A Review of Self-Organizing Neural Network Architectures

Abstract

This article reviews self-organizing neural network architectures, including Adaptive Resonance Theory (ART) and its variants, along with their applications in cognitive agents, planning, and pattern recognition. These models are inspired by biological systems and emphasize real-time learning and adaptation to changing environments. The architectures discussed include ART1, ART2, ART2-A, Fuzzy ART, ARTMAP, and FALCON. The article also considers the integration of these models into larger cognitive architectures, such as FALCON-X and iFALCON, for creating autonomous agents.

Introduction

A core challenge in the design of intelligent systems is the ability to learn and recognize invariant properties of the environment through interaction. This article explores various self-organizing neural network architectures that address this challenge, drawing from the principles of Adaptive Resonance Theory (ART) and related models. These models are capable of real-time learning and adaptation to novel and complex environments without forgetting previously learned information.

Adaptive Resonance Theory (ART) Networks

ART1: This network is capable of self-organizing, self-stabilizing, and self-scaling its recognition codes in response to arbitrary temporal sequences of binary input patterns. A key feature of ART1 is its ability to maintain stability while retaining plasticity. ART1 uses a vigilance parameter to control the degree of match required between an input pattern and existing memory representations.

ART2: This is an extension of ART1 for analog (continuous-valued) input patterns, as well as binary patterns. It can self-organize stable recognition categories. ART2 is useful in various applications, such as the recognition of printed or written text.

ART2-A: This is a simplified and computationally efficient version of ART2, that accurately reproduces the behavior of ART2 in the fast-learn limit, and runs approximately two to three orders of magnitude faster than ART2.

Fuzzy ART: This model extends ART1 to process both analog and binary input patterns by incorporating fuzzy set theory computations. Fuzzy ART uses the MIN operator of fuzzy set theory to replace the intersection operator in ART1. Normalizing input vectors at a preprocessing stage prevents category proliferation, and complement coding allows for a symmetric theory using both MIN and MAX operators.

ARTMAP: This supervised learning system uses two ART modules, ARTa and ARTb, linked by an associative learning network. It autonomously learns to classify arbitrarily ordered vectors into recognition categories based on predictive success. ARTMAP maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis. It can quickly distinguish rare but important events even if they are similar to frequent events with different consequences. Fuzzy ARTMAP is a variant of ARTMAP that is used for analog pattern classification.

Key Mechanisms in ART Networks

Self-Organization: ART networks discover recognition codes through interaction with the environment, without external guidance.

Stability-Plasticity Dilemma: These networks are designed to maintain stability of learned memories while remaining plastic to learn new information. This is achieved through a combination of bottom-up input and top-down expectations.

Vigilance Parameter: This parameter controls the degree of match required between an input pattern and an existing category. A higher vigilance value results in finer categories, while a lower vigilance value leads to broader categories.

Matching Process: A match between bottom-up input and top-down expectations leads to a state of resonance, which reinforces the learned pattern. A mismatch leads to a reset and a search for a better category.

Attentional Subsystem: ART architectures include attentional mechanisms to focus on relevant features of the input.

Applications of ART Networks

ART networks have been applied to a variety of problems:

Pattern Recognition: ART networks are used for recognition of analog and binary patterns, such as those found in visual and auditory processing. They are capable of rapidly learning to categorize many different patterns.

Robotics and Autonomous Agents: ART models are used to create cognitive architectures for autonomous agents. These agents can learn, plan, and adapt in dynamic environments.

Intentional Planning Agents: The iFALCON architecture extends fusion ART networks to create intentional agents that can perform hierarchical planning. These agents can map symbolic descriptions to weighted neural connections, and learn new plans.

Information Fusion: ARTMAP networks are used for information fusion, where information from multiple sources are combined and categorized.

Navigation: ART models are also used for navigation tasks, where an agent learns to navigate in a complex environment based on rewards.

Real-Time Learning: ART systems are capable of learning in real-time without human intervention.

Hybrid Architectures

FALCON-X: This model integrates the Adaptive Control of Thought (ACT-R) architecture with a fusion ART network, replacing ACT-R's production system with a fusion ART model. FALCON-X combines high-level deliberative cognitive behaviors with real-time learning abilities.

iFALCON: This architecture is based on a multi-channel adaptive resonance theory network and extends the fusion ART model to represent sequences and hierarchical structures. It is used to implement intentional agents that can perform planning.

Conclusion

Self-organizing neural networks, particularly those based on ART, provide a powerful framework for designing intelligent systems that can learn and adapt in complex environments. These models emphasize real-time learning, stability, and plasticity, and have found applications in various areas, including pattern recognition, robotics, and cognitive modeling. The integration of ART networks into larger cognitive architectures, such as FALCON-X and iFALCON, enables the creation of more sophisticated autonomous agents. Future research can further explore the capabilities of these models in addressing increasingly complex problems in artificial intelligence and cognitive science.