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

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Nonconscious Mimicry01:13

Nonconscious Mimicry

Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
Cognition and Behavior01:23

Cognition and Behavior

Social psychology examines the complex interplay between individual mental processes and social interactions. Historically, the field was divided into two domains: social behavior and social cognition. Researchers focusing on social behavior analyzed actions within social contexts, such as conformity, aggression, or cooperation. Meanwhile, social cognition researchers investigated how people perceive, interpret, and mentally represent their social environments. However, modern perspectives no...

You might also read

Related Articles

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

Sort by
Same author

Efficient IntVec: High recognition rate with reduced computational cost.

Neural networks : the official journal of the International Neural Network Society·2019
Same author

Introduction.

International journal of neural systems·2018
Same author

Margined winner-take-all: New learning rule for pattern recognition.

Neural networks : the official journal of the International Neural Network Society·2017
Same author

Training multi-layered neural network neocognitron.

Neural networks : the official journal of the International Neural Network Society·2013
Same author

Artificial vision by multi-layered neural networks: neocognitron and its advances.

Neural networks : the official journal of the International Neural Network Society·2012
Same author

Neocognitron trained with winner-kill-loser rule.

Neural networks : the official journal of the International Neural Network Society·2010
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

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

Decentralized ADMM for factorization-based Low-rank matrix estimation.

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

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

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

Related Experiment Videos

Increasing robustness against background noise: visual pattern recognition by a neocognitron.

Kunihiko Fukushima1

  • 1Fuzzy Logic Systems Institute, Iizuka, Fukuoka, Japan. fukushima@m.ieice.org

Neural Networks : the Official Journal of the International Neural Network Society
|April 13, 2011
PubMed
Summary
This summary is machine-generated.

This study enhances the neocognitron, a neural network for visual pattern recognition, to improve handwritten digit recognition on noisy backgrounds. The modified neocognitron demonstrates significantly improved robustness against background noise.

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Machine Learning

Background:

  • The neocognitron is a hierarchical neural network designed for visual pattern recognition.
  • While effective for handwritten digits, its performance degrades on noisy backgrounds.

Purpose of the Study:

  • To investigate the causes of the neocognitron's vulnerability to noise.
  • To propose modifications for enhancing noise robustness in visual pattern recognition.

Main Methods:

  • Analysis of feature-extracting S-cells' behavior in response to noise.
  • Introduction of subtractive inhibition from V-cells to S-cells using a root-mean-square average.
  • Implementation of several additional modifications to the neocognitron architecture.

Main Results:

  • The study identified specific vulnerabilities in S-cell processing under noisy conditions.
  • The proposed subtractive inhibition mechanism and other modifications were integrated into the neocognitron.
  • Computer simulations confirmed the enhanced neocognitron's superior performance on noisy data.

Conclusions:

  • The modified neocognitron exhibits significantly improved robustness against background noise compared to conventional versions.
  • The proposed enhancements offer a viable solution for reliable handwritten digit recognition in challenging visual environments.