Related Experiment Videos
Anticipated synchronization and the predict-prevent control method in the FitzHugh-Nagumo model system
C Mayol1, C R Mirasso, R Toral
1Instituto de Física Interdisciplinar y Sistemas Complejos, CSIC-UIB, Campus Universitat Illes Balears, Palma de Mallorca, Spain.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 26, 2012
Summary
Synchronization in coupled FitzHugh-Nagumo systems is robust to external forcing when the slave anticipates the master. Predict-prevent control effectively suppresses unwanted pulses, even with parameter mismatches.
Area of Science:
- Computational Neuroscience
- Nonlinear Dynamics
- Systems Biology
Background:
- FitzHugh-Nagumo systems are simplified models of neuron dynamics.
- Unidirectional coupling creates master-slave configurations for studying information flow.
- External forcing can disrupt or influence system synchronization.
Purpose of the Study:
- Investigate the synchronization region of unidirectionally coupled FitzHugh-Nagumo systems.
- Analyze the robustness of synchronization against external forcing.
- Evaluate the efficacy of predict-prevent control for pulse suppression.
Main Methods:
- Master-slave configuration of two FitzHugh-Nagumo systems.
- Analysis of system dynamics under external forcing.
- Application and comparison of predict-prevent control versus direct control methods.
Main Results:
- Synchronization is robust in the anticipatory region of the slave system.
- Predict-prevent control, using slave output, is more efficient than direct control.
- Perfect parameter matching is not required for efficient control; mismatches can enhance it.
Conclusions:
- The anticipatory synchronization region offers robust dynamics against external perturbations.
- Predict-prevent control is a superior strategy for managing unwanted dynamics in coupled systems.
- Parameter variability can be leveraged to improve control efficacy in coupled nonlinear systems.
Related Concept Videos
01:07Time-Domain Interpretation of PD Control
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
Consider the example of control of motor torque. Initially, a positive...
01:10Control Systems
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
At the heart...
01:13Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
01:27Pharmacodynamic Models: Overview
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
01:09Pharmacodynamic Models: Additive and Proportional Drug Effect Model
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
01:25Multimachine 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:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by: