# Example of good coding style
def compute_consciousness_parameters(
    neural_state: np.ndarray, 
    connectivity_matrix: np.ndarray,
    thresholds: Dict[str, float]
) -> ConsciousnessParameters:
    """Compute all five consciousness parameters from neural state.
    
    Parameters
    ----------
    neural_state : np.ndarray
        Current activation state of neurons (shape: n_neurons,)
    connectivity_matrix : np.ndarray  
        Weighted adjacency matrix (shape: n_neurons, n_neurons)
    thresholds : Dict[str, float]
        Critical thresholds for each parameter
        
    Returns
    -------
    ConsciousnessParameters
        Named tuple containing all five parameter values
        
    Examples
    --------
    >>> state = np.random.rand(65536)
    >>> weights = np.random.randn(65536, 65536) * 0.1
    >>> thresholds = {'phi': 0.65, 'connectivity': 15.0}
    >>> params = compute_consciousness_parameters(state, weights, thresholds)
    """

// Example shader structure
#version 430
layout(local_size_x = 32, local_size_y = 32) in;

// Input textures
layout(binding = 0) uniform sampler2D neural_state_tex;
layout(binding = 1) uniform sampler2D weight_tex;

// Output buffer
layout(std430, binding = 2) buffer Output {
    vec4 output_data[];
};

void main() {
    ivec2 coord = ivec2(gl_GlobalInvocationID.xy);
    
    // HNS addition with carry propagation
    vec4 a = texelFetch(neural_state_tex, coord, 0);
    vec4 b = texelFetch(weight_tex, coord, 0);
    
    // Perform hierarchical arithmetic
    vec4 result = hns_add(a, b);
    
    output_data[gl_GlobalInvocationID.x] = result;
}

# Example test structure
def test_hns_precision():
    """Test HNS accumulative precision over 1M iterations."""
    # Initialize test value
    initial_value = np.array([0.000001, 0, 0, 0], dtype=np.float32)
    
    # Perform accumulative addition
    result = initial_value.copy()
    for _ in range(1000000):
        result = hns_add(result, initial_value)
    
    # Verify perfect precision
    expected = np.array([1.0, 0, 0, 0], dtype=np.float32)
    assert np.allclose(result, expected, rtol=0, atol=0), \
        "HNS should maintain perfect precision"

def test_consciousness_emergence():
    """Test that consciousness parameters emerge within expected timeframe."""
    engine = ConsciousnessEngine(neurons=65536)
    results = engine.simulate(epochs=10000, monitor_parameters=True)
    
    # Check emergence timing
    assert 5000 <= results.emerged_at_epoch <= 8000, \
        "Consciousness should emerge within expected timeframe"
    
    # Check parameter stability
    assert results.is_stable(), \
        "Parameters should stabilize after emergence"

# Run performance tests
python benchmarks/hns_throughput.py --size 1000000 --iterations 100

# Profile GPU utilization
nvidia-smi dmon -s pucvmet -d 10  # Monitor during simulation

# Compare with baselines
python benchmarks/framework_comparison.py

## Description
Brief description of changes and motivation.

## Type of Change
- [ ] Bug fix
- [ ] New feature  
- [ ] Breaking change
- [ ] Documentation update
- [ ] Performance improvement

## Testing
- [ ] Unit tests pass
- [ ] Integration tests pass
- [ ] Performance benchmarks included
- [ ] Consciousness validation tested

## Related Issues
Closes #123

**Environment**
- OS: Ubuntu 22.04
- GPU: NVIDIA RTX 3090
- Python: 3.10.12
- NeuroCHIMERA: v1.0.0

**Problem**
Consciousness parameters not emerging in simulation with 32K neurons.

**Code**
```python
engine = ConsciousnessEngine(neurons=32768)
results = engine.simulate(epochs=5000)
print(results.emerged_at_epoch)  # Returns None

## 🏆 Recognition

Contributors will be recognized in our:
- **README.md**: List of contributors
- **Release Notes**: Significant contributions highlighted
- **Research Papers**: Co-authorship for substantial research contributions
- **Project Website**: Contributor profiles and impact stories

## 📜 Code of Conduct

We are committed to providing a welcoming and inclusive environment for all contributors. Please read our [Code of Conduct](CODE_OF_CONDUCT.md) before participating.

### Key Principles
- **Respect**: Treat all contributors with respect and professionalism
- **Inclusivity**: Welcome contributions from people of all backgrounds and experience levels
- **Collaboration**: Work together to build the best possible framework
- **Quality**: Maintain high standards for code, documentation, and research

## 🙏 Thank You

Thank you for contributing to NeuroCHIMERA! Your efforts help advance the scientific understanding of consciousness and enable new possibilities in neuromorphic computing.

Together, we're building a framework that bridges theoretical neuroscience with practical GPU computing, opening new avenues for consciousness research and artificial intelligence.

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*This contributing guide is itself open to contributions! Please suggest improvements via pull requests.*