Resonating Patterns: Adaptive Resonance Theory and Self-Organizing Maps

Adaptive Resonance Theory (ART) represents a powerful neural network architecture designed to address the stability-plasticity dilemma. Its primary objective is to enable rapid learning without compromising retention. ART embodies characteristics of self-organization and self-stabilization, distinguishing it from traditional neural networks. Unlike error-based learning prevalent in conventional models, ART employs competitive learning mechanisms. One of the distinguishing features of ART is its involvement in hypothesis testing, a departure from the approach of deep learning. This capability allows ART to learn dynamically in real-time environments. Over time, various ART models have emerged that cater to different types of data, such as binary and analog. This research proposal aims to explore the artlib library in Python, implementing various ART models on different datasets to analyze and compare their performance with more conventional algorithms like Self-Organizing Maps and K-Means clustering. This study seeks to highlight their efficacy, practical implementation, and differences between these frameworks.