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

Block Diagram Reduction01:22

Block Diagram Reduction

The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
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...
Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
Neuroplasticity01:01

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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...
Reducing Line Loss01:18

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...

You might also read

Related Articles

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

Sort by
Same author

Diabetic Polyneuropathy: New Strategies to Target Sensory Neurons in Dorsal Root Ganglia.

International journal of molecular sciences·2023
Same author

MEK5-ERK5 Axis Promotes Self-renewal and Tumorigenicity of Glioma Stem Cells.

Cancer research communications·2023
Same author

Chemical-state distributions in charged LiCoO<sub>2</sub> cathode particles visualized by soft X-ray spectromicroscopy.

Scientific reports·2023
Same author

Predictors of life-space mobility in patients with fracture 3 months after discharge from convalescent rehabilitation ward: a prospective longitudinal study.

Journal of physical therapy science·2023
Same author

Effect of multimodal comprehensive communication skills training with video analysis by artificial intelligence for physicians on acute geriatric care: a mixed-methods study.

BMJ open·2023
Same author

5-Oxoproline Enhances 4-Hydroxytamoxifen-induced Cytotoxicity by Increasing Oxidative Stress in MCF-7 Breast Cancer Cells.

Anticancer research·2023

Related Experiment Video

Updated: Jun 12, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Exceptional reducibility of complex-valued neural networks.

Masaki Kobayashi1

  • 1Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, Takeda, Kofu, Yamanashi 400-8511, Japan. k-masaki@yamanashi.ac.jp

IEEE Transactions on Neural Networks
|June 17, 2010
PubMed
Summary

Researchers introduce a new method to minimize complex-valued neural networks by presenting an additional type of reducibility. This advancement aids in optimizing neural network architecture and performance.

More Related Videos

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jun 12, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Neural networks are evaluated for minimality, defined by the inability to reduce hidden neurons while preserving the input-output map.
  • Reducibility in neural networks refers to the condition where hidden neurons can be reduced.
  • Real-valued neural networks exhibit three basic forms of reducibility.

Purpose of the Study:

  • To extend the concept of reducibility to complex-valued neural networks.
  • To identify and present a novel type of reducibility in general complex-valued neural networks, termed 'exceptional reducibility'.
  • To propose a method for minimizing complex-valued neural networks.

Main Methods:

  • Analysis of reducibility conditions in real-valued neural networks.
  • Extension of reducibility concepts to complex-valued neural networks, specifically those without bias terms.
  • Identification and characterization of a new 'exceptional reducibility' in general complex-valued neural networks.
  • Development of a minimization technique for complex-valued neural networks based on the new reducibility type.

Main Results:

  • Real-valued neural networks possess three simple types of reducibility.
  • Complex-valued neural networks without bias terms can be naturally extended from real-valued network reducibility.
  • General complex-valued neural networks exhibit an additional, 'exceptional' type of reducibility.
  • A novel method for minimizing complex-valued neural networks has been developed.

Conclusions:

  • The study expands the understanding of reducibility in neural networks, particularly for complex-valued models.
  • A new type of reducibility, 'exceptional reducibility', is identified in complex-valued neural networks.
  • The proposed method offers a pathway to minimize complex-valued neural networks, potentially improving efficiency and performance.