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Computational models of learning and beyond: Symmetries of associative learning

Alonso, E. & Mondragon, E. ORCID: 0000-0003-4180-1261 (2010). Computational models of learning and beyond: Symmetries of associative learning. In: Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications. (pp. 316-332). IGI Global. doi: 10.4018/978-1-60960-021-1.ch013

Abstract

The authors propose in this chapter to use abstract algebra to unify different models of theories of associative learning -- as complementary to current psychological, mathematical and computational models of associative learning phenomena and data. The idea is to compare recent research in associative learning to identify the symmetries of behaviour. This approach, a common practice in Physics and Biology, would help us understand the structure of conditioning as opposed to the study of specific linguistic (either natural or formal) expressions that are inherently incomplete and often contradictory.

Publication Type: Book Section
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
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