Hippocampal formation-inspired probabilistic generative model.
作者： Hiroshi Yamakawa , Akira Taniguchi , Ayako Fukawa
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摘要: We constructed a hippocampal formation (HPF)-inspired probabilistic generative model (HPF-PGM) using the structure-constrained interface decomposition method. By modeling brain regions with PGMs, this is positioned as module that can be integrated whole-brain PGM. discuss relationship between simultaneous localization and mapping (SLAM) in robotics findings of HPF neuroscience. Furthermore, we survey for various computational models, including brain-inspired SLAM, spatial concept formation, deep models. The HPF-PGM highly consistent anatomical structure functions HPF, contrast to typical conventional SLAM referencing brain, suggest importance integration egocentric/allocentric information from entorhinal cortex hippocampus use discrete-event queues.
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