首页|Hippocampal formation-inspired probabilistic generative model

Hippocampal formation-inspired probabilistic generative model

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  • NSTL
  • Elsevier
In building artificial intelligence (AI) agents, referring to how brains function in real environments can accelerate development by reducing the design space. In this study, we propose a probabilistic generative model (PGM) for navigation in uncertain environments by integrating the neuroscientific knowledge of hippocampal formation (HF) and the engineering knowledge in robotics and AI, namely, simultaneous localization and mapping (SLAM). We follow the approach of brain reference architecture (BRA) (Yamakawa, 2021) to compose the PGM and outline how to verify the model. To this end, we survey and discuss the relationship between the HF findings and SLAM models. The proposed hippocampal formation-inspired probabilistic generative model (HF-PGM) is designed to be highly consistent with the anatomical structure and functions of the HF. By referencing the brain, we elaborate on the importance of integration of egocentric/allocentric information from the entorhinal cortex to the hippocampus and the use of discrete-event queues. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Brain-inspired artificial intelligenceBrain reference architectureHippocampal formationSimultaneous localization and mappingProbabilistic generative modelPhase precession queue assumptionSPATIAL MEMORYHEAD-DIRECTIONPATH-INTEGRATIONNAVIGATIONCELLSREPRESENTATIONSBRAINSPACECOMMUNICATIONRATS

Taniguchi, Akira、Fukawa, Ayako、Yamakawa, Hiroshi

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Ritsumeikan Univ

Edogawa Ku,Whole Brain Architecture Initiat

2022

10.1016/j.neunet.2022.04.001

Neural Networks

Neural Networks

EISCI
ISSN：0893-6080
年,卷(期)：2022.151
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