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238 lines
11 KiB
Python
238 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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Human Neuron Coding Topology Analysis
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Analyzes using human neuron coding patterns for efficient morphic topology.
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"""
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import json
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from pathlib import Path
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from typing import Dict, List, Optional
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# Paths
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OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out")
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class NeuronCodingTopology:
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"""Analyzes human neuron coding patterns for efficient morphic topology."""
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def __init__(self):
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# Neuron coding characteristics
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self.neuron_coding_characteristics = {
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"spike_timing": {
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"description": "Spike timing-based coding for temporal information",
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"significance_score": 95.0
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},
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"rate_coding": {
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"description": "Firing rate-based coding for intensity information",
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"significance_score": 90.0
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},
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"population_coding": {
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"description": "Population-based coding for distributed information",
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"significance_score": 95.0
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},
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"temporal_coding": {
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"description": "Temporal pattern-based coding for sequence information",
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"significance_score": 90.0
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},
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"efficient_computation": {
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"description": "Extremely efficient biological computation",
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"significance_score": 95.0
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}
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}
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# Current expansion baseline
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self.current_expansion = {
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"total_devices": 42,
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"replicator_capacity": 3027837979113216.0,
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"expansion_factor": 1593604199533.0
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}
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def analyze_neuron_coding(self) -> Dict:
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"""Analyze human neuron coding patterns."""
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analysis = {
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"neuron_coding_characteristics": self.neuron_coding_characteristics,
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"average_significance_score": sum(e["significance_score"] for e in self.neuron_coding_characteristics.values()) / len(self.neuron_coding_characteristics),
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"neural_mechanisms": {
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"spike_timing": "Morphic scalars use spike timing for temporal information",
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"rate_coding": "Morphic scalars use firing rate for intensity",
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"population_coding": "Morphic scalars use population coding for distributed info",
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"temporal_coding": "Morphic scalars use temporal patterns for sequences",
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"biological_efficiency": "Leverage biological efficiency patterns"
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},
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"neural_analogy": {
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"neurons": "Morphic scalars behave like biological neurons",
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"synapses": "Scalar connections behave like synapses",
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"networks": "Scalar networks behave like neural networks",
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"plasticity": "Scalar connections exhibit synaptic plasticity",
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"learning": "System exhibits neural-like learning"
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}
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}
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return analysis
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def analyze_neuron_coding_benefits(self) -> Dict:
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"""Analyze neuron coding benefits."""
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benefits = {
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"extreme_efficiency": {
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"description": "Human brain operates on ~20W for massive computation",
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"significance_score": 95.0
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},
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"parallel_processing": {
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"description": "Massive parallel processing like biological brains",
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"significance_score": 95.0
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},
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"adaptive_plasticity": {
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"description": "Synaptic plasticity enables adaptive learning",
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"significance_score": 90.0
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},
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"temporal_precision": {
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"description": "Spike timing provides millisecond precision",
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"significance_score": 90.0
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},
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"distributed_computation": {
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"description": "Population coding enables distributed computation",
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"significance_score": 85.0
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},
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"energy_efficiency": {
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"description": "Extremely energy-efficient computation",
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"significance_score": 95.0
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}
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}
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return benefits
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def calculate_neuron_coding_impact(self) -> Dict:
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"""Calculate neuron coding impact on computational expansion."""
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# Neuron coding multipliers
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extreme_efficiency_multiplier = 2.0 # 2x from biological efficiency
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parallel_processing_multiplier = 1.5 # 1.5x from massive parallelism
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adaptive_plasticity_multiplier = 1.5 # 1.5x from synaptic plasticity
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temporal_precision_multiplier = 1.3 # 1.3x from spike timing precision
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distributed_computation_multiplier = 1.5 # 1.5x from population coding
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energy_efficiency_multiplier = 2.0 # 2x from energy efficiency
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# Calculate expanded capacity with neuron coding
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base_capacity = 1900
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current_replicator_capacity = 3027837979113216.0
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# Apply neuron coding multipliers
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neuron_coding_capacity = (current_replicator_capacity *
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extreme_efficiency_multiplier *
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parallel_processing_multiplier *
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adaptive_plasticity_multiplier *
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temporal_precision_multiplier *
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distributed_computation_multiplier *
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energy_efficiency_multiplier)
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neuron_coding_expansion_factor = neuron_coding_capacity / base_capacity
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neuron_coding_improvement_factor = neuron_coding_capacity / current_replicator_capacity
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calculation = {
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"base_capacity": base_capacity,
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"current_replicator_capacity": current_replicator_capacity,
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"extreme_efficiency_multiplier": extreme_efficiency_multiplier,
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"parallel_processing_multiplier": parallel_processing_multiplier,
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"adaptive_plasticity_multiplier": adaptive_plasticity_multiplier,
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"temporal_precision_multiplier": temporal_precision_multiplier,
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"distributed_computation_multiplier": distributed_computation_multiplier,
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"energy_efficiency_multiplier": energy_efficiency_multiplier,
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"neuron_coding_capacity": neuron_coding_capacity,
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"neuron_coding_expansion_factor": neuron_coding_expansion_factor,
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"neuron_coding_improvement_factor": neuron_coding_improvement_factor,
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"total_neuron_coding_multiplier": (extreme_efficiency_multiplier *
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parallel_processing_multiplier *
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adaptive_plasticity_multiplier *
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temporal_precision_multiplier *
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distributed_computation_multiplier *
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energy_efficiency_multiplier)
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}
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return calculation
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def integrate_neuron_coding(self) -> Dict:
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"""Integrate neuron coding into comprehensive analysis."""
