title: A Simple Method to Simultaneously Detect and Identify Spikes from Raw Extracellular Recordings.
abstract: The ability to track when and which neurons fire in the vicinity of an electrode, in an efficient and reliable manner can revolutionize the neuroscience field. The current bottleneck lies in spike sorting algorithms; existing methods for detecting and discriminating the activity of multiple neurons rely on inefficient, multi-step processing of extracellular recordings. In this work, we show that a single-step processing of raw (unfiltered) extracellular signals is sufficient for both the detection and identification of active neurons, thus greatly simplifying and optimizing the spike sorting approach. The efficiency and reliability of our method is demonstrated in both real and simulated data.
authors.affiliation: Computational Biology Laboratory, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas Heraklion, Greece.
authors.forename: Panagiotis C
authors.initials: PC
authors.lastname: Petrantonakis
authors.affiliation: Computational Biology Laboratory, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas Heraklion, Greece.
authors.forename: Panayiota
authors.initials: P
authors.lastname: Poirazi
keywords: extracellular signals; multi-electrode recordings; spike sorting