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

Competition02:34

Competition

24.8K
When organisms require the same limited resources within an environment, they may have to compete for them. Competition is a net-negative interaction. Even if two competing individuals or populations do not interact directly, the overall fitness of both competitors is lowered as a result of not having full access to the limited resource.
24.8K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Linear Circuits01:17

Linear Circuits

872
A linear circuit is characterized by its output having a direct proportionality to its input, adhering to the linearity property, which encompasses the principles of homogeneity (scaling) and additivity. Homogeneity dictates that when the input, also referred to as the excitation, is multiplied by a constant factor, the output, known as the response, is correspondingly scaled by the same constant factor. For instance, if the current is multiplied by a constant 'k,' the voltage likewise...
872
Linear Equations01:27

Linear Equations

480
Linear equations form the foundation of many algebraic and real-world applications, characterized by their simplicity and utility. A linear equation is an algebraic statement in which each term is either a constant or a product of a constant and a single variable. These equations represent straight lines when plotted on a Cartesian coordinate plane, reflecting a constant rate of change between two quantities.A typical linear equation in one variable has the form: ax + b = c, where a, b, and c...
480
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Fixed Action Patterns01:06

Fixed Action Patterns

17.6K
A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.
17.6K

You might also read

Related Articles

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

Sort by
Same author

Attractor-Based Models for Sequences and Pattern Generation in Neural Circuits.

Neural computation·2026
Same author

Topological analysis of neuronal assemblies reveals low-rank structure modulated by cholinergic activity.

bioRxiv : the preprint server for biology·2025
Same author

Central Adiposity and Visceral Fat in Long-Term Survivors of Acute Lymphoblastic Leukemia in Childhood and Adolescence: Exploration of an Underappreciated Risk.

Pediatric blood & cancer·2025
Same author

Diet quality in relation to serum perfluoroalkyl substance concentrations in Canadian preadolescents.

Environmental research·2025
Same author

Topological Neuroscience: Linking Circuits to Function.

Annual review of neuroscience·2025
Same author

AAHPM Assessment Workgroup: Hospice and Palliative Medicine Fellowship Assessment Needs and Directions.

Journal of pain and symptom management·2024

Related Experiment Video

Updated: Feb 2, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.5K

Fixed Points of Competitive Threshold-Linear Networks.

Carina Curto1, Jesse Geneson2, Katherine Morrison3

  • 1Department of Mathematics, The Pennsylvania State University, University Park, PA 16802, U.S.A. ccurto@psu.edu.

Neural Computation
|November 22, 2018
PubMed
Summary
This summary is machine-generated.

We introduce new methods to characterize fixed points in threshold-linear networks (TLNs), a type of neural network model. These findings enable predicting network dynamics using graph theory for combinatorial TLNs (CTLNs).

More Related Videos

Comparing the Affinity of GTPase-binding Proteins using Competition Assays
10:37

Comparing the Affinity of GTPase-binding Proteins using Competition Assays

Published on: October 8, 2015

9.6K
Psychophysical Tracking Method to Assess Taste Detection Thresholds in Children, Adolescents, and Adults: The Taste Detection Threshold TDT Test
08:52

Psychophysical Tracking Method to Assess Taste Detection Thresholds in Children, Adolescents, and Adults: The Taste Detection Threshold TDT Test

Published on: April 21, 2021

5.4K

Related Experiment Videos

Last Updated: Feb 2, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.5K
Comparing the Affinity of GTPase-binding Proteins using Competition Assays
10:37

Comparing the Affinity of GTPase-binding Proteins using Competition Assays

Published on: October 8, 2015

9.6K
Psychophysical Tracking Method to Assess Taste Detection Thresholds in Children, Adolescents, and Adults: The Taste Detection Threshold TDT Test
08:52

Psychophysical Tracking Method to Assess Taste Detection Thresholds in Children, Adolescents, and Adults: The Taste Detection Threshold TDT Test

Published on: April 21, 2021

5.4K

Area of Science:

  • Computational Neuroscience
  • Network Dynamics
  • Systems Biology

Background:

  • Threshold-linear networks (TLNs) are computational models exhibiting nonlinear dynamics based on network connectivity.
  • Fixed points (equilibria) are crucial for understanding the emergent behavior and stability of TLNs.
  • Characterizing these fixed points is essential for predicting network dynamics.

Purpose of the Study:

  • To develop novel characterizations for the set of fixed points in competitive threshold-linear networks (TLNs).
  • To apply these characterizations to combinatorial TLNs (CTLNs) using graph-theoretic approaches.
  • To investigate how fixed points of larger networks relate to their constituent subnetworks.

Main Methods:

  • Developed two new characterizations for TLN fixed points: a sign condition and a domination-based approach.
  • Applied these methods to combinatorial TLNs (CTLNs) by defining connectivity matrices from directed graphs.
  • Proved graph rules for determining CTLN fixed points directly from their underlying graph structure.
  • Studied composite networks, relating their fixed points to those of their subnetworks.

Main Results:

  • Established a sign condition for identifying fixed points in competitive TLNs.
  • Introduced a domination-based characterization for TLN fixed points.
  • Derived graph rules enabling the prediction of CTLN fixed points from graph properties.
  • Demonstrated theorems connecting the fixed points of complex networks to their simpler components.

Conclusions:

  • The study provides novel mathematical tools for analyzing TLN fixed points.
  • Results facilitate a graphical calculus for inferring network dynamics from connectivity in CTLNs.
  • Findings offer a foundation for understanding complex biological and computational networks.