connectionist-models
AI models based on neural networks and associative memory, such as Boltzmann Machines, representing a shift from symbolic AI to holistic, distributed processing.
2 chapters across 2 books
Mind over machine: the power of human intuition and expertise (1988)Stuart E. Athanasiou, Tom Dreyfus
Chapter 3 traces the evolution of artificial intelligence from early optimistic expectations to a more cautious and realistic understanding of its capabilities and limitations. It discusses foundational AI research, challenges such as natural language processing and common sense reasoning, and alternative approaches including connectionist models and holistic systems. The chapter also highlights key figures and programs, reflecting on the complexity of simulating human thought and the ongoing debates within AI research.
The Handbook of Brain Theory and Neural Networks (2003)Michael A. Arbib
This chapter serves as an introduction to Part II of the Handbook, which offers a guided tour through 22 thematic road maps covering diverse topics in brain theory and neural networks. It outlines the organization of these road maps into eight general categories, ranging from biological models of neurons to applications in artificial intelligence, and emphasizes the interconnectedness and multiple approaches to studying brain function and neural computation. The chapter also discusses the methodological distinctions between biological realism and connectionist models, and encourages readers to explore the material in a flexible, personalized order.