Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Network Function of a Circuit

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.

Block Diagram Reduction

Block Diagram Reduction

The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...

Stability of structures

Stability of structures

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,...

Multimachine Stability

Multimachine Stability

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:

You might also read

Related Articles

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

Sort by

Same author

Energy landscapes and synergetic state transitions in frustrated Stuart-Landau oscillator networks: a homotopy continuation study.

Frontiers in network physiology·2026

Same author

CauFinder: Steering Cell-State and Phenotype Transitions by Causal Disentanglement Learning.

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

Same author

Identifying the optimal rapid antigen test for screening and determining the end of isolation: A modeling study.

PLoS computational biology·2026

Same author

Force Learning in Balanced Cortical E-I Networks.

Neural computation·2026

Same author

Psychological distress among Japanese high school students during the COVID-19 pandemic: An energy landscape analysis.

PLoS medicine·2026

Same author

Stratification of viral shedding patterns in saliva of COVID-19 patients.

eLife·2026

Search research articles

Related Experiment Video

Updated: Apr 16, 2026

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Published on: August 1, 2011

Unambiguous reconstruction of network structure using avalanche dynamics.

Timothée Leleu^1,2, Kazuyuki Aihara²

¹Center for Emergent Matter Science, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|March 14, 2015

Summary

Researchers developed a new network inference method using neuronal avalanche models. This approach links the structure of neural networks to the statistical properties of their activity patterns, enabling robust network reconstruction.

More Related Videos

Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations

Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations

Published on: March 19, 2021

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Related Experiment Videos

Last Updated: Apr 16, 2026

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Published on: August 1, 2011

Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations

Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations

Published on: March 19, 2021

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

Neuroscience
Network Science
Statistical Physics

Background:

Understanding the structure of complex networks, particularly neural networks, is crucial for deciphering their function.
Neuronal avalanches, characterized by crackling noise, represent a common mode of brain activity and may hold clues to network organization.

Purpose of the Study:

To present a robust method for inferring network structure.
To establish a direct link between the statistical properties of neuronal avalanches and the underlying network's weight matrix.

Main Methods:

Derivation of a closed-form expression for the joint probability distribution of avalanche sizes.
Utilizing a model of neuronal avalanches as a paradigmatic example.
Establishing a one-to-one correspondence between avalanche composition and the network weight matrix.

Main Results:

The study demonstrates an exact correspondence between the expected composition of neuronal avalanches and the network's weight matrix.
A robust method for network structure inference based on crackling noise properties is presented.

Conclusions:

The findings provide a novel framework for network inference by leveraging the statistical signatures of neuronal activity.
This method offers a powerful tool for analyzing complex systems where activity propagates in cascades.