Related Experiment Video
Updated: Mar 10, 2026
13:44
Sealable Femtoliter Chamber Arrays for Cell-free Biology
Published on: March 11, 2015
9.9K
Generalized synchrony of coupled stochastic processes with multiplicative noise
Haider Hasan Jafri1, R K Brojen Singh2, Ramakrishna Ramaswamy2,3
1Department of Physics, Aligarh Muslim University, Aligarh 202 002, India.
Physical Review. E
|December 15, 2016
Summary
We found that coupled noisy systems can exhibit generalized synchronization, a phenomenon where drive and response become strongly correlated above a coupling threshold. This stochastic synchronization is detected using permutation entropy or mutual information.
Area of Science:
- Physics
- Nonlinear Dynamics
- Complex Systems
Background:
- Coupled dynamical systems often exhibit complex behaviors, including synchronization.
- Stochastic processes and intrinsic noise are prevalent in natural systems like chemical and biological oscillators.
- Drive-response configurations are common in coupled oscillatory networks.
Purpose of the Study:
- To investigate the emergence of generalized synchronization in systems with multiplicative noise and intrinsic noise.
- To identify reliable methods for detecting stochastic generalized synchronization in the presence of significant dynamical fluctuations.
Main Methods:
- Modeling coupled stochastic processes with multiplicative noise.
- Analyzing drive-response configurations in oscillatory systems.
- Employing permutation entropy and mutual information to quantify correlations.
Main Results:
- A stochastic analog of generalized synchronization was identified above a critical coupling threshold.
- Strong correlations between drive and response variables were observed.
- Permutation entropy and mutual information proved effective in detecting this transition.
Conclusions:
- Multiplicative noise in coupled stochastic systems can lead to generalized synchronization.
- Advanced correlation measures are essential for detecting synchronization in noisy environments.
- This work provides insights into synchronization phenomena in complex natural systems.
Related Concept Videos
00:59Propagation of Uncertainty from Random Error
2.1K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
2.1K
01:10Propagation of Uncertainty from Systematic Error
1.5K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.5K
01:21Entropy Changes Accompanying Specific Processes
28
Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression...
28
01:25Poisson's And Laplace's Equation
4.5K
The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
4.5K
01:23Linear time-invariant Systems
1.0K
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
1.0K
01:10Entropy Change in Reversible Processes
3.3K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
3.3K