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

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.
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...
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.

You might also read

Related Articles

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

Sort by
Same author

On the active and passive exchanges of ions through cell surfaces and membranes in general.

American scientist·2010
Same author

The active and passive exchanges of inorganic ions through the surfaces of living cells and through living membranes generally.

Proceedings of the Royal Society of London. Series B, Biological sciences·2010
Same author

The production and ripening of red blood cells.

The Scientific monthly·2010
Same author

The comparative physiology of respiratory organs.

Experientia·2010
Same author

Metformin and antihypertensive therapy with drugs blocking the renin angiotensin system, a cause of concern?

Clinical nephrology·2006
Same author

Measuring covariation in RNA alignments: physical realism improves information measures.

Bioinformatics (Oxford, England)·2006
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Related Experiment Video

Updated: Jun 26, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Hidden neural networks

A Krogh1, S K Riis

  • 1Department of Chemistry, Center for Biological Sequence Analysis, Technical University of Denmark, Building 208, DK 2800 Lyngby, Denmark. krogh@cbs.dtu.dk.

Neural Computation
|February 9, 1999
PubMed
Summary
This summary is machine-generated.

Hidden Neural Networks (HNNs) integrate neural networks with hidden Markov models, offering improved performance in tasks like phoneme recognition. This hybrid model provides a valid probabilistic interpretation and simultaneous parameter estimation.

More Related Videos

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

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 26, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

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:

  • Machine Learning
  • Artificial Intelligence
  • Probabilistic Graphical Models

Background:

  • Standard Hidden Markov Models (HMMs) are widely used but have limitations.
  • Neural Networks (NNs) offer powerful pattern recognition capabilities.
  • Integrating HMMs and NNs can potentially enhance model performance.

Purpose of the Study:

  • To introduce a novel framework called Hidden Neural Networks (HNNs).
  • To describe the architecture and estimation methods for HNNs.
  • To evaluate the performance of HNNs against standard HMMs.

Main Methods:

  • Developed a general framework for hybrid Hidden Markov Models (HMMs) and Neural Networks (NNs) termed Hidden Neural Networks (HNNs).
  • Replaced standard HMM probability parameters with state-specific neural network outputs.
  • Employed a discriminative conditional maximum likelihood criterion for simultaneous estimation of all HNN parameters.
  • Interpreted HNNs as undirected probabilistic independence networks, with NNs representing clique functions.

Main Results:

  • HNNs demonstrated clear performance gains in recognizing broad phoneme classes on the TIMIT database.
  • The proposed HNN framework achieves a valid probabilistic interpretation through global normalization.
  • Simultaneous estimation of all parameters using the discriminative conditional maximum likelihood criterion was successful.

Conclusions:

  • Hidden Neural Networks (HNNs) represent a significant advancement over standard Hidden Markov Models (HMMs).
  • The HNN framework offers a robust and interpretable hybrid approach for complex pattern recognition tasks.
  • The results confirm the effectiveness of HNNs in improving performance on phoneme recognition tasks.