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 Concept Videos

Probability Histograms01:17

Probability Histograms

8.7K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
8.7K

You might also read

Related Articles

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

Sort by
Same author

Working Memory of Multi-Object Scenes in Primate Frontal Cortex.

bioRxiv : the preprint server for biology·2026
Same author

Computational basis of hierarchical and counterfactual information processing.

Nature human behaviour·2025
Same author

MEDiCINe: Motion Correction for Neural Electrophysiology Recordings.

eNeuro·2025
Same author

The Text4HealthyAging Program: An Evidence-Based Text Messaging Innovation to Support Healthy Urban Aging in Canada and Australia.

Gerontology & geriatric medicine·2022
Same author

Protocol for a multi-phase, mixed methods study to develop and evaluate culturally adapted CBT to improve community mental health services for Canadians of south Asian origin.

Trials·2021
Same author

The Quality Mental Health Care Network: A roadmap to improving quality mental healthcare in Canada.

Healthcare management forum·2021

Related Experiment Video

Updated: Apr 28, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

11.5K

Neuronal spike train entropy estimation by history clustering.

Nicholas Watters1, George N Reeke

  • 1Harvard College, Cambridge, MA 02138, U.S.A. nwatters01@college.harvard.edu.

Neural Computation
|June 13, 2014
PubMed
Summary

Estimating neural signal entropy is challenging. This study introduces two model-based methods, with a faster option proving accurate and efficient for analyzing neural spike train data, even with limited records.

More Related Videos

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.3K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.9K

Related Experiment Videos

Last Updated: Apr 28, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

11.5K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.3K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.9K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Information Theory

Background:

  • Neurons communicate via action potentials (spikes).
  • Signal information is encoded in spike timing, specifically inter-spike intervals.
  • Accurate estimation of spike train entropy is crucial but difficult, especially with small datasets.

Purpose of the Study:

  • To develop and evaluate novel model-based methods for estimating neural signal entropy.
  • To compare the performance of these new methods against existing entropy estimation techniques.
  • To identify an accurate and efficient method for analyzing neural spike train data.

Main Methods:

  • Developed two related model-based methods for entropy estimation.
  • Compared proposed methods with existing techniques using neural data.
  • Utilized a computationally intensive but accurate method to generate reference entropy values.
  • Assessed convergence and accuracy with varying lengths of spike train records.

Main Results:

  • One proposed method is fast and reasonably accurate, converging well with short spike records.
  • A second, slower method is highly accurate but computationally demanding.
  • The faster method demonstrates closer convergence to the accurate method's estimates, especially with smaller datasets.
  • The novel faster method outperforms many existing entropy estimators in terms of accuracy and data requirements.

Conclusions:

  • The presented fast, model-based method offers a practical solution for estimating neural signal entropy.
  • This method provides reliable entropy estimates from limited neural data, improving upon existing techniques.
  • The findings facilitate more efficient and accurate analysis of information coding in neural systems.