Connectionist Model

Connectionist models, primarily artificial neural networks, aim to understand and replicate intelligent behavior through interconnected nodes processing information. Current research emphasizes enhancing their explainability using fractal geometry and graph-based analyses, bridging the gap with symbolic AI through neuro-symbolic approaches and exploring the role of scaling symmetries in network architectures. This work is significant for advancing AI's capabilities in tasks like abstract reasoning, class-incremental learning, and natural language processing, ultimately contributing to a deeper understanding of intelligence itself.