القائمة
Metabolic quantum limit to the information capacity of magnetoencephalography - 代谢量子极限作为MEG信息容量的基本约束
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| name | metabolic-quantum-limit-meg-magnetoencephalography |
| description | Metabolic quantum limit to the information capacity of magnetoencephalography - 代谢量子极限作为MEG信息容量的基本约束 |
| version | 1.0.0 |
| author | arXiv cron job |
| arxiv_id | 2511.06401v3 |
| created | 2026-06-15T00:00:00.000Z |
| category | neuroscience |
| activation_keywords | ["metabolic quantum limit","meg","magnetoencephalography","information capacity","quantum limit","brain imaging","metabolic constraint","neural sensing","fundamental bound"] |
| tags | ["neuroscience","brain-imaging","quantum-information","metabolic-modeling","meg-fundamentals"] |
首次推导代谢量子极限作为MEG信息容量的基本物理约束,揭示脑成像技术的根本信息边界,连接神经代谢成本与量子信息理论。
建立MEG信息容量的双重约束:
推导MEG最大信息传输率的理论表达式:
I_max = C_metabolic × Q_quantum_limit
Metabolic Constraint:
- ATP consumption per action potential
- Glucose代谢率 → 能量预算
- Neural firing rate → metabolic cost
Quantum Limit:
- Magnetic field measurement uncertainty
- Sensor quantum efficiency
- Heisenberg uncertainty principle
# Compute theoretical information capacity
def meg_information_capacity(sensors, metabolic_budget):
"""
Calculate MEG information capacity from metabolic quantum limit
"""
# Metabolic signal strength
neural_activity_rate = metabolic_budget / ATP_per_spike
signal_amplitude = magnetic_field_per_spike * neural_activity_rate
# Quantum measurement noise
quantum_noise = sensor_quantum_noise(sensors)
# Information capacity (Shannon)
SNR = signal_amplitude / quantum_noise
info_rate = bandwidth * log2(1 + SNR)
return info_rate
# Optimize MEG experiment within metabolic constraints
experiment_design = optimize_within_limits(
metabolic_budget=subject_budget,
quantum_noise=sensor_noise,
target_info_rate=required_resolution
)
# Assess MEG system performance vs theoretical limit
capacity_gap = theoretical_limit - actual_performance
efficiency = actual / theoretical
# Compare information capacity across modalities
modalities = {
'MEG': meg_capacity(metabolic, quantum),
'EEG': eeg_capacity(metabolic, classical),
'fMRI': fmri_capacity(hemodynamic, thermal)
}