Jove
Visualize
Contact Us

Related Concept Videos

Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

11.0K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
11.0K
Fundamental Attribution Error01:14

Fundamental Attribution Error

13.8K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
13.8K
Random Error01:04

Random Error

9.8K
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...
9.8K
Margin of Error01:27

Margin of Error

7.6K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
7.6K
Standard Error of the Mean01:13

Standard Error of the Mean

12.4K
The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
The standard error of the mean is an example of a standard error. It is a unique standard deviation known as the standard deviation of the sampling distribution of the mean. The standard error of the mean is a statistic that calculates how correctly a sample distribution represents a...
12.4K
Contaminants and Errors01:16

Contaminants and Errors

376
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
376

You might also read

Related Articles

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

Sort by
Same author

Multiplexed Crossbar GFET Array With BioADC for Multi-Modal Aptamer-Based Sensing.

IEEE transactions on biomedical circuits and systems·2026
Same author

Estimation of energy-dissipation lower bounds for neuromorphic learning in memory.

Physical review. E·2026
Same author

A 1024-Channel Hybrid Voltage/Current-Clamp Neural Interface System-on-Chip With Dynamic Incremental SAR Acquisition.

IEEE transactions on biomedical circuits and systems·2026
Same author

Wearable technologies for assisted mobility in the real world.

Nature communications·2025
Same author

Brain-Body Coupling in Listening to Metronomic Sounds and Music.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Graph Representations for Reading Comprehension Analysis using Large Language Model and Eye-Tracking biomarker.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

CEST MRI reveals nicotine-induced alterations in glutamate-associated molecular connectivity in the mouse brain.

Frontiers in neuroscience·2026
Same journal

Brain protein burden is related to intravoxel incoherent motion: PET-MR imaging study.

Frontiers in neuroscience·2026
Same journal

Screening the optimal rTSMS frequency to orchestrate immune-fibrotic remodeling for adult spinal cord repair.

Frontiers in neuroscience·2026
Same journal

Assessment of tenecteplase target-associated pathogenic mechanisms underlying depression in acute ischemic stroke patients: insights from artificial intelligence-driven multi-omics analysis and <i>in vitro</i> validation.

Frontiers in neuroscience·2026
Same journal

Sex-divergent intrinsic brain function in Parkinson's disease: elevated nigral fluctuations and premotor-visuospatial coupling in female patients.

Frontiers in neuroscience·2026
Same journal

Spatial transcriptomics on an expanded dataset at the brain-electrode interface: exploration of variability and identification of novel biomarkers.

Frontiers in neuroscience·2026
See all related articles
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 Video

Updated: Feb 5, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.8K

Deep Supervised Learning Using Local Errors.

Hesham Mostafa1, Vishwajith Ramesh2, Gert Cauwenberghs1,2

  • 1Institute for Neural Computation, University of California, San Diego, San Diego, CA, United States.

Frontiers in Neuroscience
|September 21, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel local error mechanism for deep learning, bypassing the need for error backpropagation. This biologically plausible approach trains neural networks efficiently, matching backpropagation performance while reducing hardware demands.

Keywords:
backpropagationbiological learninghardware acceleratorslocal errorssupervised learning

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.6K

Related Experiment Videos

Last Updated: Feb 5, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.8K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.6K

Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Machine Learning

Background:

  • Error backpropagation is effective for deep networks but biologically implausible due to delayed error signals.
  • Biological neural networks require local learning rules, unlike the non-local dependencies in backpropagation.

Purpose of the Study:

  • To propose a biologically plausible alternative to error backpropagation for training deep neural networks.
  • To develop a local learning mechanism that reduces computational and memory overheads.

Main Methods:

  • Proposed a new learning mechanism using fixed, random auxiliary classifiers for local error generation in each layer.
  • Trained deep networks layer-by-layer or simultaneously using only local error information.
  • Addressed biological plausibility concerns, including weight symmetry.

Main Results:

  • The proposed local error mechanism achieved performance comparable to standard backpropagation on the CIFAR10 dataset.
  • Outperformed biologically-motivated feedback alignment techniques.
  • Demonstrated suitability for custom hardware, significantly reducing memory and communication overheads.

Conclusions:

  • The local error mechanism offers a biologically plausible and efficient alternative to backpropagation for deep learning.
  • This approach facilitates supervised learning of feature hierarchies and has practical implications for hardware acceleration.