Effcient behavioral assays are crucial for understanding the neural mechanisms of cognitive functions.Here, we designed a high-throughput automatic training system for spatial cognition(HASS) for free-moving mice.Mice...Effcient behavioral assays are crucial for understanding the neural mechanisms of cognitive functions.Here, we designed a high-throughput automatic training system for spatial cognition(HASS) for free-moving mice.Mice were trained to return to the home arm and remain there during a delay period. Software was designed to enable automatic training in all its phases, including habituation, shaping, and learning. Using this system, we trained mice to successfully perform a spatially delayed nonmatch to sample task, which tested spatial cognition,working memory, and decision making. Performance depended on the delay duration, which is a hallmark of working memory tasks. The HASS enabled a human operator to train more than six mice simultaneously with minimal intervention, therefore greatly enhancing experimental efficiency and minimizing stress to the mice.Combined with the optogenetic method and neurophysiological techniques, the HASS will be useful in deciphering the neural circuitry underlying spatial cognition.展开更多
Spatio-temporal databases aim at appropriately managing moving objects so as to support various types of queries. While much research has been conducted on developing query processing techniques, less effort has been ...Spatio-temporal databases aim at appropriately managing moving objects so as to support various types of queries. While much research has been conducted on developing query processing techniques, less effort has been made to address the issue of when and how to update location information of moving objects. Previous work shifts the workload of processing updates to each object which usually has limited CPU and battery capacities. This results in a tremendous processing overhead for each moving object. In this paper, we focus on designing efficient update strategies for two important types of moving objects, free-moving objects(FMOs) and network-constrained objects(NCOs), which are classified based on object movement models. For FMOs, we develop a novel update strategy, namely the FMO update strategy(FMOUS), to explicitly indicate a time point at which the object needs to update location information. As each object knows in advance when to update(meaning that it does not have to continuously check), the processing overhead can be greatly reduced. In addition, the FMO update procedure(FMOUP) is designed to efficiently process the updates issued from moving objects. Similarly, for NCOs, we propose the NCO update strategy(NCOUS) and the NCO update procedure(NCOUP) to inform each object when and how to update location information. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed update strategies.展开更多
基金supported by the Instrument Developing Project of the Chinese Academy of Sciences(YZ201540)the National Science Foundation for Distinguished Young Scholars of China(31525010)+4 种基金the General Program of the National Science Foundation of China(31471049)the Key Research Project of Frontier Science of the Chinese Academy of Sciences(QYZDB-SSW-SMC009)China–Netherlands CAS-NWO Programme:Joint Research Projects,The Future of Brain and Cognition(153D31KYSB20160106)the Key Project of Shanghai Science and Technology Commission(15JC1400102,16JC1400101)the State Key Laboratory of Neuroscience,China
文摘Effcient behavioral assays are crucial for understanding the neural mechanisms of cognitive functions.Here, we designed a high-throughput automatic training system for spatial cognition(HASS) for free-moving mice.Mice were trained to return to the home arm and remain there during a delay period. Software was designed to enable automatic training in all its phases, including habituation, shaping, and learning. Using this system, we trained mice to successfully perform a spatially delayed nonmatch to sample task, which tested spatial cognition,working memory, and decision making. Performance depended on the delay duration, which is a hallmark of working memory tasks. The HASS enabled a human operator to train more than six mice simultaneously with minimal intervention, therefore greatly enhancing experimental efficiency and minimizing stress to the mice.Combined with the optogenetic method and neurophysiological techniques, the HASS will be useful in deciphering the neural circuitry underlying spatial cognition.
基金supported by the National Science Council of Taiwan(Nos.NSC-102-2119-M-244-001 and MOST-103-2119-M-244-001)
文摘Spatio-temporal databases aim at appropriately managing moving objects so as to support various types of queries. While much research has been conducted on developing query processing techniques, less effort has been made to address the issue of when and how to update location information of moving objects. Previous work shifts the workload of processing updates to each object which usually has limited CPU and battery capacities. This results in a tremendous processing overhead for each moving object. In this paper, we focus on designing efficient update strategies for two important types of moving objects, free-moving objects(FMOs) and network-constrained objects(NCOs), which are classified based on object movement models. For FMOs, we develop a novel update strategy, namely the FMO update strategy(FMOUS), to explicitly indicate a time point at which the object needs to update location information. As each object knows in advance when to update(meaning that it does not have to continuously check), the processing overhead can be greatly reduced. In addition, the FMO update procedure(FMOUP) is designed to efficiently process the updates issued from moving objects. Similarly, for NCOs, we propose the NCO update strategy(NCOUS) and the NCO update procedure(NCOUP) to inform each object when and how to update location information. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed update strategies.
