparison between algorithmsthe ground truth spiking activity of neurons captured in the data corpus must be measuredsuch as by simultaneously recording via patch clamp pipette or some other recording modalityUnfortunatelybecause ground truth recordings are so challenging to performthey remain too rare to allow for this sort of analysis in a large scalemeaningful wayUntil this need is metspike sorting development lacks a compassand cutting edge techniques such as supervised machine learning which require large amounts of labelled data remain out of reachAccordinglywe propose a series of multimodal neural recordings combining multielectrode array and patch pipette techniques to generateacorpusofgroundtruthdataforvalidationofspikesortingalgorithms PROJECTNARRATIVE Electrophysiologicalrecordingsystemsallowdirectobservationofneuralactivityinanimal subjectsThisfacilitatesthestudyofcrucialneuroscientifictopicssuchasdevelopmentlearningandmemoryandcognitionaswellasbraindiseasessuchasAlzheimer sepilepsyParkinson sanddepressionLeafLabstoolsforcharacterizingandanalyzinghigh channel countelectrophysiologyrecordingswillallowresearcherstomoreeasilycollectandinterpret neuraldataatalargescale