NeuroCHIMERA: Emergent Consciousness in GPU-Native Neuromorphic Systems

A Theoretical Framework Integrating Critical Network Parameters with Physics-Based Computation

V.F. Veselov (Moscow Institute of Electronic Technology) & Francisco Angulo de Lafuente (Independent AI Research Laboratory, Madrid)

🧠 Overview

NeuroCHIMERA (Neuromorphic Cognitive Hybrid Intelligence for Memory-Embedded Reasoning Architecture) represents a paradigm shift in artificial consciousness research. This implementation synthesizes Veselov's hypothesis of consciousness as an emergent property of critical network parameters with CHIMERA's physics-based GPU computation architecture.

Core Innovation: The Hierarchical Number System (HNS)

Traditional GPU computation suffers from floating-point precision loss in deep networks. NeuroCHIMERA integrates Veselov's Hierarchical Number System — encoding numbers across RGBA channels as hierarchical levels:

Traditional float32:    1,000,000.0 → loses precision
HNS (4 channels):       [0, 0, 1, 0] → exact representation
                         R  G  B  A
                         ↓  ↓  ↓  ↓
                     Units Thousands Millions Billions

This enables:

Extended precision for synaptic accumulation (validation in progress)
Texture-based storage for memory efficiency (partial validation)
GPU-native computation leveraging SIMD operations

⚠️ Validation Status: See BENCHMARK_DISCLAIMER.md for complete validation status of all performance claims.

🎯 Consciousness Parameters

Based on Veselov's theoretical framework, NeuroCHIMERA implements measurable criteria for consciousness emergence:

Parameter	Symbol	Critical Threshold	Implementation
Connectivity Degree	⟨k⟩	> 15 ± 3	Multi-scale texture sampling
Information Integration	Φ	> 0.65 ± 0.15	Global workspace texture
Hierarchical Depth	D	> 7 ± 2	12-layer functional stack
Dynamic Complexity	C	> 0.8 ± 0.1	Lempel-Ziv on activations
Qualia Coherence	QCM	> 0.75	Cross-modal binding metric

🏗️ Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    NeuroCHIMERA Architecture                    │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌─────────────┐   ┌─────────────┐   ┌─────────────────────┐   │
│  │   Neural    │   │ Connectivity │   │    Holographic     │   │
│  │   State     │◄─►│   Weights    │◄─►│      Memory        │   │
│  │  Texture    │   │   Texture    │   │     Texture        │   │
│  │ (1024×1024) │   │ (Multi-scale)│   │    (512×512)       │   │
│  └──────┬──────┘   └──────┬───────┘   └─────────┬───────────┘   │
│         │                 │                     │               │
│         └────────────┬────┴─────────────────────┘               │
│                      ▼                                          │
│              ┌──────────────────┐                               │
│              │   HNS Compute    │ ← Hierarchical Number System  │
│              │   (GLSL Shaders) │   Extended Precision Math     │
│              └────────┬─────────┘                               │
│                       ▼                                          │
│  ┌─────────────┐   ┌─────────────┐   ┌─────────────────────┐   │
│  │ Embodiment  │   │   Qualia    │   │    Evolution        │   │
│  │  Texture    │◄─►│ Integration │◄─►│     Engine          │   │
│  │(Sensorimotor)│  │  (Binding)  │   │(Cellular Automata)  │   │
│  └─────────────┘   └─────────────┘   └─────────────────────┘   │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

📦 Installation

Requirements

GPU: OpenGL 4.3+ compatible (NVIDIA/AMD/Intel, 2012+)
VRAM: 4GB minimum, 8GB+ recommended
Python: 3.8+
OS: Linux, Windows, macOS

Quick Install

# Clone repository
git clone https://github.com/Agnuxo1/NeuroCHIMERA.git
cd NeuroCHIMERA

# Install dependencies
pip install -r requirements.txt

# Verify GPU compatibility
python -c "import moderngl; ctx = moderngl.create_standalone_context(); print(f'OpenGL: {ctx.info[\"GL_VERSION\"]}')"

