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

Introduction to Normal Distributions01:29

Introduction to Normal Distributions

Standardized test scores often follow a symmetric distribution that can be modeled with the normal distribution, a fundamental concept in statistics. This distribution is particularly useful for interpreting test performance fairly across populations, as it provides a mathematical framework for understanding variability and central tendency in large datasets.From Histogram to Frequency DistributionRaw test data are often displayed using histograms, where the height of each bar represents the...
Binomial Probability Distribution01:15

Binomial Probability Distribution

A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...

You might also read

Related Articles

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

Sort by
Same author

The ASSIST CLAD study: A phase 2 randomized controlled trial of mesenchymal stromal cells for new-onset chronic lung allograft dysfunction.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation·2026
Same author

Post-caesarean analgesia: a multicentre retrospective analysis comparing practices in Queensland, Australia (2019-2022).

International journal of obstetric anesthesia·2025
Same author

Placement, management and complications associated with peripheral intravenous catheter use in UK small animal practice.

The Journal of small animal practice·2024
Same author

Small beams, fast predictions: a comparison of machine learning dose prediction models for proton minibeam therapy.

Medical physics·2022
Same author

The Phenotypic Spectrum of New-onset IBD in Canadian Children of South Asian Ethnicity: A Prospective Multi-Centre Comparative Study.

Journal of Crohn's & colitis·2021
Same author

FIR and IIR Synapses, a New Neural Network Architecture for Time Series Modeling.

Neural computation·2019

Related Experiment Video

Updated: Jul 7, 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

On the distribution of performance from multiple neural-network trials.

S Lawrence1, A D Back, A C Tsoi

  • 1NEC Res. Inst., Princeton, NJ.

IEEE Transactions on Neural Networks
|January 1, 1997
PubMed
Summary

Neural network simulation performance often deviates from Gaussian distributions. New reporting guidelines are proposed to better reflect actual result distributions for improved interpretation.

More Related Videos

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
08:28

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

Related Experiment Videos

Last Updated: Jul 7, 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

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
08:28

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

Area of Science:

  • Computational neuroscience
  • Machine learning performance analysis

Background:

  • Neural network simulation performance is typically reported using mean and standard deviation.
  • This statistical approach assumes a Gaussian distribution, which is often not accurate for simulation results.

Purpose of the Study:

  • To investigate the distribution of neural network simulation results for practical problems.
  • To demonstrate how assuming Gaussian distributions can distort the interpretation of results, particularly in comparative studies.
  • To propose improved guidelines for reporting simulation performance.

Main Methods:

  • Analysis of result distributions from neural network simulations on practical tasks.
  • Comparison of results assuming Gaussian distributions versus actual observed distributions.
  • Controlled task evaluation to assess performance skew based on target function complexity.

Main Results:

  • Observed distributions of simulation results are frequently non-Gaussian, asymmetric, or multimodal.
  • Assuming Gaussian distributions can significantly impact the interpretation of comparative study outcomes.
  • Performance distributions exhibit skewness: towards better performance for smoother functions and worse for complex functions.

Conclusions:

  • Standard reporting of neural network simulation performance using mean and standard deviation can be misleading.
  • The characteristics of the target function influence the skewness of performance distributions.
  • Adopting new reporting guidelines that detail the actual distribution is crucial for accurate interpretation and comparison of neural network performance.