The Modeling And Simulation Of Visuospatial Working Memory

Working memory (WM) is the ability to maintain and manipulate limited amounts of information which is necessary for such complex cognitive tasks such as language comprehension, learning and reasoning during short periods of cognitive activity. It is the hot topic of cognitive psychology and cognitive neuroscience research.Camperi and Wang (1998) have presented a network model for working memory that combines intrinsic cellular bistability with the recurrent network architecture of the neocortex. While Christopher (2006) replaced this intrinsic bistability with a biological mechanism-Ca2+ release subsystem. In this study, we aim to further expand the above work. First, we integrate the traditional firing-rate network with Ca2+ subsystem-induced bistability, amend the synaptic weights and suggest that Ca2+ concentration only increase the efficacy of synaptic input but has nothing to do with the external input for the transient cue. We found that our network model maintains the persistent activity in response to a brief transient stimulus like that of the previous two models and the working memory performance is resistant to noise and distraction stimulus.Since the basal ganglia and dopamine are confirmed to play an important role in protecting memories against noise and distraction stimulus, we add the basal ganglia in our present model and remove the plausible Ca2+ subsystem subsequently. We found that the new network model maintained the persistent activity in response to a brief transient stimulus, which was similar to the above work. Furthermore the working memory performance was resistant to noise and performed better than the above work in the aspect of resistance to distraction stimulus when the model is tuned to be bistable by dopamine.