Jove
Visualize
Contact Us

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

372
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
372
Neural Regulation01:37

Neural Regulation

43.8K
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.
43.8K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

407
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
407
Linearization and Approximation01:26

Linearization and Approximation

108
Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
108
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

384
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
384
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

120
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
120

You might also read

Related Articles

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

Sort by
Same journal

Canard solutions in neural mass models: consequences on critical regimes.

Journal of mathematical neuroscience·2021
Same journal

Rendering neuronal state equations compatible with the principle of stationary action.

Journal of mathematical neuroscience·2021
Same journal

Pattern formation in a 2-population homogenized neuronal network model.

Journal of mathematical neuroscience·2021
Same journal

Auditory streaming emerges from fast excitation and slow delayed inhibition.

Journal of mathematical neuroscience·2021
Same journal

A model of on/off transitions in neurons of the deep cerebellar nuclei: deciphering the underlying ionic mechanisms.

Journal of mathematical neuroscience·2021
Same journal

Estimating Fisher discriminant error in a linear integrator model of neural population activity.

Journal of mathematical neuroscience·2021
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: Feb 26, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.2K

Regularization of Ill-Posed Point Neuron Models.

Bjørn Fredrik Nielsen1

  • 1Faculty of Science and Technology, Norwegian University of Life Sciences, P.O. Box 5003, Ås, 1432, Norway. bjorn.f.nielsen@nmbu.no.

Journal of Mathematical Neuroscience
|July 15, 2017
PubMed
Summary
This summary is machine-generated.

Neural point neuron models using steep firing rate functions are well-posed. However, their solutions may not always converge to the ill-posed Heaviside limit, especially if the solution reaches the firing threshold multiple times.

Keywords:
ExistenceIll-posedPoint neuron modelsRegularization

More Related Videos

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.3K
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.6K

Related Experiment Videos

Last Updated: Feb 26, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.2K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.3K
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.6K

Area of Science:

  • Computational neuroscience
  • Mathematical modeling of neural systems

Background:

  • Point neuron models are fundamental in computational neuroscience.
  • Heaviside firing rate functions can lead to ill-posed models, causing mathematical instability.
  • Well-posed approximations are needed for reliable numerical simulations.

Purpose of the Study:

  • To investigate the well-posedness of point neuron models with steep, Lipschitz continuous firing rate functions.
  • To determine if solutions of these well-posed models converge to solutions of ill-posed Heaviside models.
  • To analyze the conditions under which convergence occurs.

Main Methods:

  • Employing Lipschitz continuous, steep firing rate functions as approximations.
  • Utilizing the Arzelà-Ascoli theorem to analyze solution convergence.
  • Investigating the properties of the limit function, specifically its threshold simplicity.

Main Results:

  • Models with steep firing rate functions are well-posed, allowing approximate solutions with finite precision arithmetic.
  • Convergence of regularized solutions to the ill-posed limit is not guaranteed for all cases.
  • Convergence depends on the "threshold simplicity" of the limit function, meaning the set of times at which components hit the firing threshold must have zero Lebesgue measure.

Conclusions:

  • While steep firing rate functions create well-posed models, their solutions may not fully capture the behavior of ill-posed Heaviside models.
  • The existence of multiple solutions for Heaviside models complicates convergence analysis.
  • The "threshold simplicity" assumption is crucial for ensuring convergence of regularized solutions to the Heaviside limit.