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Related Concept Videos

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...
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...
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...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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...

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Related Experiment Videos

Multilayer optical learning networks.

K Wagner, D Psaltis

    Applied Optics
    |June 5, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel optical neural network that learns pattern transformations using holographic gratings. The system adaptively adjusts interconnections for high-speed, parallel processing, mimicking backpropagation learning.

    Related Experiment Videos

    Area of Science:

    • Optoelectronics
    • Artificial Intelligence
    • Holography

    Background:

    • Optical neural networks offer potential for high-speed computation.
    • Adaptive interconnections are crucial for learning in neural networks.
    • Holographic methods provide a pathway for implementing complex optical interconnections.

    Purpose of the Study:

    • To present a new learning approach for multilayer optical neural networks.
    • To demonstrate a network capable of learning distributed representations for pattern transformation.
    • To utilize adaptive, self-aligning holographic interconnections for efficient learning.

    Main Methods:

    • Employing volume holographic gratings in photorefractive crystals for adaptive interconnections.
    • Utilizing nonlinear Fabry-Perot etalons for fast thresholding of transformed patterns.
    • Implementing a learning procedure based on holographic interference patterns and error signals, approximating backpropagation.

    Main Results:

    • The proposed network successfully learns interconnections for distributed pattern transformation.
    • Adaptive and self-aligning interconnections were achieved using holographic gratings.
    • The system demonstrated a massively parallel, high-speed implementation of a learning procedure.

    Conclusions:

    • A novel, adaptive learning method for optical neural networks has been developed.
    • Holographically interconnected nonlinear devices provide a viable architecture for high-speed optical learning.
    • The approach offers a promising direction for realizing efficient optical implementations of neural network learning algorithms.