Jove
Visualize
Contact Us

Related Concept Videos

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...
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...
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...
Purposive Learning01:22

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a bonus...
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...
Chunking and Rehearsal in Sensory Memory01:22

Chunking and Rehearsal in Sensory Memory

Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of information more...

You might also read

Related Articles

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

Sort by
Same author

FlowRoI: fast optical-flow-based Roi extraction for high-throughput immune cell image compression.

Npj imaging·2026
Same author

Fast reprogramming and adaptive reproduction of contact-rich assembly.

Frontiers in robotics and AI·2026
Same author

Cooperative robotic exploration of a planetary skylight surface and lava cave.

Science robotics·2025
Same author

Clinical Evaluation of Next-generation, Multi-weight Hyaluronic Acid Plus Antioxidant Complex-based Topical Formulations with Targeted Delivery to Enhance Skin Rejuvenation.

The Journal of clinical and aesthetic dermatology·2024
Same author

Evaluation of Antioxidants' Ability to Enhance Hyaluronic-acid Based Topical Moisturizers.

The Journal of clinical and aesthetic dermatology·2024
Same author

EEG and EMG dataset for the detection of errors introduced by an active orthosis device.

Frontiers in human neuroscience·2024
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 Experiment Video

Updated: May 13, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Learning in compressed space.

Alexander Fabisch1, Yohannes Kassahun, Hendrik Wöhrle

  • 1University of Bremen, Fachbereich 3 - Mathematik und Informatik, Postfach 330 440, 28334 Bremen, Germany. afabisch@googlemail.com

Neural Networks : the Official Journal of the International Neural Network Society
|March 19, 2013
PubMed
Summary

Compressed sensing and model compression significantly reduce training time for artificial neural networks. These methods compress network weights or inputs, speeding up complex machine learning tasks.

Related Experiment Videos

Last Updated: May 13, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Complex machine learning problems often involve large artificial neural networks.
  • Compressed sensing and model compression are two techniques for addressing this complexity.
  • Feed-forward artificial neural networks are a common architecture.

Purpose of the Study:

  • To investigate the application of compressed sensing and model compression in feed-forward neural networks.
  • To develop a backpropagation method within a compressed parameter space.
  • To demonstrate significant reductions in training time using these compression techniques.

Main Methods:

  • Developing a backpropagation algorithm for compressed parameter spaces.
  • Establishing the equivalence between compressing layer weights and compressing layer inputs.
  • Utilizing orthogonal functions and random projections for compression.
  • Conducting experiments in supervised and reinforcement learning settings.

Main Results:

  • Demonstrated that compressing layer weights is equivalent to compressing layer inputs.
  • Successfully implemented backpropagation in compressed parameter space.
  • Achieved significant reductions in training time for artificial neural networks.
  • Validated the effectiveness of random projections for model compression.

Conclusions:

  • Compressed sensing and model compression offer efficient solutions for complex machine learning.
  • The developed methods significantly decrease training duration for neural networks.
  • The theoretical framework provides a basis for further research in efficient deep learning.