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

39.5K
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.
39.5K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Regression Analysis01:11

Regression Analysis

5.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.7K
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Correlation and Regression00:53

Correlation and Regression

1.3K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
1.3K
Neural Regulation of Blood Pressure01:18

Neural Regulation of Blood Pressure

2.8K
The neural regulation of blood pressure involves intricate interactions between the autonomic nervous system (ANS) and cardiovascular system, ensuring adequate perfusion of tissues. This regulation primarily occurs through baroreceptor and chemoreceptor reflexes, involving both short-term and long-term mechanisms.
Baroreceptor Reflex
Baroreceptors, located in the carotid sinuses and aortic arch, detect changes in blood pressure. When blood pressure rises, these stretch-sensitive receptors...
2.8K

You might also read

Related Articles

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

Sort by
Same author

Leveraging deep learning to control neural oscillators.

Biological cybernetics·2021
Same author

Analysis of neural clusters due to deep brain stimulation pulses.

Biological cybernetics·2020
Same author

Phase reduction and phase-based optimal control for biological systems: a tutorial.

Biological cybernetics·2018
Same author

Phase model-based neuron stabilization into arbitrary clusters.

Journal of computational neuroscience·2018

Related Experiment Video

Updated: Jul 9, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K

Symbolic regression via neural networks.

N Boddupalli1, T Matchen1, J Moehlis1

  • 1Department of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, California 93106, USA.

Chaos (Woodbury, N.Y.)
|December 7, 2023
PubMed
Summary

This study introduces a novel deep learning model that generates symbolic expressions for governing equations. This approach combines deep learning accuracy with symbolic solution utility for dynamical systems analysis.

More Related Videos

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

10.3K
Author Spotlight: Understanding Processing of Olfactory and Spatial Information by Brain with Real-Time Behavioral Analysis
06:21

Author Spotlight: Understanding Processing of Olfactory and Spatial Information by Brain with Real-Time Behavioral Analysis

Published on: September 20, 2024

810

Related Experiment Videos

Last Updated: Jul 9, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
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

10.3K
Author Spotlight: Understanding Processing of Olfactory and Spatial Information by Brain with Real-Time Behavioral Analysis
06:21

Author Spotlight: Understanding Processing of Olfactory and Spatial Information by Brain with Real-Time Behavioral Analysis

Published on: September 20, 2024

810

Area of Science:

  • Dynamical Systems
  • Machine Learning
  • Scientific Computing

Background:

  • Identifying governing equations is crucial across science and engineering.
  • Traditional deep learning lacks interpretability for dynamical systems.
  • Existing symbolic methods often require domain expertise or struggle with overfitting.

Purpose of the Study:

  • To develop a novel approach combining deep learning flexibility with symbolic solution utility.
  • To create a deep neural network capable of generating symbolic expressions for governing equations.
  • To demonstrate the accuracy of this new method across various classical dynamical systems.

Main Methods:

  • A novel deep neural network architecture is proposed.
  • The model is designed to output symbolic expressions representing governing equations.
  • The approach integrates deep learning's data approximation capabilities with symbolic regression.

Main Results:

  • The developed deep neural network accurately generates symbolic expressions for governing equations.
  • The algorithm demonstrates high accuracy across a range of classical dynamical systems.
  • The method offers insights beyond numerical prediction by providing interpretable symbolic solutions.

Conclusions:

  • This novel deep learning approach successfully bridges the gap between accurate dynamical system prediction and interpretable symbolic modeling.
  • The method enhances the utility of machine learning in scientific discovery by providing explicit governing equations.
  • Future work can explore applications in complex systems where interpretability is paramount.