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Spatiotemporal patterns in FitzHugh-Nagumo network and its application in image encryption
Zhao Yao1, Kehui Sun1, Huihai Wang2
1School of Physics, Central South University, Changsha, 410083, China.
Summary
This study explores the FitzHugh-Nagumo (FHN) system
Area of Science:
- Computational Neuroscience
- Complex Systems Dynamics
Background:
- Neuronal dynamics are crucial for understanding brain function.
- The FitzHugh-Nagumo (FHN) model is a simplified representation of neuron behavior.
Purpose of the Study:
- To investigate the dynamics of the FHN system from single neurons to networks.
- To explore applications of FHN network dynamics in image encryption.
Main Methods:
- Analysis of firing patterns (bursting, spiking, chaotic) using an energy function.
- Construction of coupled FHN systems with electrical synaptic connections.
- Development of a grid-like FHN network to study spatiotemporal patterns.
Main Results:
- Identified bursting firing mode with higher energy oscillation than chaotic patterns.
- Observed phase locking in coupled FHN systems, relevant to biological rhythms.
- Demonstrated energy diversity in FHN networks inducing target waves and complex spatiotemporal patterns.
Conclusions:
- FHN network dynamics, particularly energy diversity, can generate complex patterns applicable to image encryption.
- A proposed FHN network-based encryption scheme shows good security performance, a large key space, and efficiency.
- Implementation on FPGA confirms the feasibility and parallelization potential for image processing applications.
