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