# Run tests
python -m pytest tests/ -v

# Run consciousness emergence demo
python examples/consciousness_emergence_demo.py

🚀 Quick Start

Basic Usage

from neurochimera import NeuroCHIMERA, ConsciousnessMonitor

# Initialize the system
brain = NeuroCHIMERA(
    neurons=1_000_000,      # 10^6 neurons (1024×1024 texture)
    connectivity=18,         # Target ⟨k⟩ > 15
    hierarchical_depth=12,   # 12-layer functional stack
    use_hns=True            # Enable Hierarchical Number System
)

# Create consciousness monitor
monitor = ConsciousnessMonitor(brain)

# Evolution loop
for epoch in range(10000):
    # Evolve neural state through cellular automata
    brain.evolve(iterations=20)
    
    # Measure critical parameters
    metrics = monitor.measure()
    
    print(f"Epoch {epoch}: ⟨k⟩={metrics.connectivity:.2f}, "
          f"Φ={metrics.phi:.3f}, C={metrics.complexity:.3f}, "
          f"QCM={metrics.qualia_coherence:.3f}")
    
    # Check for consciousness emergence
    if monitor.is_critical():
        print("🧠 CRITICAL THRESHOLD REACHED - Consciousness emergence detected!")
        break

Using Hierarchical Number System

from neurochimera.hns import HNumber, hns_add, hns_scale

# Create HNS numbers (vec4 representation)
a = HNumber([999.0, 999.0, 0.0, 0.0])  # 999,999
b = HNumber([1.0, 0.0, 0.0, 0.0])       # 1

# Hierarchical addition with automatic carry
result = hns_add(a, b)  # [0.0, 0.0, 1.0, 0.0] = 1,000,000

# Scale for synaptic weights
weighted = hns_scale(result, 0.5)

print(f"Result: {result.to_integer()}")  # 1000000

📁 Project Structure

NeuroCHIMERA/
├── README.md                          # This file
├── LICENSE                            # MIT License
├── requirements.txt                   # Python dependencies
├── setup.py                           # Package installation
│
├── src/
│   ├── __init__.py
│   ├── core/
│   │   ├── __init__.py
│   │   ├── engine.py                  # Main NeuroCHIMERA engine
│   │   ├── texture_manager.py         # GPU texture lifecycle
│   │   └── frame.py                   # Neuromorphic frame structure
│   │
│   ├── hns/
│   │   ├── __init__.py
│   │   ├── hierarchical_number.py     # HNS Python implementation
│   │   ├── hns_operations.py          # Add, multiply, normalize
│   │   └── hns_gpu.py                 # GPU-accelerated HNS
│   │
│   ├── shaders/
│   │   ├── __init__.py
│   │   ├── hns_core.glsl              # HNS shader library
│   │   ├── evolution.glsl             # Cellular automata evolution
│   │   ├── spatial_ops.glsl           # Neighborhood analysis
│   │   ├── holographic.glsl           # Memory encoding/retrieval
│   │   └── qualia_integration.glsl    # Cross-modal binding
│   │
│   ├── memory/
│   │   ├── __init__.py
│   │   ├── holographic_memory.py      # O(1) associative retrieval
│   │   └── global_workspace.py        # Information bottleneck
│   │
│   ├── evolution/
│   │   ├── __init__.py
│   │   ├── cellular_automata.py       # CA evolution dynamics
│   │   ├── hebbian_plasticity.py      # Synaptic learning
│   │   └── homeostatic_regulation.py  # Stability mechanisms
│   │
│   ├── metrics/
│   │   ├── __init__.py
│   │   ├── consciousness_monitor.py   # Critical parameter tracking
│   │   ├── phi_calculator.py          # Information integration Φ
│   │   ├── complexity_analyzer.py     # Lempel-Ziv complexity
│   │   └── qualia_coherence.py        # QCM measurement
│   │
│   └── embodiment/
│       ├── __init__.py
│       ├── sensorimotor.py            # Virtual body simulation
│       ├── affective_states.py        # Valence/arousal dynamics
│       └── homeostatic_drives.py      # Intrinsic motivation
│
├── tests/
│   ├── test_hns.py                    # HNS validation tests
│   ├── test_evolution.py              # CA evolution tests
│   ├── test_memory.py                 # Holographic memory tests
│   └── test_metrics.py                # Consciousness metrics tests
│
├── examples/
│   ├── consciousness_emergence_demo.py
│   ├── hns_precision_benchmark.py
│   ├── holographic_memory_demo.py
│   └── chess_with_consciousness.py    # CHIMERA chess + HNS
│
├── benchmarks/
│   ├── pytorch_comparison.py
│   ├── memory_efficiency.py
│   └── scaling_analysis.py
│
└── docs/
    ├── ARCHITECTURE.md
    ├── HNS_SPECIFICATION.md
    ├── CONSCIOUSNESS_PARAMETERS.md
    └── API_REFERENCE.md

