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Related Concept Videos

Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or playing an...
Introduction to Learning01:18

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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...

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Related Experiment Video

Updated: Jun 23, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Associative memory for online learning in noisy environments using self-organizing incremental neural network.

Akihito Sudo1, Akihiro Sato, Osamu Hasegawa

  • 1Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 226-8503, Japan. sudo@isl.titech.ac.jp

IEEE Transactions on Neural Networks
|April 29, 2009
PubMed
Summary

This study introduces a novel associative memory capable of online incremental learning and robust to two types of noise. It adaptively adjusts memory size, ensuring efficient learning for intelligent robots in real-world environments.

Related Experiment Videos

Last Updated: Jun 23, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Robotics

Background:

  • Real-world environments demand associative memory systems capable of continuous learning and noise resilience.
  • Sequential presentation of noisy data poses challenges for conventional associative memory models.
  • Intelligent robots require associative memory that can adapt to unknown data volumes and noise types.

Purpose of the Study:

  • To propose a novel associative memory system designed for online incremental learning in noisy, real-world environments.
  • To develop an associative memory robust to both noise-added original patterns and faultily presented random patterns.
  • To create an adaptive memory system with a dynamically growing size suitable for unknown learning capacities.

Main Methods:

  • Implementation of a growing self-organizing network as the core of the associative memory.
  • Development of algorithms to handle sequential learning without catastrophic forgetting.
  • Integration of noise-filtering mechanisms for two distinct types of input noise.

Main Results:

  • The proposed associative memory achieves accurate learning of new patterns without compromising previously learned information.
  • The memory size dynamically scales, preventing both redundancy and insufficiency.
  • The system demonstrates robustness against both types of input noise, a capability not found in conventional models.
  • The associative memory supports bidirectional one-to-many or many-to-one associations and handles bipolar and real-valued data.

Conclusions:

  • The novel associative memory meets critical requirements for intelligent robots operating in real environments.
  • The use of a growing self-organizing network offers a unique approach to associative memory design.
  • The proposed system advances the development of robust and adaptive memory solutions for AI applications.