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摘要: 海马结构空间细胞的放电活动被认为能够形成对环境内在地图的表达，即所谓的认知地图.先前的仿生环境认知地图构建方法（例如RatSLAM）以及传统的SLAM方法均缺乏足够的生理学依据，不能准确地体现出生物在导航中的生理学现象和认知功能实现过程.本文模仿海马结构空间细胞的认知机理提出了一种构建精确的环境认知地图的方法，其特点在于通过构建统一的空间细胞吸引子计算模型对自运动线索进行路径积分；网格细胞和位置细胞对环境的表达来源于条纹细胞的前向驱动作用；通过环境的颜色深度图像进行闭环检测，对空间细胞路径积分进行误差修正，最终生成精确的环境认知地图.该认知地图是一种拓扑度量地图，包含了环境特征点坐标、视觉线索以及特定位点的拓扑关系.本文通过仿真实验和机器人平台物理实验验证了方法的有效性，研究成果为仿海马认知机理的机器人导航方法研究奠定了基础.Abstract: Spatial cells in hippocampus play a functional role in representation and processing of spatial information, which appear to provide a basis for cognitive map:a representation of environment. Most prior biomimetic map building algorithms, such as RatSLAM algorithm or traditional SLAM methods, have little biological fidelity to the hippocampal formation. In this paper, a neural network model based on the behavioral and neurophysiological mechanisms of the spatial cells is constructed, and is applied to building the accurate cognitive map of real environments. The proposed algorithm has a uniform calculation method for spatial cells based on continuous attractor network dynamics to integrate self-motion cues, which can reproduce grid cells firing responses and place cells firing fields via feedforward inputs from band cells. RGB-D images serve as visual cues for loop closure detection and correcting the accumulative errors intrinsically associated with the path integration mechanism, which contributes to building spatial cognitive maps of indoor environments on a mobile robot. A cognitive map is a fine-grained topological-metric map. A node in the cognitive map is constructed by associating the major peak of place cell population activities with corresponding visual cues and the transition stores the change in positions. Simulation experiments and physical experiments with a mobile robot have verified the effectiveness of the algorithm. The proposed algorithm may provide a foundation for robotic navigation.
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Key words:
- Cognitive map /
- hippocampal formation /
- spatial cells /
- band cells /
- loop closure detection
1) 本文责任编委 徐德 -
图 8 位置细胞的连续吸引子模型表达
Fig. 8 The expression of the continuous attractor model of place cells
图 13 老鼠移动轨迹上网格细胞和位置细胞放电率图
Fig. 13 The firing rate of place cells and grid cells based on mouse trajectory
图 17 闭环检测与空间细胞放电重置过程
Fig. 17 Closed loop detection and space cell discharge reset process
图 18 位置细胞相互竞争前后的放电率图
Fig. 18 The firing rate before and after the place cell competition
图 23 网格间距为4 cm时单个神经元的响应图
Fig. 23 The response of a single neuron when the grid space is 4 cm
表 1 参数设置
参数 值 $n_X=n_Y$ 32 $k_p$ 7 $\varphi$ 0.00002 $\rho$ 20 $c_t$ 1 $S_{th}$ 1 $\vartheta$ 0.5 $\mu_R$ 0.65 $\mu_D$ 0.35 
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