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

Transformations of Functions II01:29

Transformations of Functions II

Transformations in mathematics alter the position or orientation of a function’s graph while preserving its fundamental shape. One important type of transformation is the horizontal shift, which involves modifying the input variable within a function’s equation. This operation affects where outputs occur along the horizontal axis but does not alter the function’s overall structure.A horizontal shift is achieved by replacing the input variable x with either x + c or x - c, where c is a constant.
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
Transformation01:26

Transformation

Microbial communities are dynamic environments where cell lysis releases free DNA into the surroundings. Other cells can take up this extracellular DNA through a process known as transformation.When a cell incorporates this foreign DNA into its genome, resulting in genetic modification, the process is known as transformation. Cells capable of this process are termed competent. Competence can be natural, as observed in certain bacteria and archaea, or artificially induced in the...
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...
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...

You might also read

Related Articles

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

Sort by
Same author

Nested Grover's Algorithm for Tree Search.

Entropy (Basel, Switzerland)·2026
Same author

Multilevel Data Representation for Training Deep Helmholtz Machines.

Neural computation·2025
Same author

Quantum Machine Learning-Quo Vadis?

Entropy (Basel, Switzerland)·2024
Same author

Can a Hebbian-like learning rule be avoiding the curse of dimensionality in sparse distributed data?

Biological cybernetics·2024
Same author

Promoting the Shift From Pixel-Level Correlations to Object Semantics Learning by Rethinking Computer Vision Benchmark Data Sets.

Neural computation·2024
Same author

Competitive learning to generate sparse representations for associative memory.

Neural networks : the official journal of the International Neural Network Society·2023
Same journal

Anchor-based disentanglement framework for incremental multi-view clustering.

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

Complex-valued amplitude-phase interference modeling for adversarially robust classification.

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

Related Experiment Video

Updated: Jun 20, 2026

Mapping Inhibitory Neuronal Circuits by Laser Scanning Photostimulation
09:50

Mapping Inhibitory Neuronal Circuits by Laser Scanning Photostimulation

Published on: October 6, 2011

Neocognitron and the Map Transformation Cascade.

Angelo Cardoso1, Andreas Wichert

  • 1Department of Informatics, IST - Technical University of Lisboa, Portugal. angelo.cardoso@ist.utl.pt

Neural Networks : the Official Journal of the International Neural Network Society
|September 29, 2009
PubMed
Summary
This summary is machine-generated.

We introduce the Map Transformation Cascade, a simplified neural network model inspired by Neocognitron. This model effectively reduces information and classifies patterns, achieving performance comparable to Neocognitron in handwriting recognition tasks.

More Related Videos

Double In Utero Electroporation to Target Temporally and Spatially Separated Cell Populations
10:45

Double In Utero Electroporation to Target Temporally and Spatially Separated Cell Populations

Published on: June 14, 2020

Long-Term Imaging of Identified Neural Populations using Microprisms in Freely Moving and Head-Fixed Animals
06:25

Long-Term Imaging of Identified Neural Populations using Microprisms in Freely Moving and Head-Fixed Animals

Published on: January 19, 2024

Related Experiment Videos

Last Updated: Jun 20, 2026

Mapping Inhibitory Neuronal Circuits by Laser Scanning Photostimulation
09:50

Mapping Inhibitory Neuronal Circuits by Laser Scanning Photostimulation

Published on: October 6, 2011

Double In Utero Electroporation to Target Temporally and Spatially Separated Cell Populations
10:45

Double In Utero Electroporation to Target Temporally and Spatially Separated Cell Populations

Published on: June 14, 2020

Long-Term Imaging of Identified Neural Populations using Microprisms in Freely Moving and Head-Fixed Animals
06:25

Long-Term Imaging of Identified Neural Populations using Microprisms in Freely Moving and Head-Fixed Animals

Published on: January 19, 2024

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision

Background:

  • Hierarchical neural networks like Neocognitron are complex for pattern recognition.
  • A need exists for simplified yet effective models in pattern classification.

Purpose of the Study:

  • To propose a simplified neural network model, the Map Transformation Cascade.
  • To demonstrate its efficacy in pattern recognition tasks, specifically handwriting recognition.

Main Methods:

  • The Map Transformation Cascade employs a sequence of filters for pattern transformation.
  • It separates learning into information reduction (clustering) and classification (supervised learning).
  • Utilizes algorithms like K-Means for clustering and nearest neighbor for classification.

Main Results:

  • The proposed Map Transformation Cascade achieves performance comparable to the Neocognitron model.
  • Successfully demonstrated in handwriting recognition tasks.

Conclusions:

  • The Map Transformation Cascade offers a simplified yet powerful alternative for hierarchical pattern recognition.
  • Its modular learning approach enhances flexibility and effectiveness.