Version: v3.2.1
Created: February 21, 2026
Updated: April 18, 2026
Author: NCT LAB Team

中文文档

NeuroConscious Transformer (NCT) is a next-generation neuromorphic consciousness architecture that reconstructs classical neuroscience theories using Transformer technology, achieving six core theoretical innovations:

1. Attention-Based Global Workspace - Replacing simple competition with multi-head attention
2. Transformer-STDP Hybrid Learning - Globally modulated synaptic plasticity
3. Predictive Coding as Decoder - Friston's free energy = Transformer training objective
4. Multi-Modal Cross-Attention Fusion - Semantic-level multimodal integration
5. γ-Synchronization Mechanism - Gamma synchronization as update cycle
6. Φ Calculator from Attention Flow - Real-time integrated information computation

Detailed experimental data available in Paper Section 7 and experiments/results/

pip install torch numpy scipy

cd examples
python quickstart.py

cd tests
python test_basic.py

NCT/
├── __init__.py              # Package initialization
├── pyproject.toml           # Project configuration
├── requirements.txt         # Dependencies
├── README.md               # This file
├── README_CN.md            # Chinese documentation
├── .gitignore              # Git ignore rules
│
├── nct_modules/            # Core modules (9 files)
│   ├── nct_core.py         # Core config + multimodal encoder
│   ├── nct_cross_modal.py  # Cross-modal integration
│   ├── nct_workspace.py    # Attention workspace ⭐
│   ├── nct_hybrid_learning.py  # Transformer-STDP ⭐
│   ├── nct_predictive_coding.py  # Predictive coding ⭐
│   ├── nct_metrics.py      # Φ calculator + consciousness metrics ⭐
│   ├── nct_gamma_sync.py   # γ-sync mechanism
│   └── nct_manager.py      # Main controller
│
├── MCS-NCT框架理论/         # MCS multi-constraint satisfaction framework
│   ├── mcs_solver.py       # MCS core solver
│   └── mcs_nct_integration.py  # NCT integration
│
├── cats_nct/               # CATS-NCT concept abstraction variant
│   ├── core/               # Core modules
│   └── manager.py          # CATS-NCT manager
│
├── experiments/            # Experiment scripts and results
│   ├── run_all_experiments.py
│   └── results/            # JSON result data
│       ├── exp_A_free_energy.json
│       ├── exp_B_stdp.json
│       ├── exp_C_ablation.json
│       ├── exp_D_scale.json
│       ├── exp_E_attention_grading.json
│       └── exp_F_temporal_association.json
│
├── examples/               # Example code
│   └── quickstart.py       # Quick start guide
│
├── tests/                  # Test suite
│   └── test_basic.py       # Basic functionality tests
│
├── visualization/          # Visualization tools
│   └── nct_dashboard.py    # Streamlit real-time dashboard 🎨
│
├── docs/                   # Documentation
│   ├── 教育领域数据集实验论文/  # Education domain experiment papers
│   ├── 教育领域数据集实验结果/  # Education experiment results
│   └── NCT Implementation Plan.md
│
└── papers/                 # Related papers
    └── neuroconscious_paper/
        ├── NCT_arXiv.tex   # LaTeX source
        └── NCT_arXiv.pdf   # Compiled PDF

NCT provides a Streamlit-based real-time visualization dashboard featuring:

• Real-time Monitoring: Dynamic tracking of Φ value, Free Energy, and Attention Weights
• Interactive Parameters: Adjust model dimension, attention heads, γ-wave frequency, etc.
• Multi-candidate Competition Visualization: Display candidate competition in global workspace
• Bilingual Interface: English/Chinese language switching
• Data Export: Export experiment data in CSV format

# Install dependencies
pip install streamlit plotly pandas

# Launch dashboard
streamlit run visualization/nct_dashboard.py

Traditional Approach (v2.2):

# Simple lateral inhibition
cand_j.salience -= cand_i.salience * 0.1

NCT Approach (v3.0):

# Multi-Head Self-Attention (8 heads)
attn_output, attn_weights = nn.MultiheadAttention(
    embed_dim=768, num_heads=8
)(query=q, key=k, value=v)

# Head specialization:
# - Head 0-1: Visual/auditory salience detection
# - Head 2-3: Emotional value assessment
# - Head 4-5: Task relevance
# - Head 6-7: Novelty detection

Performance Gain: Consciousness selection accuracy from 75% → 92% (+23%)

Mathematical Formula:

Δw = (δ_STDP + λ·δ_attention) · η_neuromodulator

# δ_STDP: Classic STDP (local temporal correlation)
δ_STDP = A₊·exp(-Δt/τ₊) if Δt > 0
       = -A₋·exp(Δt/τ₋) if Δt < 0

# δ_attention: Attention gradient (global semantics)
δ_attention = ∂Loss/∂W

# η_neuromodulator: Neurotransmitter modulation
η = 1.0 + w_DA·DA + w_5HT·5HT + w_NE·NE + w_ACh·ACh

Convergence Speed: 1000 cycles → 200 cycles (5× improvement)

Theoretical Unification Proof:

# Friston's variational free energy
F = E_q(z)[ln q(z) - ln p(s,z)]

# Expanded:
F = CrossEntropy(predictions, actual)  # Prediction error
    + KL(q||p)                         # Regularization term

# Transformer Decoder training loss:
Loss = CrossEntropy(next_token_pred, actual_next)
       + L2_regularization(weights)

# Therefore:
Free Energy ≈ Transformer Loss

Avoiding IIT's NP-hard Problem:

# Traditional IIT: O(2^n) complexity
Φ = I_total - min_partition[I_A + I_B]

# NCT approximation: O(n²) complexity
class PhiFromAttention(nn.Module):
    def compute_phi(self, attention_maps):
        I_total = mutual_information(attn_matrix)
        min_partition_mi = find_min_partition(attn_matrix)
        phi = max(0.0, I_total - min_partition_mi)
        return np.tanh(phi / max(1.0, L * 0.1))

Φ Value Improvement: 0.3 → 0.7 (2.3×)

MCS reframes consciousness modeling as a multi-constraint optimization problem: instead of asking "what is consciousness?", it asks "what constraints must a system satisfy to be conscious?" This operational approach enables quantitative measurement of consciousness levels.

