title: An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes.
abstract: For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called "Deconfusion" which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes.
authors.affiliation: Bernstein Center for Computational Neuroscience, Berlin, 10099, Germany. ff@cs.tu-berlin.de.
authors.forename: Felix
authors.initials: F
authors.lastname: Franke
authors.affiliation: Institute for Software Engineering and Theoretical Computer Science, Berlin Institute of Technology, Berlin, 10587, Germany.
authors.forename: Michal
authors.initials: M
authors.lastname: Natora
authors.affiliation: Bernstein Center for Computational Neuroscience, Freiburg, 79104, Germany.
authors.forename: Clemens
authors.initials: C
authors.lastname: Boucsein
authors.affiliation: Max Planck Institute for Biological Cybernetics, Tübingen, 72076, Germany.
authors.forename: Matthias H J
authors.initials: MHJ
authors.lastname: Munk
authors.affiliation: Bernstein Center for Computational Neuroscience, Berlin, 10099, Germany.
authors.forename: Klaus
authors.initials: K
authors.lastname: Obermayer
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000220:Adaptation, Biological; D000465:Algorithms; D000818:Animals; D000831:Animals, Newborn; D016208:Databases, Factual / Q000706:statistics & numerical data; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D009622:Noise; D009862:Online Systems; D012372:ROC Curve; D051381:Rats; D020318:Rats, Long-Evans; D012815:Signal Processing, Computer-Assisted