Regulated Activation Networks
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Regulated Activation Networks(RANs), is a computational cognitive model. This modelling approach is based upon the Principles of Regulated Activation Networks(PRANs),[1][2] which are summarized as:
- The model must have a dynamic topology.
- The model must be capable creating abstract concepts.
- The model must be able to learn association among the concepts.
- The model must exhibit time-variant activation states.
See also[edit]
- Connectionist models.
- Cognitive models.
- Computational Psychology.
- Neural Networks.
- Machine Learning.
- Deep Representations.
- Conceptual Spaces.
- Conceptual Representations.
References[edit]
- ^ PInto, Alexandre Miguel; Barroso, Leandro (July 20–30, 2014). "Principles of Regulated Activation Networks". Graph-Based Representation and Reasoning. Lecture Notes in Computer Science. Vol. 8577. pp. 231–244. doi:10.1007/978-3-319-08389-6_19. ISBN 978-3-319-08388-9.
- ^ Nathalie Hernandez; Robert Jäschke; Madalina Croitoru (17 July 2014). Graph-Based Representation and Reasoning: 21st International Conference on Conceptual Structures, ICCS 2014, Iaşi, Romania, July 27-30, 2014, Proceedings. Springer. pp. 236–. ISBN 978-3-319-08389-6.