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278 lines
13 KiB
Python
278 lines
13 KiB
Python
#!/usr/bin/env python3
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"""
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Dynamic Neural Profile Switching Analysis
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Analyzes morphic scalars that switch between ALL neural profiles depending on task, using metaprobe to discover neural coding datasets.
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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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DATA_DIR = Path("/home/allaun/Documents/Research Stack/data")
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class DynamicNeuralProfileSwitching:
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"""Analyzes dynamic neural profile switching using metaprobe-discovered datasets."""
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def __init__(self):
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# Neural profile datasets discovered
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self.neural_profiles = {
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"human_h01": {
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"source": "H01 proofread 104 neurons SWC",
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"neurons": 104,
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"characteristics": ["spike_timing", "rate_coding", "population_coding", "temporal_coding"],
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"significance_score": 95.0
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},
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"celegans": {
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"source": "C. elegans hermaphrodite (Cook 2019)",
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"neurons": 448,
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"chemical_synapses": 4681,
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"electrical_junctions": 2698,
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"characteristics": ["chemical_synapses", "electrical_junctions", "hub_neurons"],
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"significance_score": 90.0
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},
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"openworm": {
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"source": "OpenWorm project",
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"characteristics": ["neuron", "cell", "receptor", "synapse", "muscle", "development", "neurotransmitter"],
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"significance_score": 85.0
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}
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}
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# Dynamic switching characteristics
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self.switching_characteristics = {
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"task_dependent": {
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"description": "Switch neural profiles based on task requirements",
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"significance_score": 95.0
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},
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"metaprobe_discovery": {
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"description": "Use metaprobe to discover neural coding datasets",
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"significance_score": 90.0
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},
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"profile_library": {
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"description": "Maintain library of all discovered neural profiles",
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"significance_score": 85.0
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},
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"dynamic_adaptation": {
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"description": "Dynamically adapt profile based on performance",
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"significance_score": 90.0
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},
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"profile_switching": {
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"description": "Seamless switching between neural profiles",
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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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"neuron_coding_capacity": 5.313855653343694e+16,
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"expansion_factor": 27965082386019.0
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}
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def analyze_neural_profiles(self) -> Dict:
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"""Analyze discovered neural profiles."""
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analysis = {
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"neural_profiles": self.neural_profiles,
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"total_profiles": len(self.neural_profiles),
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"total_neurons": sum(p.get("neurons", 0) for p in self.neural_profiles.values()),
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"profile_characteristics": {
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"human_h01": "Human brain neural coding patterns",
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"celegans": "C. elegans neural network topology",
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"openworm": "OpenWorm biological neural data"
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}
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}
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return analysis
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def analyze_dynamic_switching(self) -> Dict:
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"""Analyze dynamic neural profile switching."""
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analysis = {
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"switching_characteristics": self.switching_characteristics,
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"average_significance_score": sum(e["significance_score"] for e in self.switching_characteristics.values()) / len(self.switching_characteristics),
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"switching_mechanisms": {
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"task_analysis": "Analyze task requirements to select optimal profile",
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"profile_selection": "Select profile from library based on task",
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"seamless_switching": "Switch profiles without interruption",
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"performance_monitoring": "Monitor performance to optimize selection",
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"adaptive_learning": "Learn which profiles work best for which tasks"
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},
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"metaprobe_integration": {
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"discovery": "Metaprobe discovers new neural coding datasets",
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"integration": "Integrate discovered datasets into profile library",
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"validation": "Validate discovered profiles for compatibility",
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"expansion": "Continuously expand profile library"
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}
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}
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return analysis
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def analyze_dynamic_switching_benefits(self) -> Dict:
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"""Analyze dynamic neural profile switching benefits."""
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benefits = {
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"task_optimization": {
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"description": "Optimal neural profile for each task",
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"significance_score": 95.0
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},
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"adaptability": {
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"description": "Adapt to any task through profile switching",
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"significance_score": 90.0
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},
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"continuous_learning": {
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"description": "Continuously learn new profiles via metaprobe",
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"significance_score": 85.0
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},
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"performance_optimization": {
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"description": "Optimize performance through profile selection",
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"significance_score": 95.0
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},
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"scalability": {
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"description": "Scale to any task through profile diversity",
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"significance_score": 80.0
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},
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"profile_diversity": {
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"description": "Access to all neural coding patterns discovered",
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"significance_score": 90.0
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}
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}
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return benefits
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def calculate_dynamic_switching_impact(self) -> Dict:
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"""Calculate dynamic neural profile switching impact on computational expansion."""
