| name | eeg-hopfield-emotion-energy |
| description | Energy landscapes for quantifying brain network stability during emotional processing. Uses Hopfield network energy framework to analyze EEG dynamics, mapping emotional states to attractor basins in brain network energy landscapes. Provides a physics-based framework for understanding emotional stability and transitions. Activation: energy landscape, Hopfield network emotion, brain network stability, emotional processing EEG, attractor dynamics, affective neuroscience, 能量景观, 情绪脑网络, 吸引子动力学
|
| version | 1.0.0 |
| metadata | {"hermes":{"source_paper":"Energy Landscapes of Emotion: Quantifying Brain Network Stability during Emotional Processing","arxiv_id":"2603.27644","tags":["eeg","emotion","energy-landscape","hopfield","attractor","stability"]}} |
Energy Landscapes of Emotion
Overview
Maps emotional states to attractor basins in brain network energy landscapes using Hopfield network framework. Provides quantitative measures of emotional stability, transition barriers, and metastability from EEG data.
Core Framework
Energy Function
E(x) = -½ Σᵢⱼ Wᵢⱼ xᵢ xⱼ + Σᵢ θᵢ xᵢ
Where:
- Wᵢⱼ = functional connectivity between brain regions
- xᵢ = activity of region i
- θᵢ = bias terms (regional excitability)
Emotional States as Attractors
- Each emotional state corresponds to a local energy minimum
- Depth of basin → emotional stability
- Barrier height → difficulty of emotional transition
- Basin width → emotional flexibility
Analysis Pipeline
class EmotionEnergyLandscape:
def __init__(self, connectivity_matrix):
self.W = connectivity_matrix
self.energy_history = []
def compute_energy(self, brain_state):
"""Compute Hopfield energy for a brain state."""
return -0.5 * brain_state @ self.W @ brain_state.T
def find_attractors(self, states):
"""Identify emotional attractor basins."""
energies = [self.compute_energy(s) for s in states]
attractors = cluster_by_energy(energies, states)
return attractors
def transition_barriers(self, attractor_a, attractor_b):
"""Compute energy barrier between emotional states."""
path = minimum_energy_path(attractor_a, attractor_b)
return max(path.energies) - attractor_a.energy
Key Metrics
- Landscape Depth: Overall energy range → emotional range capacity
- Basin Stability: Depth of each attractor → emotional persistence
- Transition Rates: Barrier heights → emotional switching frequency
- Metastability: Number and arrangement of basins → emotional complexity
Applications
- Depression: abnormally deep negative attractors
- Anxiety: shallow basins with low barriers
- Bipolar: multiple competing deep basins
- Emotional regulation therapy planning
Related Skills
- eeg-hopfield-emotion-energy, neural-dynamics-decision-making, kuramoto-brain-network
