Connectionist Models

Connectionist models are systems that mimic how the brain processes information through networks of interconnected units, called nodes, resembling neurons. These models learn by adjusting the strength of connections based on experience, allowing them to recognize patterns, solve problems, or interpret data, much like how the brain learns. They are used in artificial intelligence to develop tasks such as speech recognition, image processing, and language understanding, emphasizing the importance of distributed processing and adaptability within interconnected systems.