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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

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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.

Neural Networks : the Official Journal of the International Neural Network Society

|October 24, 2025

Summary

This study explores the FitzHugh-Nagumo (FHN) system

Keywords:

FPGA implementation Image encryption Neuron dynamics Neuronal network Spatiotemporal patterns

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Published on: August 5, 2014

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.