connectionism
kuh-NEK-shuh-niz-um
kəˈnɛkʃənɪzəmListen
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Dictionary meaning
an approach to cognitive science and artificial intelligence that models mental phenomena using artificial neural networks — systems of interconnected processing units whose connection weights are adjusted through learning to produce intelligent behaviour; connectionism arose as a significant alternative to the classical symbolic paradigm in cognitive science during the 1980s, with the publication of the parallel distributed processing volumes by David Rumelhart and James McClelland; key properties of connectionist systems include distributed representation (knowledge is spread across connection weights rather than stored in discrete symbols), graceful degradation (performance declines gradually with damage), and learning through exposure to examples; modern deep learning systems are the most powerful realisation of connectionist principles and have reshaped both AI and debates in cognitive science about the nature of mental representation
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