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Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-20 23:52:15 +09:00

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title

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

Anesthesia depth monitor (PCI, perturbational complexity).
Vegetative/MCS patient assessment.
Subliminal vs supraliminal masking experiments.
AI consciousness benchmarks (GWT-inspired).

💻 패턴

EEG ERP analysis (MNE)

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)

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)

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)

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

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

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

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