Jiaqi Zhou, Liping Gong, Xiaodong Huang, Chunlai Mu, Yuanyuan Mi
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引用次数: 0
Abstract
Sequential working memory (SWM), referring to the temporary storage and manipulation of information in order, plays a fundamental role in brain cognitive functions. The serial position effect refers to the phenomena that recall accuracy of an item is associated to the order of the item being presented. The neural mechanism underpinning the serial position effect remains unclear. The synaptic mechanism of working memory proposes that information is stored as hidden states in the form of facilitated neuronal synapse connections. Here, we build a continuous attractor neural network with synaptic short-term plasticity (STP) to explore the neural mechanism of the serial position effect. Using a delay recall task, our model reproduces the the experimental finding that as the maintenance period extends, the serial position effect transitions from the primacy to the recency effect. Using both numerical simulation and theoretical analysis, we show that the transition moment is determined by the parameters of STP and the interval between presented stimulus items. Our results highlight the pivotal role of STP in processing the order information in SWM.
期刊介绍:
Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions.
Also: comp neuro