Methodology for studying magnetic field effects on chimera states in Hindmarsh-Rose neuronal networks. Covers traveling chimera, multicluster chimera, and multicluster chimera breather transformations under spatial magnetic field applications.
Scanned 9/11/2026
Install to Claude Code
npx -y skills add hiyenwong/ai_collection --skill chimera-magnetic-field-neuronal --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Chimera Magnetic Field Neuronal?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/hiyenwong-chimera-magnetic-field-neuronal-ai-collection)More formats (shields.io, HTML) on the badges page.
---
name: chimera-magnetic-field-neuronal
description: "Methodology for studying magnetic field effects on chimera states in Hindmarsh-Rose neuronal networks. Covers traveling chimera, multicluster chimera, and multicluster chimera breather transformations under spatial magnetic field applications."
version: 1.0.0
author: Hermes Agent
license: MIT
metadata:
hermes:
tags: [chimera-state, hindmarsh-rose, magnetic-field, neuronal-dynamics, synchronization, neural-network, brain-cells, collective-dynamics]
category: ai_collection
arxiv_id: "2607.07426"
arxiv_url: "https://arxiv.org/abs/2607.07426"
published: "2026-07-08"
authors: ["Gael R Simo", "Carmel T lambu", "Adamou Dang Koko", "Patrick Louodop", "Robert Tchitnga", "Hilda A. Cerdeira"]
categories: ["nlin.AO"]
trigger_words: ["chimera state", "magnetic field", "hindmarsh-rose", "neuronal network", "traveling chimera", "multicluster chimera", "chimera breather", "synchronization", "coherence", "incoherence", "brain cells"]
created: "2026-07-12"
updated: "2026-07-12"
---
# Chimera State in a Neuronal Network under the Action of a Magnetic Field
**arXiv**: 2607.07426 | **Published**: 2026-07-08 | **Authors**: Gael R Simo, Carmel T lambu, Adamou Dang Koko, Patrick Louodop, Robert Tchitnga, Hilda A. Cerdeira
## Core Thesis
This study demonstrates the influence of **magnetic fields** on three categories of chimera states in Hindmarsh-Rose (HR) neuronal networks:
1. **Traveling chimera state**
2. **Traveling multicluster chimera state**
3. **Traveling multicluster chimera breather**
The key discovery: magnetic fields can **transform areas of incoherence into areas of coherence**, enriching the synchronization field and providing insights into how magnetic fields affect brain cells.
## Key Concepts
### Chimera States
A chimera state is a dynamical pattern in a network of identical oscillators where **coherent** (synchronized) and **incoherent** (desynchronized) domains coexist. In neuronal networks, this corresponds to some brain regions showing synchronized activity while others remain chaotic.
### Three Chimera Categories
1. **Traveling Chimera**: The coherent/incoherent boundary moves through the network over time
2. **Traveling Multicluster Chimera**: Multiple coherent clusters travel through the network
3. **Traveling Multicluster Chimera Breather**: The clusters exhibit breathing (expansion/contraction) dynamics while traveling
### Magnetic Field Application Patterns
The study applies magnetic fields in three spatial configurations:
1. **Full network application**: Entire network subjected to magnetic field
2. **Half-network application**: One half of the network subjected to field
3. **Dual-region application**: Two symmetrical but distinct regions subjected to field
### Emergent Phenomena
- **Multitraveling Chimera State**: Multiple coherent/incoherent domains traveling independently
- **Multialternating Chimera State**: Coherent and incoherent domains alternating in time and space
## Hindmarsh-Rose Model
The HR model describes neuronal spiking-bursting dynamics:
```
dx/dt = y - ax³ + bx² - z + I_ext
dy/dt = c - dx² - y
dz/dt = ε(s(x - x₀) - z) + ε·B(t)
```
Where:
- x: membrane potential
- y: spiking variable (fast)
- z: bursting variable (slow)
- I_ext: external current
- B(t): magnetic field influence term (added to slow variable)
## Methodology
### Numerical Procedure
1. **Initialize** the HR network with the target chimera state
2. **Apply** magnetic field in one of three spatial configurations
3. **Simulate** the network dynamics over time
4. **Analyze** coherence patterns using:
- Local order parameter (measuring phase synchronization)
- Snapshots of membrane potential across network
- Time-series analysis of coherence metrics
5. **Compare** before/after field application to identify transformations
### Coherence Measurement
The local order parameter measures synchronization:
```
r_i = |(1/δ) Σ_{j=i-δ/2}^{i+δ/2} exp(i·θ_j)|
```
where θ_j is the phase of neuron j, and δ is the neighborhood size.
## Key Findings
### Transformation of Chimera States
| Initial State | Magnetic Field Applied | Resulting Phenomenon |
|---------------|----------------------|---------------------|
| Traveling chimera | Full network | Modified traveling pattern |
| Traveling chimera | Half network | Boundary shift, new coherence zones |
| Multicluster chimera | Full network | Cluster restructuring |
| Multicluster chimera breather | Dual regions | **Multialternating chimera state** |
| Any chimera | Spatial field | **Multitraveling chimera state** |
### Core Insight
The magnetic field acts as a **control parameter** that:
- Transforms incoherent regions into coherent ones
- Creates new chimera variants not seen without the field
- Provides a mechanism for external modulation of brain synchronization patterns
## Applications
### 1. Transcranial Magnetic Stimulation (TMS)
Understanding how magnetic fields affect neuronal synchronization provides theoretical grounding for TMS therapy design:
- Predict which brain regions will synchronize under field application
- Design field patterns for specific therapeutic outcomes
### 2. Neuromodulation
Magnetic field effects on chimera states suggest new neuromodulation strategies:
- Spatial targeting of specific brain subnetworks
- Temporal modulation for inducing desired synchronization patterns
### 3. Brain Dynamics Modeling
The chimera framework provides a natural model for:
- Epileptic seizures (transition from incoherent to hyper-coherent states)
- Sleep-wake transitions (coherence pattern changes)
- Cognitive states (localized coherence in task-relevant regions)
## Verification Methods
### Numerical Reproduction
1. Implement HR neuronal network (typically N=100-500 nodes)
2. Set coupling parameters to produce baseline chimera states
3. Add magnetic field term to slow variable equation
4. Run simulation, compute local order parameter
5. Compare coherence maps before/after field application
### Metrics
- **Local order parameter**: r_i ∈ [0, 1], coherence measure
- **Chimera index**: Measure of coexistence of coherent/incoherent domains
- **Traveling speed**: Rate of chimera boundary propagation
## Trigger Words
chimera state, magnetic field, hindmarsh-rose, neuronal network, traveling chimera, multicluster chimera, chimera breather, synchronization, coherence, incoherence, brain cells, transcranial magnetic stimulation, neuromodulation, collective dynamics
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!