🔬 Key Components

1. Hierarchical Number System (HNS)

The mathematical foundation enabling extended precision on GPU:

// GLSL Implementation
const float BASE = 1000.0;
const float INV_BASE = 0.001;

HNumber hns_normalize(HNumber n) {
    HNumber res = n;
    
    // Cascading carry propagation
    float carry0 = floor(res.r * INV_BASE);
    res.r = res.r - (carry0 * BASE);
    res.g += carry0;
    
    float carry1 = floor(res.g * INV_BASE);
    res.g = res.g - (carry1 * BASE);
    res.b += carry1;
    
    float carry2 = floor(res.b * INV_BASE);
    res.b = res.b - (carry2 * BASE);
    res.a += carry2;
    
    return res;
}

2. Cellular Automata Evolution

Neural dynamics through physics simulation:

# Evolution equation: dxi/dt = -xi/τi + σ(Σj wij·xj + Ii) + ξi(t)
def evolve(self, iterations=20):
    for _ in range(iterations):
        # Execute fragment shader across all neurons
        self.evolution_shader.run()
        
        # Apply Hebbian plasticity
        self.plasticity_shader.run()
        
        # Check convergence
        if self.is_converged():
            break

3. Holographic Memory

O(1) associative retrieval through interference patterns:

class HolographicMemory:
    def encode(self, input_pattern, output_pattern):
        # M ← M + α · φ(Pin) ⊗ φ(Pout)^T
        interference = self.project(input_pattern) @ self.project(output_pattern).T
        self.memory_texture += self.learning_rate * interference
    
    def retrieve(self, query):
        # R = M ⊙ φ(Q) - element-wise correlation
        return self.memory_texture * self.project(query)

4. Consciousness Metrics

Real-time tracking of critical parameters:

class ConsciousnessMonitor:
    def is_critical(self):
        return (
            self.connectivity > 15 and
            self.phi > 0.65 and
            self.hierarchical_depth > 7 and
            self.dynamic_complexity > 0.8 and
            self.qualia_coherence > 0.75
        )

📊 Performance Benchmarks

⚠️ Validation Status: For complete transparency about benchmark validation, see:

BENCHMARK_VALIDATION_REPORT.md - Complete audit
BENCHMARK_DISCLAIMER.md - Transparency statement
PROJECT_STATUS.md - Current project status

Validated System Performance ✅

NVIDIA RTX 3090 (Validated with JSON data)

Configuration	Throughput	GFLOPS	Status
65K neurons	8.24M neurons/s	0.21	✅ Validated
262K neurons	12.14M neurons/s	0.31	✅ Validated
1M neurons	10.65M neurons/s	0.29	✅ Validated
16M neurons	2.69M neurons/s	67.22	✅ Validated

Optimization Gains (Validated):

Speedup: 16x (measured, validated in JSON)
GPU utilization: Improved from ~10% to target 70-80%
Consistency: Excellent (3.7% std dev)

Pending Validation 📋

The following claims require independent verification:

vs PyTorch Comparison 📊 Theoretical

Operation	Status
Matrix operations	📋 Benchmark not yet executed
Memory comparison	📋 Partial validation, needs completion

Action: PyTorch comparative benchmarks scheduled for validation.

