TY - JOUR A2 - Babiloni, Fabio AU - Ursino, M. AU - Cuppini, C. AU - Magosso, E. PY - 2010 DA - 2010/03/01 TI - A Semantic Model to Study Neural Organization of Language in Bilingualism SP - 350269 VL - 2010 AB - A neural network model of object semantic representation is used to simulate learning of new words from a foreign language. The network consists of feature areas, devoted to description of object properties, and a lexical area, devoted to words representation. Neurons in the feature areas are implemented as Wilson-Cowan oscillators, to allow segmentation of different simultaneous objects via gamma-band synchronization. Excitatory synapses among neurons in the feature and lexical areas are learned, during a training phase, via a Hebbian rule. In this work, we first assume that some words in the first language (L1) and the corresponding object representations are initially learned during a preliminary training phase. Subsequently, second-language (L2) words are learned by simultaneously presenting the new word together with the L1 one. A competitive mechanism between the two words is also implemented by the use of inhibitory interneurons. Simulations show that, after a weak training, the L2 word allows retrieval of the object properties but requires engagement of the first language. Conversely, after a prolonged training, the L2 word becomes able to retrieve object per se. In this case, a conflict between words can occur, requiring a higher-level decision mechanism. SN - 1687-5265 UR - https://doi.org/10.1155/2010/350269 DO - 10.1155/2010/350269 JF - Computational Intelligence and Neuroscience PB - Hindawi Publishing Corporation KW - ER -