connectionism
Sign in to savethumb|A 'second wave' connectionist (ANN) model with a hidden layer Connectionism is an approach to the study of human mental processes and cognition that utilizes mathematical models known as connectionist networks or artificial neural networks.
Article
14 sectionsContents
- Basic principle
- Activation function
- Memory and learning
- Biological realism
- Precursors
- The first wave
- The neural network winter
- The second wave
- Connectionism vs. computationalism debate
- Symbolism vs. connectionism debate
- See also
- Notes
- References
- External links
thumb|A 'second wave' connectionist (ANN) model with a hidden layer Connectionism is an approach to the study of human mental processes and cognition that utilizes mathematical models known as connectionist networks or artificial neural networks.
Connectionism has had many "waves" since its beginnings. The first wave appeared 1943 with Warren Sturgis McCulloch and Walter Pitts both focusing on comprehending neural circuitry through a formal and mathematical approach, and Frank Rosenblatt who published the 1958 paper "The Perceptron: A Probabilistic Model For Information Storage and Organization in the Brain" in Psychological Review, while working at the Cornell Aeronautical Laboratory. The first wave ended with the 1969 book Perceptrons about limitations of the original perceptron idea, written by Marvin Minsky and Seymour Papert, which contributed to discouraging major funding agencies in the US from investing in connectionist research. With a few noteworthy deviations, most connectionist research entered a period of inactivity until the mid-1980s. The term connectionist model was reintroduced in a 1982 paper in the journal Cognitive Science by Jerome Feldman and Dana Ballard.