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Hippocampal Based Model Reveals the Distinct Roles of Dentate Gyrus and CA3 during Robotic Spatial Navigation

机译:基于海马模型揭示了机器人空间导航中齿状回和CA3的独特作用

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Animals are exemplary explorers and achieve great navigational performances in dynamic environments. Their robotic counterparts still have difficulties in self-localization and environment mapping tasks. Place cells, a type of cell firing at specific positions in the environment, are found in multiple areas of the hippocampal formation. Although, the functional role of these areas with a similar type of cell behavior is still not clearly distinguished. Biomimetic models of navigation have been tested in the context of computer simulations or small and controlled arenas. In this paper, we present a computational model of the hippocampal formation for robotic spatial representation within large environments. Necessary components for the formation of a cognitive map, such as grid and place cells, were obtained through attrac-tor dynamics. Prediction of future hippocampal inputs was performed through self-organization. Obtained data suggests that the integration of the described components is sufficient for robotic space representation. In addition, our results suggest that dentate gyrus (DG), the hippocampal input area, integrates signals from different dorsal-ventral scales of grid cells and that spatial and sensory input are not necessarily associated in this region. Moreover, we present a mechanism for prediction of future hippocampal events based on associative learning.
机译:动物是模范探索者,在动态环境中具有出色的导航性能。他们的机器人同行在自我定位和环境映射任务方面仍然遇到困难。在海马结构的多个区域中都发现了位置细胞,这是一种在环境中特定位置发射的细胞。虽然,这些区域具有相似类型的细胞行为的功能角色仍未明确区分。导航的仿生模型已在计算机模拟或小型受控竞技场的环境中进行了测试。在本文中,我们为大型环境中的机器人空间表示提供了海马结构的计算模型。通过吸引子动力学获得了形成认知图的必要组件,例如网格和放置单元。通过自组织进行未来海马输入的预测。获得的数据表明,所描述组件的集成足以实现机器人空间表示。此外,我们的研究结果表明,海马齿状回(DG)整合了来自不同背-腹尺度的网格细胞的信号,并且空间和感觉输入不一定与此区域相关。此外,我们提出了一种基于联想学习预测未来海马事件的机制。

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