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

Random Error01:04

Random Error

9.9K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
9.9K
Entropy02:39

Entropy

36.7K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
36.7K
Second Law of Thermodynamics02:49

Second Law of Thermodynamics

27.2K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic models, the...
27.2K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

577
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
577
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

981
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
981
Entropy within the Cell01:22

Entropy within the Cell

13.3K
A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
13.3K

You might also read

Related Articles

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

Sort by
Same author

On two diffusion neuronal models with multiplicative noise: The mean first-passage time properties.

Chaos (Woodbury, N.Y.)·2020
Same author

Effect of rooibos tea ( Aspalathus linearis ) on Japanese quail growth, egg production and plasma metabolites.

British poultry science·2017
Same author

Parametric inference of neuronal response latency in presence of a background signal.

Bio Systems·2013
Same author

Randomness of spontaneous activity and information transfer in neurons.

Physiological research·2008
Same author

Effect of rooibos tea (Aspalathus linearis) on Japanese quail growth, egg production and plasma metabolites.

British poultry science·2008
Same author

Modeling the influence of non-adherence on antibiotic efficacy: application to ciprofloxacin.

International journal of clinical pharmacology and therapeutics·2007
Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

New results on prescribed-time synchronization of complex networks via intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Variance-constrained multi-view ensemble broad network for imbalanced data.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Dynamic analysis and reliable mechanical optimization application of ring HNN effected with a memristive neuron.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

DAFF-Net: A detection and search method for small-scale low surface brightness galaxies.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Quasi-synchronization for complex networks with hybrid pinning intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Feb 23, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.6K

Entropy factor for randomness quantification in neuronal data.

K Rajdl1, P Lansky1, L Kostal1

  • 1Institute of Physiology, Academy of Sciences of the Czech Republic, Videnska 1083, 142 20 Prague 4, Czech Republic.

Neural Networks : the Official Journal of the International Neural Network Society
|September 10, 2017
PubMed
Summary
This summary is machine-generated.

A new entropy factor measures neural spike train randomness, differing from the Fano factor. This analysis reveals increased spike count variability can paradoxically enhance predictability in neural activity.

Keywords:
Fano factorRenewal processShannon entropyVariability measure

More Related Videos

The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy
07:55

The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy

Published on: July 9, 2017

12.1K
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

34.4K

Related Experiment Videos

Last Updated: Feb 23, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.6K
The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy
07:55

The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy

Published on: July 9, 2017

12.1K
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

34.4K

Area of Science:

  • Computational neuroscience
  • Information theory
  • Neural coding

Background:

  • Neural spike train analysis often uses measures like the Fano factor to quantify randomness.
  • Understanding neural variability is crucial for deciphering information processing in the brain.

Purpose of the Study:

  • To introduce a novel measure, the entropy factor, for neural spike train randomness.
  • To compare the entropy factor with the Fano factor using theoretical models and experimental data.

Main Methods:

  • The entropy factor is derived from Shannon entropy applied to spike counts within time windows.
  • Theoretical analysis involved equilibrium renewal processes and specific interspike interval distributions (gamma, inverse Gaussian).
  • Experimental evaluation used spontaneous activity recordings from macaque primary visual cortex.

Main Results:

  • The entropy factor exhibits distinct theoretical properties compared to the Fano factor.
  • Analysis of macaque visual cortex data showed significant differences between the two factors.
  • A paradoxical finding: increased spike count variability correlated with increased predictability, as indicated by the entropy factor.

Conclusions:

  • The entropy factor offers a new perspective on neural spike train variability and predictability.
  • The study highlights limitations of the Fano factor and introduces a potentially more informative measure.
  • Neural coding may involve complex relationships between variability and predictability not captured by traditional methods.