Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Dynamic programming algorithms for comparing multineuronal spike trains via cost-based metrics and alignments.

Jonathan D Victor1, David H Goldberg, Daniel Gardner

  • 1Department of Neurology and Neuroscience, Weill Medical College of Cornell University, 1300 York Avenue, New York City, NY 10021, USA. jdvicto@med.cornell.edu

Journal of Neuroscience Methods
|December 19, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Automated EEG Classification to Track Levels of Consciousness.

medRxiv : the preprint server for health sciences·2026
Same author

Improved autobiographical memory with central thalamic deep brain stimulation in traumatic brain injury.

Brain communications·2026
Same author

Neurophysiological correlates of delayed recovery of consciousness in a critically ill patient with COVID-19 with repeated cardiac arrest.

British journal of anaesthesia·2026
Same author

Walks with jumps: a neurobiologically motivated class of paths in the hyperbolic plane.

Journal of mathematical biology·2026
Same author

Imagery modulates the pupillary response, but this does not reliably index differences in imagery vividness.

Cortex; a journal devoted to the study of the nervous system and behavior·2026
Same author

Residual Foveal Motion Facilitates Processing of Visually Tracked Objects.

bioRxiv : the preprint server for biology·2025

Cost-based metrics measure dissimilarities between neural spike trains for analyzing neural coding and variability. This study enhances an efficient algorithm for multineuronal responses, enabling broader testing of neural coding hypotheses.

Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Cost-based metrics quantify dissimilarity between spike trains.
  • These metrics are vital for understanding neural variability and neural coding.
  • Existing algorithms efficiently analyze single- and multineuronal responses.

Purpose of the Study:

  • To generalize an efficient algorithm for cost-based dissimilarity metrics in multineuronal responses.
  • To identify computational efficiencies for parallel computation of these metrics.
  • To facilitate the testing of diverse neural coding hypotheses.

Main Methods:

  • Analysis of an existing efficient algorithm for metric-space analysis of neuronal responses.
  • Development of criteria for generalizing the algorithm.
  • Identification of parallel processing efficiencies.

Related Experiment Videos

Main Results:

  • A generalized algorithm for cost-based dissimilarity metrics in multineuronal systems.
  • New efficiencies applicable to parallel computation of dissimilarity measures.
  • Enhanced capability for testing neural coding hypotheses.

Conclusions:

  • The generalized algorithm expands the applicability of cost-based metrics for neural data analysis.
  • Computational efficiencies improve the scalability of analyzing multineuronal responses.
  • This work provides a powerful tool for investigating neural coding mechanisms.