基于生物位置细胞放电机理的空间位置表征方法

李伟龙; 吴德伟; 周阳; 杜佳

doi:10.11999/JEIT151331

基于生物位置细胞放电机理的空间位置表征方法

doi: 10.11999/JEIT151331

1.
(空军工程大学信息与导航学院西安 710077) ②(西安通信学院西安 710106)

基金项目:

国家自然科学基金(61273048)

计量
- 文章访问数: 1461
- HTML全文浏览量: 123
- PDF下载量: 411
- 被引次数: 0
出版历程
- 收稿日期: 2015-11-26
- 修回日期: 2016-04-18
- 刊出日期: 2016-08-19

A Method of Spatial Place Representation Based on Biological Place Cells Firing

1.
(Information and Navigation College, Air Force Engineering University, Xi&rsquo

Funds:

The National Natural Science Foundation of China (61273048)

摘要

摘要: 为实现运行体智能自主定位，该文提出基于生物位置细胞放电机理的空间位置表征方法。首先建立运行体空间位置与运行体和不同路标之间距离的对应关系，以两种不同的空间覆盖方式构建位置细胞图，然后利用感知的距离信息激发位置细胞放电估计运行体位置，并分析了模型中各参数对空间位置表征和定位性能的影响。仿真结果表明，两种方法均能通过构建的位置细胞图实现空间位置表征和自主定位，但等间隔离散空间构建位置细胞图方法受距离间隔影响较大，而任意探索构建位置细胞图方法从生物自主感知空间位置的角度出发，通过选择合适的距离阈值和运动训练时间构建位置细胞图，能够更好地完成运行体空间位置表征并且定位精度也较高。
- 自主导航 /
- 仿生定位 /
- 空间表征 /
- 位置细胞
Abstract: In order to realize intelligent and autonomous navigation of vehicles, a method of spatial place representation based on biological place cells firing is proposed. A relationship is built that the spatial location of vehicle is corresponding with the distances between the vehicle and different landmarks, and a map of place cells is constructed in two different ways of space coverage. Then using the real-time distances sensed inspires place cells firing so as to estimate the location of the vehicle. An analysis is carried out about different parameters in the model influence on spatial location expression and the performance of positioning. The simulation results show that both two ways can realize location expression and autonomous positioning through using the map of place cells. The way that the space is separated equally is influenced by the distance interval greatly, and the way of building the map of place cells through exploring randomly, starting off with the perspective of sensing spatial location autonomously, can finish spatial location expression and autonomous positioning better through choosing appropriate interval and trained time.
- Autonomous navigation /
- Biological positioning /
- Spatial representation /
- Place cell

HTML全文

参考文献(18)

BURGESS N. The 2014 nobel prize in physiology or medicine: A spatial model for cognitive neuroscience[J]. Neuron, 2014, 84(6): 1120-1125.

DERDIKMAN D and MOSER E I. A manifold of spatial maps in the brain[J]. Trends in Cognitive Sciences, 2010, 14(12): 561-569.

OKEEFE J and DOSLROVSKV J. The hippocampus as a spatial map[J]. Brain Research, 1971, 34(1): 171-175.

PFEIFFER B E and FOSTER D J. Hippocampal place-cell sequences depict future paths to remembered goals[J]. Nature, 2013, 497: 74-79.

DAVACHI L and DUBROW S. How the hippocampus preserves order: the role of prediction and context[J]. Trends in Cognitive Sciences, 2015, 19(2): 92-99.

WILLS T J and CACUCCI F. The development of the hippocampal neural representation of space[J]. Current Opinion in Neurobiology, 2014, 24: 111-119.

MADL T, CHEN K, MONTALDI D, et al. Computational cognitive models of spatial memory in navigation space: a review[J]. Neural Networks, 2015, 65: 18-43.

HOWARD L R, JAVADI A H, YU Y, et al. The hippocampus and entorhinal cortex encode the path and Euclidean distances to goals during navigation[J]. Current Biology, 2014, 24(12): 1331-1340.

RAYMOND P K and EDMUND T R. A computational theory of hippocampal function, and tests of the theory: new developments[J]. Neuroscience and Biobehavioral Reviews, 2015, 48: 92-147.

HUGO J S and BARRY C. Neural systems supporting navigation[J]. Current Opinion in Behavioral Sciences, 2015, 1: 47-55.

BARRY C and BURGESS N. Neural mechanisms of self- location[J]. Current Biology, 2014, 24: 330-339.

GIOVANNANGELI C and GAUSSIER P. Autonomous vision-based navigation: goal-oriented action planning by transient states prediction, cognitive map building, and sensory-motor learning[C]. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, 2008: 22-26.

JAUFFRET A, CUPERLIER N, GAUSSIER P, et al. Multimodal integration of visual place cells and grid cells for navigation tasks of a real robot[C]. 12th International Conference on Simulation of Adaptive Behavior(SAB 2012), Odense, 2012: 136-145.

ERDEM U M, MILFORD M J, and HASSELMO M E. A hierarchical model of goal directed navigation selects trajectories in a visual environment[J]. Neurobiology of Learning and Memory, 2015, 117: 109-121.

ERDEM U M and HASSELMO M E. A biologically inspired hierarchical goal directed navigation model[J]. Journal of Physiology-Paris, 2014, 108: 28-37.

OKEEFE J and BURGESS N. Geometrical determinants of the place fields of hippocampal neurons[J]. Nature, 1996, 381: 425-428.

李伟龙, 吴德伟, 杜佳, 等. 用于无人机自主定位的仿生视觉路标获取方法[J]. 仪器仪表学报, 2015, 36(6): 1207-1214.

LI Weilong, WU Dewei, DU Jia, et al. The landmark acquisition method based on bionic vision for UAV autonomous positioning[J]. Chinese Journal of Scientific Instrument, 2015, 36(6): 1207-1214.

施引文献

资源附件(0)

访问统计