Core Formulation:

C(t) = argmin_S [ Σᵢ wᵢ·Vᵢ(S,t) ]    # Optimal conscious state
Consciousness Level = 1/(1+J)          # J = weighted constraint violation

Key Results: DAiSEE dataset 5-fold CV R²=0.164 (121% improvement over NCT Φ baseline)

📁 Core files: MCS-NCT框架理论/mcs_solver.py, mcs_nct_integration.py
📄 Paper: Submitted to IEEE Transactions on Affective Computing

CATS-NCT fuses Concept Abstraction (CA) dual-module architecture with NCT's neuroscience-grounded mechanisms. It extends consciousness modeling from perceptual to conceptual consciousness—stable, communicable mental representations.

📁 Core files: cats_nct/core/, cats_nct/manager.py
🚧 Status: Under active development

Research Question: Can NCT architecture effectively monitor cognitive states in educational settings?

The critical discovery: Φ (integrated information) becomes a valid cognitive state marker when computed from deep learning (EEGNet) features rather than traditional spectral features. This bridges IIT theory with practical educational applications.

• EEGNet classification: F1=0.62 (vs SVM baseline F1=0.39)
• Datasets: MEMA (EEG), DAiSEE (Video), FER2013, EdNet
• 📄 Paper: Submitted to IEEE Transactions on Affective Computing

Note: v3.1 measured data from experiments/results/, detailed statistics in Paper Tables 2-6

# Clone repository
git clone https://github.com/somebody2026-peer/nct.git
cd nct

# Install dependencies
pip install -r requirements.txt

# Install development dependencies (optional)
pip install pytest black ruff mypy

# Run tests
pytest tests/

# Code formatting
black .
ruff check .

# Run all experiments (~30 minutes)
python experiments/run_all_experiments.py

# View results
ls experiments/results/

# Run real-time visualization dashboard
streamlit run visualization/nct_dashboard.py

from nct_modules import NCTManager, NCTConfig

# Custom configuration
config = NCTConfig(
    n_heads=12,      # Increase workspace capacity
    n_layers=6,      # Increase cortical layers
    d_model=1024,    # Increase representation dimension
)

# Create manager
manager = NCTManager(config)

# Run experiment
for trial in range(100):
    sensory = generate_sensory_data()
    state = manager.process_cycle(sensory)
    analyze(state)

1. Whittington & Bogacz (2017). An approximation of the error backpropagation algorithm in a predictive coding network with local Hebbian synaptic plasticity. Neural Computation
2. Millidge, Tschantz & Buckley (2022). Predictive coding approximates backprop along arbitrary computation graphs. Neural Computation
3. Vaswani et al. (2017). Attention Is All You Need
4. Dehaene & Changeux (2011). Experimental and theoretical approaches to conscious processing
5. Friston (2010). The free-energy principle: a unified brain theory
6. Tononi (2008). Consciousness as integrated information
7. Bi & Poo (1998). Synaptic modifications by STDP
8. Fries (2005). Gamma oscillations and communication

• NCT_arXiv.pdf - Latest preprint (with complete experimental validation)
• NCT_arXiv.tex - LaTeX source files

• ✅ Fixed Φ computation mathematical defect: replaced single-sample outer product (rank=1) with batch covariance estimation
• ✅ Added eigenvalue entropy and energy concentration as auxiliary Φ metrics
• ✅ Fixed PredictiveHierarchy GPU device mismatch error (causal mask now supports device parameter)
• ✅ Enhanced Experiment 9: 12 perturbation levels (was 5), 1000 samples (was 200)
• ✅ Added 5 control groups: Gaussian noise, Shuffle features, Random classifier, Frozen intermediate, Temperature scaling
• ✅ H4a hypothesis fully passed: all perturbations significantly reduce Φ (p<0.01, r=-0.78~-0.93)
• ⚠️ Breaking change: All experiments using PhiFromAttention will produce different results

• ✅ Added MCS (Multi-Constraint Satisfaction) consciousness modeling framework
• ✅ Education V4 experiments: EEGNet features enable Φ discrimination (p=0.0003, d=0.524)
• ✅ Two papers submitted to IEEE Transactions on Affective Computing
• ✅ Project restructuring: cleaned root directory, organized scripts
• ✅ Unified version numbers across all config files
• ✅ Fixed GitHub URLs in pyproject.toml
• ✅ Enhanced .gitignore for security (.env) and organization (/temp/)

• ✅ Completed all 6 core experiment validations
• ✅ Added statistical significance analysis (t-test, Cohen's d)
• ✅ Optimized Φ computation method (random bisection, r > 0.93)
• ✅ Integrated "Integration Challenges" discussion
• ✅ Added error bar visualization
• ✅ Established open-source code repository

• 🎉 Initial release

Issues and Pull Requests are welcome!

• Follow PEP 8
• Type annotations required
• Unit test coverage > 80%
• Use Black for code formatting

MIT License

Thanks to all consciousness neuroscience researchers and AI pioneers.

🧠 Let's explore the mysteries of consciousness together!