title: A computational model for generation of the P300 evoked potential component.
abstract: The P300 is an endogenously evoked potential with amplitude and latency depending on the amount of information carried by the stimulus rather than its physical characteristics. It has been suggested that P300 is a manifestation of the context updating mechanism in the human working memory. We present a neural network-based model that mimics the learning and forgetting mechanisms of external stimuli in the human working memory that are believed to be responsible for P300 generation. A modified version of the Hebbian learning rule has been devised to govern the weight dynamics of the network. The model was validated by comparing the characteristics of simulated P300 with actual experimental findings such as the relationship between P300 amplitude and stimulus probability, and task relevance. The results show that the proposed P300 model mimics many aspects of the nervous system responsible for P300 generation.
authors.affiliation: Department of Electrical and Computer Engineering, University of Houston, N308-D2, Houston, TX 77204-4005,  USA.
authors.forename: Bharat K
authors.initials: BK
authors.lastname: Bonala
authors.forename: Ben H
authors.initials: BH
authors.lastname: Jansen
mesh_terms: D016477:Artifacts; D003198:Computer Simulation; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D005072:Evoked Potentials, Auditory / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology; D008570:Memory, Short-Term / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D011336:Probability