首页|Findings from Huazhong University of Science and Technology in the Area of Robot ics Reported (A Bionic Localization Memristive Circuit Based On Spatial Cognitiv e Mechanisms of Hippocampus and Entorhinal Cortex)
Findings from Huazhong University of Science and Technology in the Area of Robot ics Reported (A Bionic Localization Memristive Circuit Based On Spatial Cognitiv e Mechanisms of Hippocampus and Entorhinal Cortex)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Robotics have been published. According to news reporting from Wuhan, People's Republic of Ch ina, by NewsRx journalists, research stated, "In this article, a bionic localiza tion memristive circuit is proposed, which mainly consists of head direction cel l module, grid cell module, place cell module and decoding module. This work mod ifies the two-dimensional Continuous Attractor Network (CAN) model of grid cells into two one-dimensional models in X and Y directions." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from the Huazhong Uni versity of Science and Technology, "The head direction cell module utilizes memr istors to integrate angular velocity and represents the real orientation of an a gent. The grid cell module uses memristors to sense linear velocity and orientat ion signals, which are both self-motion cues, and encodes the position in space by firing in a periodic mode. The place cell module receives the grid cell modul e's output and fires in a specific position. The decoding module decodes the ang le or place information and transfers the neuron state to a 'one-hot' code. This proposed circuit completes the localizing task in space and realizes in-memory computing due to the use of memristors, which can shorten the execution time. Th e functions mentioned above are implemented in LTSPICE. The simulation results s how that the proposed circuit can realize path integration and localization. Mor eover, it is shown that the proposed circuit has good robustness and low area ov erhead."
WuhanPeople's Republic of ChinaAsiaBrainCentral Nervous SystemCerebral CortexCerebrumEmerging TechnologiesEntorhinal CortexHealth and MedicineMachine LearningParahippocampal Gyru sRobotRoboticsTelencephalonHuazhong University of Science and Technology
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