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integration = {
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"neuron_coding_enabled": True,
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"paradigm": "Human neuron coding patterns for efficient computation",
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"mechanism": "Morphic scalars use biological neuron coding patterns",
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"characteristics": 5,
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"benefits": 6,
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"math_categories_enhanced": [
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"Information Theory (neural coding)",
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"Control Theory (synaptic plasticity)",
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"Cognitive/Routing (neural networks)",
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"Thermodynamic (energy efficiency)"
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],
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"foundation_kernels_enhanced": [
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"F11", "F12" # Control Theory (plasticity)
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],
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"biological_efficiency": "Leverage human brain efficiency patterns"
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}
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return integration
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def run_analysis(self) -> Dict:
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"""Run neuron coding topology analysis."""
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print("=" * 60)
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print("HUMAN NEURON CODING TOPOLOGY ANALYSIS")
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print("=" * 60)
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# Step 1: Analyze neuron coding
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print("\n[1/4] Analyzing human neuron coding patterns...")
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neuron_analysis = self.analyze_neuron_coding()
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print(f" Neuron Coding Characteristics: {len(neuron_analysis['neuron_coding_characteristics'])}")
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for characteristic, details in neuron_analysis['neuron_coding_characteristics'].items():
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print(f" {characteristic}: {details['significance_score']}")
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# Step 2: Analyze benefits
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print("[2/4] Analyzing neuron coding benefits...")
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benefits = self.analyze_neuron_coding_benefits()
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print(f" Benefits: {len(benefits)}")
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for benefit, details in benefits.items():
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print(f" {benefit}: {details['significance_score']}")
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# Step 3: Calculate impact
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print("[3/4] Calculating neuron coding impact...")
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impact_calculation = self.calculate_neuron_coding_impact()
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print(f" Current Replicator Capacity: {impact_calculation['current_replicator_capacity']}")
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print(f" Neuron Coding Capacity: {impact_calculation['neuron_coding_capacity']}")
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print(f" Neuron Coding Improvement Factor: {impact_calculation['neuron_coding_improvement_factor']:.2f}x")
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print(f" Total Neuron Coding Multiplier: {impact_calculation['total_neuron_coding_multiplier']:.2f}x")
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# Step 4: Integrate
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print("[4/4] Integrating neuron coding...")
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integration = self.integrate_neuron_coding()
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print(f" Paradigm: {integration['paradigm']}")
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print(f" Mechanism: {integration['mechanism']}")
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print(f" Characteristics: {integration['characteristics']}")
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print(f" Benefits: {integration['benefits']}")
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print("\n" + "=" * 60)
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print("HUMAN NEURON CODING TOPOLOGY ANALYSIS COMPLETE")
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print("=" * 60)
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return {
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"neuron_analysis": neuron_analysis,
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"benefits_analysis": benefits,
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"impact_calculation": impact_calculation,
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"integration": integration
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}
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if __name__ == '__main__':
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analyzer = NeuronCodingTopology()
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results = analyzer.run_analysis()
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# Save results
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output_file = OUTPUT_DIR / "neuron_coding_topology.json"
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with open(output_file, 'w') as f:
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json.dump(results, f, indent=2)
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print(f"\nAnalysis results saved to {output_file}")
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# Print summary
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print("\n" + "=" * 60)
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print("NEURON CODING TOPOLOGY SUMMARY")
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print("=" * 60)
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print(f"Paradigm: {results['integration']['paradigm']}")
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print(f"Neuron Coding Capacity: {results['impact_calculation']['neuron_coding_capacity']}")
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print(f"Neuron Coding Improvement Factor: {results['impact_calculation']['neuron_coding_improvement_factor']:.2f}x")
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print(f"Total Neuron Coding Multiplier: {results['impact_calculation']['total_neuron_coding_multiplier']:.2f}x")
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