Fast, scalable, Bayesian spike identification for multi-electrode arrays

J. Prentice, J. Homann, K. Simmons, G. Tkacik, V. Balasubramanian, P. Nelson, PLoS One 6 (2011).

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Abstract
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.
Publishing Year
Date Published
2011-07-20
Journal Title
PLoS One
Acknowledgement
This work was supported by National Science Foundation (NSF) grants IBN-0344678, EF-0928048, National Institutes of Health (NIH) grant RO1 EY08124, NIH training grant T32-07035, and NIH training grant 5T90DA022763-04. Michael Berry and Olivier Marre have developed an algorithm similar to, but different from, ours (manuscript in preparation). We thank them for discussions of their work, and specifically thank Olivier Marre for suggesting to us that the most complete subtraction of a spike can be obtained by refitting the spike without a prior.
Volume
6
Issue
7
IST-REx-ID

Cite this

Prentice J, Homann J, Simmons K, Tkacik G, Balasubramanian V, Nelson P. Fast, scalable, Bayesian spike identification for multi-electrode arrays. PLoS One. 2011;6(7). doi:10.1371/journal.pone.0019884
Prentice, J., Homann, J., Simmons, K., Tkacik, G., Balasubramanian, V., & Nelson, P. (2011). Fast, scalable, Bayesian spike identification for multi-electrode arrays. PLoS One, 6(7). https://doi.org/10.1371/journal.pone.0019884
Prentice, Jason, Jan Homann, Kristina Simmons, Gasper Tkacik, Vijay Balasubramanian, and Philip Nelson. “Fast, Scalable, Bayesian Spike Identification for Multi-Electrode Arrays.” PLoS One 6, no. 7 (2011). https://doi.org/10.1371/journal.pone.0019884.
J. Prentice, J. Homann, K. Simmons, G. Tkacik, V. Balasubramanian, and P. Nelson, “Fast, scalable, Bayesian spike identification for multi-electrode arrays,” PLoS One, vol. 6, no. 7, 2011.
Prentice J, Homann J, Simmons K, Tkacik G, Balasubramanian V, Nelson P. 2011. Fast, scalable, Bayesian spike identification for multi-electrode arrays. PLoS One. 6(7).
Prentice, Jason, et al. “Fast, Scalable, Bayesian Spike Identification for Multi-Electrode Arrays.” PLoS One, vol. 6, no. 7, Public Library of Science, 2011, doi:10.1371/journal.pone.0019884.
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