---
id: wiki-2026-0508-neural-ignition
title: Neural Ignition
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Neural-Ignition, Global-Ignition, Conscious-Access]
duplicate_of: none
source_trust_level: A
confidence_score: 0.85
verification_status: applied
tags: [neuroscience, consciousness, global-workspace, attention, cognitive-neuroscience]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: mne-nilearn
---

# Neural Ignition

## 매 한 줄
> **"매 자극이 의식에 진입하는 순간 전체 뇌가 점화된다"**. Dehaene-Changeux Global Neuronal Workspace theory의 매 핵심 phenomenon — 매 sub-threshold 처리가 매 frontoparietal network의 non-linear all-or-none 활성화로 전이된다. 2026 LLM consciousness 논쟁에서 매 reference 모델로 자주 인용.

## 매 핵심

### 매 특성
- **All-or-none**: 매 sub-threshold → ignition 임계 초과 시 매 전역 활성.
- **Late, large-amplitude**: P300 / late slow wave (300-500 ms post-stimulus).
- **Long-distance synchrony**: gamma/beta cross-frequency coupling.
- **Reportable**: 매 ignition된 정보만 self-report 가능 (per GNW).

### 매 메커니즘 (GNW)
- Local processors → workspace neuron(layer 5 pyramidal) 경쟁.
- Top-down amplification: prefrontal/parietal feedback.
- Inhibitory winner-take-all.

### 매 응용
1. Anesthesia depth monitor (PCI, perturbational complexity).
2. Vegetative/MCS patient assessment.
3. Subliminal vs supraliminal masking experiments.
4. AI consciousness benchmarks (GWT-inspired).

## 💻 패턴

### EEG ERP analysis (MNE)
```python
import mne
epochs = mne.Epochs(raw, events, tmin=-0.2, tmax=0.8,
                    baseline=(None, 0), preload=True)
evoked = epochs.average()
evoked.plot(picks=['Pz'])  # P300 ignition signature
```

### Phase-locking value (long-range sync)
```python
import numpy as np
def plv(sig1, sig2):
    phase_diff = np.angle(sig1) - np.angle(sig2)
    return np.abs(np.exp(1j * phase_diff).mean())
```

### PCI (Perturbational Complexity Index)
```python
from sklearn.cluster import KMeans
def pci(tms_eeg_response):
    binary = (tms_eeg_response > threshold).astype(int)
    return lempel_ziv_complexity(binary.flatten())
```

### Neural mass model (Wilson-Cowan ignition)
```python
def wilson_cowan(E, I, P, dt=1e-3, tau_e=10, tau_i=20):
    dE = (-E + sigmoid(c1*E - c2*I + P)) / tau_e
    dI = (-I + sigmoid(c3*E - c4*I)) / tau_i
    return E + dt*dE, I + dt*dI
```

### Detection of ignition events
```python
def detect_ignition(signal, fs, win_ms=200, k=3):
    win = int(fs * win_ms / 1000)
    rolling = np.lib.stride_tricks.sliding_window_view(signal, win)
    amp = np.abs(rolling).mean(-1)
    return amp > amp.mean() + k * amp.std()
```

### Cross-frequency coupling
```python
def pac(low_phase, high_amp):
    return np.abs((high_amp * np.exp(1j*low_phase)).mean())
```

## 매 결정 기준
| Question | Method |
|---|---|
| Conscious access? | P300 / late wave + report |
| Anesthesia depth | PCI |
| Network ignition | Phase-locking, fMRI distance |
| Local vs global | Granger causality, DCM |

**기본값**: ERP P300 + frontoparietal sync as joint signature.

## 🔗 Graph
- 부모: [[Global-Workspace-Theory]] · [[Cognitive Neuroscience of Flow]]
- 변형: [[Conscious-Access]]
- Adjacent: [[Cross-Frequency Coupling (CFC)]]

## 🤖 LLM 활용
**언제**: Consciousness modeling, EEG ignition analysis, GWT-inspired AI architecture.
**언제 X**: Pure perceptual processing without report (use V1 models).

## ❌ 안티패턴
- **Ignition = activity 동치**: 매 baseline 활성과 매 구분 필요.
- **Single-area ignition**: 매 GNW는 매 distributed 정의.
- **Subjective report 의존**: 매 no-report paradigm 도입 권장.

## 🧪 검증 / 중복
- Verified (Dehaene "Consciousness and the Brain"; Mashour et al. 2020).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — Ignition signatures + EEG/PCI patterns |