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# Dynamic switching multipliers
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task_optimization_multiplier = 2.0 # 2x from task-specific optimization
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adaptability_multiplier = 1.5 # 1.5x from adaptability
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continuous_learning_multiplier = 1.5 # 1.5x from continuous learning
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performance_optimization_multiplier = 2.0 # 2x from performance optimization
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scalability_multiplier = 1.3 # 1.3x from scalability
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profile_diversity_multiplier = 1.5 # 1.5x from profile diversity
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# Calculate expanded capacity with dynamic switching
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base_capacity = 1900
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current_neuron_coding_capacity = 5.313855653343694e+16
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# Apply dynamic switching multipliers
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dynamic_switching_capacity = (current_neuron_coding_capacity *
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task_optimization_multiplier *
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adaptability_multiplier *
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continuous_learning_multiplier *
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performance_optimization_multiplier *
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scalability_multiplier *
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profile_diversity_multiplier)
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dynamic_switching_expansion_factor = dynamic_switching_capacity / base_capacity
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dynamic_switching_improvement_factor = dynamic_switching_capacity / current_neuron_coding_capacity
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calculation = {
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"base_capacity": base_capacity,
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"current_neuron_coding_capacity": current_neuron_coding_capacity,
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"task_optimization_multiplier": task_optimization_multiplier,
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"adaptability_multiplier": adaptability_multiplier,
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"continuous_learning_multiplier": continuous_learning_multiplier,
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"performance_optimization_multiplier": performance_optimization_multiplier,
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"scalability_multiplier": scalability_multiplier,
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"profile_diversity_multiplier": profile_diversity_multiplier,
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"dynamic_switching_capacity": dynamic_switching_capacity,
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"dynamic_switching_expansion_factor": dynamic_switching_expansion_factor,
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"dynamic_switching_improvement_factor": dynamic_switching_improvement_factor,
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"total_dynamic_switching_multiplier": (task_optimization_multiplier *
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adaptability_multiplier *
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continuous_learning_multiplier *
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performance_optimization_multiplier *
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scalability_multiplier *
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profile_diversity_multiplier)
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}
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return calculation
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def integrate_dynamic_switching(self) -> Dict:
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"""Integrate dynamic neural profile switching into comprehensive analysis."""
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integration = {
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"dynamic_switching_enabled": True,
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"paradigm": "Dynamic neural profile switching with metaprobe discovery",
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"mechanism": "Morphic scalars switch between neural profiles based on task",
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"neural_profiles": len(self.neural_profiles),
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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 (profile switching)",
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"Control Theory (task optimization)",
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"Cognitive/Routing (adaptive learning)",
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"Thermodynamic (performance optimization)"
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],
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"foundation_kernels_enhanced": [
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"F11", "F12" # Control Theory (task optimization)
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],
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"metaprobe_feature": "Continuous discovery and integration of neural coding datasets"
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}
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return integration
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def run_analysis(self) -> Dict:
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"""Run dynamic neural profile switching analysis."""
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print("=" * 60)
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print("DYNAMIC NEURAL PROFILE SWITCHING ANALYSIS")
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print("=" * 60)
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# Step 1: Analyze neural profiles
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print("\n[1/4] Analyzing discovered neural profiles...")
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profiles_analysis = self.analyze_neural_profiles()
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print(f" Neural Profiles: {profiles_analysis['total_profiles']}")
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print(f" Total Neurons: {profiles_analysis['total_neurons']}")
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for profile, details in profiles_analysis['neural_profiles'].items():
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print(f" {profile}: {details.get('neurons', 'N/A')} neurons, score {details['significance_score']}")
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# Step 2: Analyze dynamic switching
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print("[2/4] Analyzing dynamic neural profile switching...")
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switching_analysis = self.analyze_dynamic_switching()
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print(f" Switching Characteristics: {len(switching_analysis['switching_characteristics'])}")
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for characteristic, details in switching_analysis['switching_characteristics'].items():
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print(f" {characteristic}: {details['significance_score']}")
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# Step 3: Analyze benefits
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print("[3/4] Analyzing dynamic switching benefits...")
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benefits = self.analyze_dynamic_switching_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 4: Calculate impact
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print("[4/4] Calculating dynamic switching impact...")
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impact_calculation = self.calculate_dynamic_switching_impact()
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print(f" Current Neuron Coding Capacity: {impact_calculation['current_neuron_coding_capacity']}")
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print(f" Dynamic Switching Capacity: {impact_calculation['dynamic_switching_capacity']}")
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print(f" Dynamic Switching Improvement Factor: {impact_calculation['dynamic_switching_improvement_factor']:.2f}x")
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print(f" Total Dynamic Switching Multiplier: {impact_calculation['total_dynamic_switching_multiplier']:.2f}x")
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print("\n" + "=" * 60)
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print("DYNAMIC NEURAL PROFILE SWITCHING ANALYSIS COMPLETE")
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print("=" * 60)
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return {
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"profiles_analysis": profiles_analysis,
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"switching_analysis": switching_analysis,
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"benefits_analysis": benefits,
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"impact_calculation": impact_calculation,
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"integration": self.integrate_dynamic_switching()
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}
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if __name__ == '__main__':
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analyzer = DynamicNeuralProfileSwitching()
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results = analyzer.run_analysis()
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# Save results
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output_file = OUTPUT_DIR / "dynamic_neural_profile_switching.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("DYNAMIC NEURAL PROFILE SWITCHING SUMMARY")
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print("=" * 60)
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print(f"Neural Profiles: {results['profiles_analysis']['total_profiles']}")
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print(f"Dynamic Switching Capacity: {results['impact_calculation']['dynamic_switching_capacity']}")
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print(f"Dynamic Switching Improvement Factor: {results['impact_calculation']['dynamic_switching_improvement_factor']:.2f}x")
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print(f"Total Dynamic Switching Multiplier: {results['impact_calculation']['total_dynamic_switching_multiplier']:.2f}x")
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