Connectionism
Connectionism is an approach in artificial intelligence and cognitive science that models how the human brain processes information through networks of simple units called "neurons." Instead of relying on traditional, rule-based programming, connectionist systems learn from data by adjusting the connections between these units. This allows them to identify patterns, make decisions, and adapt over time, similar to how humans learn. Connectionist models, particularly neural networks, are widely used in tasks like image recognition and natural language processing, offering a more flexible way to solve complex problems by mimicking the brain's interconnected structure.
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Connectionism is a theory in cognitive science that models mental processes using artificial neural networks, which are inspired by the way the human brain works. These networks consist of interconnected nodes (or neurons) that simulate how we learn and remember by adjusting their connections based on experience. Connectionism suggests that knowledge is not stored in one place but rather represented across many connections. This approach is particularly useful for understanding pattern recognition, language, and learning, as it emphasizes the importance of networks and relationships between information, rather than focusing solely on individual items or symbols.