title: The formation of categories and the representation of feature saliency: analysis with a computational model trained with an Hebbian paradigm.
abstract: An important issue in semantic memory models is the formation of categories and taxonomies, and the different role played by shared vs. distinctive and salient vs. marginal features. Aim of this work is to extend our previous model to critically discuss the mechanisms leading to the formation of categories, and to investigate how feature saliency can be learned from past experience. The model assumes that an object is represented as a collection of features, which belong to different cortical areas and are topologically organized. Excitatory synapses among features are created on the basis of past experience of object presentation, with a Hebbian paradigm, including the use of potentiation and depression of synapses, and thresholding for the presynaptic and postsynaptic. The model was trained using simple schematic objects as input (i.e., vector of features) having some shared features (so as to realize a simple category) and some distinctive features with different frequency. Three different taxonomies of objects were separately trained and tested, which differ as to the number of correlated features and the structure of categories. Results show that categories can be formed from past experience, using Hebbian rules with a different threshold for postsynaptic and presynaptic activity. Furthermore, features have a different saliency, as a consequence of their different frequency during training. The trained network is able to solve simple object recognition tasks, by maintaining a distinction between categories and individual members in the category, and providing a different role for salient features vs. not-salient features. In particular, not-salient features are not evoked in memory when thinking about the object, but they facilitate the reconstruction of objects when provided as input to the model. The results can provide indications on which neural mechanisms can be exploited to form robust categories among objects and on which mechanisms could be implemented in artificial connectionist systems to extract concepts and categories from a continuous stream of input objects (each represented as a vector of features).
authors.affiliation: Department of Electrical, Electronic and Information Engineering, University of Bologna, Viale Risorgimento 2, I 40136 Bologna, Italy.
authors.forename: Mauro
authors.initials: M
authors.lastname: Ursino
authors.forename: Cristiano
authors.initials: C
authors.lastname: Cuppini
authors.forename: Elisa
authors.initials: E
authors.lastname: Magosso
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D013569:Synapses / Q000502:physiology*