Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Synthetic brain imaging: grasping, mirror neurons and imitation
Neural Networks - Special issue on the global brain: imaging and modelling
On the Role of Abstraction in Case-Based Reasoning
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Cerebral modeling and dynamic Bayesian networks
Artificial Intelligence in Medicine
SimBa: a fuzzy similarity-based modelling framework for large-scale cerebral networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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This work proposes a formal modelling of categorisation processes attempting at simulating the way information is categorised by neural populations in the human brain. The formalism mainly relies on a similarity-based approach to categorisation. It involves weighted rules that use inference and fusion techniques borrowed from fuzzy logic. The approach is illustrated by a simulation of the McGurck effect where the combination of contradictory auditory and visual stimuli creates an auditory perceptive illusion.