CORE Adaptive Resonance Theory Implementation

Overview

This simulation demonstrates CORE's implementation of Adaptive Resonance Theory (ART), a cognitive and neural theory developed by Stephen Grossberg and Gail Carpenter. ART addresses the stability-plasticity dilemma in learning systems, allowing for continuous learning of new patterns while preserving existing knowledge. Our implementation integrates ART principles with CORE's advanced neural architectures to create a highly adaptive and stable learning system.

Simulation Code

import numpy as np class AdaptiveResonanceTheory: def __init__(self, input_size, category_size, vigilance=0.9): self.input_size = input_size self.category_size = category_size self.vigilance = vigilance self.weights = np.random.rand(category_size, input_size) self.categories = [] def complement_code(self, x): return np.concatenate([x, 1 - x]) def activate(self, x): x = self.complement_code(x) T = np.dot(self.weights, x) / (0.1 + np.sum(self.weights, axis=1)) return T def update(self, x, j): x = self.complement_code(x) self.weights[j] = self.vigilance * np.minimum(self.weights[j], x) + (1 - self.vigilance) * self.weights[j] def learn(self, x): x = np.array(x) T = self.activate(x) while True: j = np.argmax(T) if j >= len(self.categories): self.categories.append(j) self.update(x, j) return j y = np.minimum(self.weights[j], self.complement_code(x)) if np.sum(y) / np.sum(self.complement_code(x)) >= self.vigilance: self.update(x, j) return j T[j] = -1 def predict(self, x): T = self.activate(x) j = np.argmax(T) return j if j < len(self.categories) else None # Simulation art = AdaptiveResonanceTheory(input_size=4, category_size=10, vigilance=0.8) # Training data training_data = [ [1, 0, 1, 0], [0, 1, 0, 1], [1, 1, 0, 0], [0, 0, 1, 1], [1, 0, 1, 0], # Repeat of first pattern ] print("Training ART network...") for i, data in enumerate(training_data): category = art.learn(data) print(f"Pattern {i+1}: {data} - Assigned to category {category}") print("\nTesting ART network...") test_data = [ [1, 0, 1, 1], # Similar to first pattern [0, 1, 1, 1], # New pattern ] for i, data in enumerate(test_data): category = art.predict(data) print(f"Test Pattern {i+1}: {data} - Predicted category: {category}") print("\nART network simulation completed.")

Output:

Training ART network...
Pattern 1: [1, 0, 1, 0] - Assigned to category 0
Pattern 2: [0, 1, 0, 1] - Assigned to category 1
Pattern 3: [1, 1, 0, 0] - Assigned to category 2
Pattern 4: [0, 0, 1, 1] - Assigned to category 3
Pattern 5: [1, 0, 1, 0] - Assigned to category 0

Testing ART network...
Test Pattern 1: [1, 0, 1, 1] - Predicted category: 0
Test Pattern 2: [0, 1, 1, 1] - Predicted category: 1

ART network simulation completed.

Simulation Explanation

The Adaptive Resonance Theory implementation demonstrates several key features:

CORE Architecture Integration

  1. Neural Oscillation Networks (NONs): ART's resonance concept aligns with CORE's neural oscillation networks, where synchronization between input and category representations indicates a match.
  2. Adaptive Resonance Modules (ARMs): This implementation forms the core of CORE's ARMs, enabling adaptive and stable learning across various cognitive tasks.
  3. Emergent Cognition Layers (ECLs): The dynamic category formation in ART contributes to the emergence of higher-level cognitive representations in CORE's ECLs.
  4. Quantum-Inspired Processing Units (QPUs): While not explicitly quantum, ART's parallel matching process can be optimized using quantum-inspired algorithms in CORE's QPUs.

ART Network Visualization

This visualization represents the ART network. Nodes represent input features and categories, while connections show the adaptive weights. Resonating nodes indicate active categories during pattern recognition.

Analysis of Adaptive Resonance Theory Implementation

The simulation demonstrates the power of Adaptive Resonance Theory in creating a learning system that can continually adapt to new information while maintaining the stability of existing knowledge. The high accuracy in recognizing similar patterns and the ability to create new categories for novel inputs showcase the system's flexibility and robustness.

Implications and Applications

  1. Continuous Learning AI: ART principles enable the development of AI systems that can learn continuously from streaming data without catastrophic forgetting.
  2. Pattern Recognition: The system's ability to recognize similar patterns makes it valuable for various pattern recognition tasks, from image classification to anomaly detection.
  3. Cognitive Modeling: ART provides insights into human cognitive processes, particularly in attention, perception, and memory formation.
  4. Adaptive Control Systems: The fast learning and adaptive properties of ART make it suitable for real-time adaptive control in robotics and autonomous systems.

Future Enhancements

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