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Updated: May 7, 2026
11:18
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
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Noise tolerance in a Neocognitron-like network.
Ângelo Cardoso1, Andreas Wichert
1INESC-ID, Av. Prof. Dr. Aníbal Cavaco Silva, 2744-016 Porto Salvo, Portugal; Instituto Superior Técnico, Universidade de Lisboa, Av. Prof. Dr. Aníbal Cavaco Silva, 2744-016 Porto Salvo, Portugal.
Summary
Hierarchical models show improved noise tolerance for handwritten digit recognition. This study enhances recognition capabilities for extremely noisy patterns using a winner-take-all approach.
Area of Science:
- Artificial Intelligence
- Computer Vision
- Machine Learning
Background:
- Neocognitron and hierarchical models are effective for object and handwritten digit recognition.
- These models often exhibit limited tolerance to various forms of noise.
Purpose of the Study:
- To analyze the noise tolerance of hierarchical models.
- To report on a novel approach for enhanced noise tolerance in pattern recognition.
Main Methods:
- Overview of previous results on model noise tolerance.
- Analysis of winner-take-all response in hierarchical models.
- Interpretation using Bayesian decision theory.
Main Results:
- The winner-take-all response in a hierarchical model demonstrates superior noise tolerance compared to related models.
- Identification of methods to further improve recognition of extremely noisy patterns.
Conclusions:
- Hierarchical models with winner-take-all mechanisms offer enhanced robustness against noise.
- The findings provide a pathway for improving recognition in challenging, noisy conditions.
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