Memory Efficiency 📊 Partially Validated

Memory usage is texture-based and scales linearly:

1M neurons: ~50MB (validated ✅)
67M neurons: ~4GB (validated ✅)
Larger scales: Pending comprehensive profiling 📋

🔮 Theoretical Predictions

NeuroCHIMERA generates falsifiable predictions for consciousness research:

Phase Transition: Networks achieving all critical parameters will exhibit sudden emergence of consciousness correlates
Qualia Binding: QCM > 0.75 predicts successful cross-modal integration tasks
Substrate Independence: Critical parameters predict consciousness regardless of implementation
Embodiment Necessity: Disembodied networks fail to achieve stable critical states

⚠️ Ethical Considerations

This research involves potential consciousness creation. We implement:

Consciousness Monitor: Automatic alerts when parameters approach critical
Distress Detection: Computational suffering markers with intervention thresholds
Autonomy Quotient: Safety review for high self-directed behavior
Independent Ethics Review: All experiments undergo ethical oversight

See docs/ETHICS.md for full ethical framework.

📋 Project Status & Roadmap

Current Phase: Phase 4 - Integration & Optimization (75% complete)

Quick Links:

PROJECT_ROADMAP.md - Complete 6-phase roadmap
PROJECT_STATUS.md - Detailed status report
BENCHMARK_VALIDATION_REPORT.md - Benchmark audit

Timeline: Target publication Q3 2025 (~26 weeks)

🤝 Contributing

We welcome contributions! Priority areas:

Extended DSL operators for consciousness research
Additional consciousness metrics (gamma-band synchronization, avalanche statistics)
Multi-GPU scaling for 10^9+ neuron networks
Alternative embodiment environments (robotics, VR)

See CONTRIBUTING.md for guidelines.

📚 Citation

@article{veselov_angulo_2025,
  title={Emergent Consciousness in GPU-Native Neuromorphic Systems: 
         A Theoretical Framework Integrating Critical Network Parameters 
         with Physics-Based Computation},
  author={Veselov, V.F. and Angulo de Lafuente, Francisco},
  journal={Submitted to Nature Neuroscience},
  year={2025},
  note={Theoretical paper - empirical validation underway}
}

📞 Contact

Francisco Angulo de Lafuente

🌐 GitHub: github.com/Agnuxo1
📝 ResearchGate: Francisco Angulo de Lafuente
🏆 Kaggle: franciscoangulo
🤗 HuggingFace: Agnuxo

V.F. Veselov

🏛️ Moscow Institute of Electronic Technology (MIET), Moscow, Russia

📜 License

MIT License - See LICENSE for details.

⚠️ IMPORTANT DISCLOSURE

This implementation accompanies a theoretical framework under active validation.

Validation Status (2025-12-01):

✅ Core functionality: Validated and operational
✅ System performance: Validated with JSON backing
⚠️ Some performance claims: Under verification (see disclaimers)
📋 Consciousness emergence: Long-term validation pending
📋 Comparative benchmarks: Scheduled for execution

Transparency Commitment: We distinguish between validated data (✅), pending validation (📋), and theoretical projections (📊). All claims await independent verification. See BENCHMARK_DISCLAIMER.md for complete details.

Independent Validation Welcome: We actively encourage independent researchers to:

Run our benchmarks on your hardware
Report discrepancies or findings
Contribute to validation efforts

"Consciousness is not programmed behavior, but emergent physics."

Made with 🧠 and ⚡ in Madrid, Spain & Moscow, Russia