CaltechAUTHORS
  A Caltech Library Service

Heuristic Spike Sorting Tuner (HSST), a framework to determine optimal parameter selection for a generic spike sorting algorithm

Bjånes, David A. and Fisher, Lee E. and Gaunt, Robert A. and Weber, Douglas J. (2020) Heuristic Spike Sorting Tuner (HSST), a framework to determine optimal parameter selection for a generic spike sorting algorithm. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200526-093718629

[img] PDF - Submitted Version
See Usage Policy.

4Mb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20200526-093718629

Abstract

Extracellular microelectrodes frequently record neural activity from more than one neuron in the vicinity of the electrode. The process of labeling each recorded spike waveform with the identity of its source neuron is called spike sorting and is often approached from an abstracted statistical perspective. However, these approaches do not consider neurophysiological realities and may ignore important features that could improve the accuracy of these methods. Further, standard algorithms typically require selection of at least one free parameter, which can have significant effects on the quality of the output. We describe a Heuristic Spike Sorting Tuner (HSST) that determines the optimal choice of the free parameters for a given spike sorting algorithm based on the neurophysiological qualification of unit isolation and signal discrimination. A set of heuristic metrics are used to score the output of a spike sorting algorithm over a range of free parameters resulting in optimal sorting quality. We demonstrate that these metrics can be used to tune parameters in several spike sorting algorithms. The HSST algorithm shows robustness to variations in signal to noise ratio, number and relative size of units per channel. Moreover, the HSST algorithm is computationally efficient, operates unsupervised, and is parallelizable for batch processing.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/2020.05.21.108902DOIDiscussion Paper
ORCID:
AuthorORCID
Bjånes, David A.0000-0002-1208-5916
Fisher, Lee E.0000-0002-9072-3119
Gaunt, Robert A.0000-0001-6202-5818
Weber, Douglas J.0000-0002-9782-3497
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Posted May 25, 2020. This work was funded by NIH Grant R01NS-72343 and DARPA cooperative agreement N66001-11-C-4171. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency (DARPA) and SPAWAR System Center Pacific (SSC Pacific). No conflicts of interest, financial or otherwise, are declared by the authors. Author Contributions: D.B. analyzed data and designed algorithm and metrics; D.B., L.F., R.G., and D.W. interpreted results of experiments; D.B. prepared figures and drafted manuscript; D.B., L.F., R.G., and D.W. edited and revised manuscript; D.B., R.G. and D.W. approved final version of manuscript.
Funders:
Funding AgencyGrant Number
NIHR01NS-72343
Defense Advanced Research Projects Agency (DARPA)N66001-11-C-4171
Record Number:CaltechAUTHORS:20200526-093718629
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200526-093718629
Official Citation:Heuristic Spike Sorting Tuner (HSST), a framework to determine optimal parameter selection for a generic spike sorting algorithm. David Bjanes, Lee E Fisher, Robert Gaunt, Douglas Weber. bioRxiv 2020.05.21.108902; doi: https://doi.org/10.1101/2020.05.21.108902
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:103444
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:26 May 2020 17:13
Last Modified:26 May 2020 17:13

Repository Staff Only: item control page