Connectionist Techniques
http://www.lt-world.org/hlt_survey/ltw-chapter11-5.pdf
- Neural Theory of Language Research Group
- Neural Networks Research Group
- Department of Cognitive Science
- Center for the Neural Basis of Cognition
- Machine Learning and Neural Networks Group
- Lokendra Shastri
- Jeff Elman
- Stan C. Kwasny
- James L. McClelland
- Risto Miikkulainen
- James Hammerton
- Subsymbolic Parsing of Sequences (SARDSRN)
- Forming Text Representations with Neural Networks
- SHRUTI
Connectionist techniques are modelled on biological brains, whose higher-order cognitive processes appear to emerge from the interplay of large numbers of simple processing units, the neurons. Rather than being used as a substrate in which to implement known elements playing known roles, neural networks are let to evolve by themselves: they gradually adapt to the environment through a modification of inter-neural connection strengths, which come to reflect the neurons' history of co-activities. Typically, the emerging network represents objects, symbols, attributes, etc. (if at all) in states, involving larger numbers of neurons. Connectionism is a field of machine learning and has an affinity to statistics, fuzzy logic, and genetic programming.
PDP
Parallel Distributed Processing; Connectionism