title: Semantic priming in a cortical network model.
abstract: Contextual recall in humans relies on the semantic relationships between items stored in memory. These relationships can be probed by priming experiments. Such experiments have revealed a rich phenomenology on how reaction times depend on various factors such as strength and nature of associations, time intervals between stimulus presentations, and so forth. Experimental protocols on humans present striking similarities with pair association task experiments in monkeys. Electrophysiological recordings of cortical neurons in such tasks have found two types of task-related activity, "retrospective" (related to a previously shown stimulus), and "prospective" (related to a stimulus that the monkey expects to appear, due to learned association between both stimuli). Mathematical models of cortical networks allow theorists to understand the link between the physiology of single neurons and synapses, and network behavior giving rise to retrospective and/or prospective activity. Here, we show that this type of network model can account for a large variety of priming effects. Furthermore, the model allows us to interpret semantic priming differences between the two hemispheres as depending on a single association strength parameter.
authors.affiliation: Université René Descartes, Paris, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Brunel
authors.forename: Frédéric
authors.initials: F
authors.lastname: Lavigne
mesh_terms: D000818:Animals; D002540:Cerebral Cortex / Q000166:cytology / Q000502:physiology*; D000882:Haplorhini; D006801:Humans; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009433:Neural Inhibition; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D012660:Semantics; D013569:Synapses / Q000502:physiology