Spike train SIMilarity Space (SSIMS): A framework for single neuron and ensemble data analysis




We present a method to evaluate the relative similarity of neural spiking patterns by combining spike train distance metrics with dimensionality reduction. Spike train distance metrics provide an estimate of similarity between activity patterns at multiple temporal resolutions. Vectors of pair-wise distances are used to represent the intrinsic relationships between multiple activity patterns at the level of single units or neuronal ensembles. Dimensionality reduction is then used to project the data into concise representations suitable for clustering analysis as well as exploratory visualization. Algorithm performance and robustness are evaluated using multielectrode ensemble activity data recorded in behaving primates. We demonstrate how Spike train SIMilarity Space (SSIMS) analysis captures the relationship between goal directions for an 8-directional reaching task and successfully segregates grasp types in a 3D grasping task in the absence of kinematic information. The algorithm enables exploration of virtually any type of neural spiking (time series) data, providing similarity-based clustering of neural activity states with minimal assumptions about potential information encoding models.

Author(s): Carlos E. Vargas-Irwin and David M. Brandman and Jonas B. Zimmermann and John P. Donoghue and Michael J. Black
Journal: Neural Computation
Volume: 27
Number (issue): 1
Pages: 1--31
Year: 2015
Month: January
Publisher: MIT Press

Department(s): Perceiving Systems
Research Project(s): Neural Prosthetics and Decoding
Bibtex Type: Article (article)
Paper Type: Journal

DOI: doi:10.1162/NECO_a_00684

Links: pdf: publisher site
pdf: author's proof


  title = {{Spike train SIMilarity Space} ({SSIMS}): A framework for single neuron and ensemble data analysis},
  author = {Vargas-Irwin, Carlos E. and Brandman, David M. and Zimmermann, Jonas B. and Donoghue, John P. and Black, Michael J.},
  journal = {Neural Computation},
  volume = {27},
  number = {1},
  pages = {1--31},
  publisher = {MIT Press},
  month = jan,
  year = {2015},
  month_numeric = {1}