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

40.4K
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.
40.4K
Survival Tree01:19

Survival Tree

166
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
166
Multimachine Stability01:25

Multimachine Stability

235
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
235
Stability01:28

Stability

194
The time response of a linear time-invariant (LTI) system can be divided into transient and steady-state responses. The transient response represents the system's initial reaction to a change in input and diminishes to zero over time. In contrast, the steady-state response is the behavior that persists after the transient effects have faded.
The stability of an LTI system is determined by the roots of its characteristic equation, known as poles. A system is stable if it produces a bounded...
194
Stability of structures01:14

Stability of structures

258
In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
258
Neural Circuits01:25

Neural Circuits

1.6K
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...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Element-wise and Recursive Solutions for the Power Spectral Density of Biological Stochastic Dynamical Systems at Fixed Points.

Physical review research·2026
Same author

Persistently Increased Expression of PKMzeta and Unbiased Gene Expression Profiles Identify Hippocampal Molecular Traces of a Long-Term Active Place Avoidance Memory and "Shadow" Proteins.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Emergent universal long-range structure in random-organizing systems.

Nature communications·2026
Same author

PropMolFlow: property-guided molecule generation with geometry-complete flow matching.

Nature computational science·2026
Same author

Generalized Probabilistic Approximate Optimization Algorithm.

Nature communications·2025
Same author

The k-core as a predictor of structural collapse in mutualistic ecosystems.

Nature physics·2025

Related Experiment Video

Updated: Sep 19, 2025

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

10.5K

Stabilization of recurrent neural networks through divisive normalization.

Flaviano Morone1, Shivang Rawat2, David J Heeger3

  • 1Center for Neural Science, NYU and Center for Soft Matter Research, Department of Physics, NYU.

Biorxiv : the Preprint Server for Biology
|June 6, 2025
PubMed
Summary

Recurrent neural networks can maintain stability beyond traditional limits using divisive normalization. This neural mechanism enhances stability by suppressing neuron responses, offering a potential reason for its prevalence in biological systems.

More Related Videos

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.6K

Related Experiment Videos

Last Updated: Sep 19, 2025

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

10.5K
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.6K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Dynamical Systems

Background:

  • Ensuring stability in recurrent neural networks is challenging due to sensitivity to synaptic weights.
  • Linear dynamical models require eigenvalues within the unit circle for stability, a strict condition for recurrent networks.

Purpose of the Study:

  • To investigate if recurrent neural networks can achieve stability even with spectral radii exceeding 1.
  • To explore the role of divisive normalization in maintaining neural circuit stability.

Main Methods:

  • Theoretical analysis of recurrent neural network dynamics.
  • Numerical simulations to validate theoretical predictions.
  • Analytical prediction of normalization breakdown.

Main Results:

  • Recurrent neural networks with divisive normalization can remain stable when spectral radii exceed 1.
  • Critical slowing down, an early warning signal for instability, precedes the loss of stability.
  • The onset of critical slowing down correlates with the breakdown of normalization.

Conclusions:

  • Divisive normalization is crucial for enhancing dynamical stability in recurrent neural networks.
  • The prevalence of normalization in neural systems may stem from its role in stability, not just computation.
  • Findings offer insights into the design principles of both biological and engineered neural circuits.