Fast, Scalable, Bayesian Spike Identification for Multi-Electrode Arrays

• Published in: PloS one Vol. 6; no. 7; p. e19884
• Main Authors: Prentice, Jason S., Homann, Jan, Simmons, Kristina D., Tkačik, Gašper, Balasubramanian, Vijay, Nelson, Philip C.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.07.2011; Public Library of Science (PLoS)
• Subjects: Albinism; Algorithms; Animals; Astronomy; Background noise; Bayes Theorem; Bayesian analysis; Biology; Biophysics; Classification; Cluster Analysis; Clustering; Computer applications; Computer Graphics; Computer Science; Computer simulation; Data analysis; Datasets; Electrodes; Engineering; Firing pattern; Guinea Pigs; Identification methods; Mathematics; Methods; Morphology; Neurons; Neurosciences; Noise; Pattern Recognition, Automated - methods; Physics; Population; Quantitative analysis; Receptive field; Retina; Retinal ganglion cells; Retinal Ganglion Cells - cytology; Spikes; Time Factors; United States > US; Pennsylvania
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0019884

We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human interaction plays a key role in our method; but effort is minimized and streamlined via a graphical interface. We illustrate our method on data from guinea pig retinal ganglion cells and document its performance on simulated data consisting of spikes added to experimentally measured background noise. We present several tests demonstrating that the algorithm is highly accurate: it exhibits low error rates on fits to synthetic data, low refractory violation rates, good receptive field coverage, and consistency across users.