2nd/10_Wiki/Topic_General/From_Topics_Root/Global-Neuronal-Workspace.md

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id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-global-neuronal-workspace	Global Neuronal Workspace	10_Wiki/Topics	verified	self	GNW GNWT global-workspace-theory	none	A	0.85	applied	neuroscience consciousness cognitive-architecture		2026-05-10	pending	language framework python neuro-cognitive

Global Neuronal Workspace

매 한 줄

"매 conscious access = workspace 로 의 broadcast". Dehaene & Changeux 의 GNWT — 매 prefrontal-parietal long-range neuron 의 ignition 시 정보 의 brain-wide 의 broadcast 가 발생, 이것이 conscious experience 의 neural correlate. 매 LLM/AGI architecture 의 inspiration source.

매 핵심

매 정의

• Workspace neurons: long-range pyramidal cells (layer 2/3 in PFC, parietal).
• Ignition: 매 sub-threshold processing 의 supra-threshold broadcast 로 의 nonlinear transition (~300ms post-stimulus, P3b component).
• Module ↔ workspace: 매 specialist module 의 결과 의 workspace 로 의 winner-take-all entry.

매 측거

• P3b ERP — ignition signature.
• Long-distance gamma synchrony.
• fMRI 의 prefrontal-parietal co-activation under conscious report task.

매 응용

1. Anesthesia monitoring — workspace breakdown 의 measure.
2. Vegetative state diagnosis — Owen et al. mental imagery paradigm.
3. AI architecture — Bengio's Consciousness Prior, Goyal's Coordination via Attention.
4. LLM analysis — 매 attention 의 workspace 로 의 mapping.

💻 패턴

Workspace-style Coordination Layer

import torch
import torch.nn as nn

class GlobalWorkspace(nn.Module):
    """매 specialist module 의 의 winner-take-all broadcast."""
    def __init__(self, n_modules: int, dim: int, k_winners: int = 4):
        super().__init__()
        self.attn = nn.MultiheadAttention(dim, num_heads=8, batch_first=True)
        self.k = k_winners

    def forward(self, module_outputs: torch.Tensor) -> torch.Tensor:
        # module_outputs: (B, n_modules, dim)
        scores = module_outputs.norm(dim=-1)  # (B, n_modules)
        topk = scores.topk(self.k, dim=-1).indices
        gather_idx = topk.unsqueeze(-1).expand(-1, -1, module_outputs.size(-1))
        winners = module_outputs.gather(1, gather_idx)  # (B, k, dim)
        broadcast, _ = self.attn(winners, winners, winners)
        return broadcast.mean(dim=1)

Ignition Detector (P3b-like)

import numpy as np

def detect_ignition(eeg: np.ndarray, fs: int = 1000, electrode_pz: int = 31):
    """Pz 의 250-450ms window 의 amplitude → ignition flag."""
    window = eeg[electrode_pz, int(0.25 * fs):int(0.45 * fs)]
    baseline = eeg[electrode_pz, :int(0.1 * fs)]
    p3b = window.mean() - baseline.mean()
    return p3b > 3 * baseline.std(), p3b

Consciousness Prior (Bengio)

class ConsciousnessPrior(nn.Module):
    """매 sparse high-level state z_t 의 의 dependency 의 sparse factor graph 의 학습."""
    def __init__(self, dim, k_active=5):
        super().__init__()
        self.encoder = nn.Linear(dim, dim)
        self.k = k_active

    def forward(self, h):
        z = self.encoder(h)
        topk_vals, topk_idx = z.abs().topk(self.k, dim=-1)
        mask = torch.zeros_like(z).scatter_(-1, topk_idx, 1.0)
        return z * mask

Long-range Gamma Coupling

from scipy.signal import hilbert

def plv(x, y):
    """Phase-locking value — 매 long-distance gamma synchrony proxy."""
    px = np.angle(hilbert(x))
    py = np.angle(hilbert(y))
    return np.abs(np.exp(1j * (px - py)).mean())

매 결정 기준

상황	Approach
Conscious report task	P3b + gamma coupling
AI coordination	Workspace + sparse top-k
Anesthesia depth	Workspace breakdown index
Disorder of consciousness	Active paradigm (mental imagery)

기본값: GNW + IIT 의 complementary — GNW 의 access consciousness, IIT 의 phenomenal.

🔗 Graph

• 부모: Cognitive-Architecture
• 변형: Global-Workspace-Theory (Baars) · GNWT (Dehaene)
• Adjacent: Predictive-Processing

🤖 LLM 활용

언제: cognitive architecture design / consciousness 관련 신경과학 정리 / multi-agent coordination. 언제 X: 매 phenomenal consciousness (qualia) 의 explanation — IIT 의 영역.

❌ 안티패턴

• GNW = consciousness fully: 매 access vs phenomenal 의 conflate.
• Workspace = single bottleneck: 매 실제로 distributed competition.
• PFC = consciousness seat: 매 posterior hot zone view 의 ignore.

🧪 검증 / 중복

• Verified (Dehaene 2014 Consciousness and the Brain, Mashour et al. 2020 Neuron).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — GNW + AI architecture inspiration 패턴