Location Estimation Model Based on the Transformation from Grid Cells to Place Cells

ZHOU Yang; WU Dewei

doi:10.11999/JEIT161284

Volume 39 Issue 9

Sep. 2017

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(9): 2272-2276

ZHOU Yang, WU Dewei. Location Estimation Model Based on the Transformation from Grid Cells to Place Cells[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2272-2276. doi: 10.11999/JEIT161284

Citation:

ZHOU Yang, WU Dewei. Location Estimation Model Based on the Transformation from Grid Cells to Place Cells[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2272-2276. doi: 10.11999/JEIT161284

Citation:

PDF( 1764 KB)

Location Estimation Model Based on the Transformation from Grid Cells to Place Cells

doi: 10.11999/JEIT161284

ZHOU Yang¹,
WU Dewei¹

Funds:

The National Natural Science Foundation of China (61273048, 61603409)

Received Date: 2016-11-25
Rev Recd Date: 2017-03-17
Publish Date: 2017-09-19

Abstract

Abstract

To achieve intelligent and autonomous positioning for the vehicle, this paper presents a location estimation model based on the transformation from grid cells to place cells. Combining with the firing characteristic of the grid cells, place cells, and the information transformation between them, this location estimation model is divided into three parts, including the learning and memorizing of spatial environment, the perception of the motion state and the estimation of the spatial location. The principle and the specific steps of each part are discussed. Finally, the proposed model is applied to vehicles positioning by simulation. Simulation validates that the proposed model is feasible to achieve vehicles autonomous positioning, and the positioning performance can be adjusted by changing the parameters of grid cells and place cells included in the model.
- Bionic navigation,
- Location estimation,
- Grid cells,
- Place cells,
- Radial basis function neural network

FullText(HTML)

References(18)

References

李伟龙, 吴德伟, 周阳, 等. 基于生物位置细胞放电机理的空间位置表征方法[J]. 电子与信息学报, 2016, 38(8): 2040-2046. doi: 10.11999/JEIT151331.

LI Weilong, WU Dewei, ZHOU Yang, et al. A method of spatial place representation based on biological place cells firing[J]. Journal of Electronics Information Technology, 2016, 38(8): 2040-2046. doi: 10.11999/JEIT151331.

ZHOU Y, WU D W, DU J, et al. A computational model for landmarks acquisition in positioning[J]. Journal of Intelligent Robotic Systems, 2016, 82(3): 537-553. doi: 10.1007/ s10846-015-0276-1.

OKEEFE J and DOSLROVSKV J. The hippocampus as a spatial map. preliminary evidence from unit activity in the freely-moving rat[J]. Brain Research, 1971, 34(1): 171-175. doi: 10.1016/0006-8993(71)90358-1.

KEINATH A T. The preferred directions of conjunctive grid x head direction cells in the medial entorhinal cortex are periodically organized[J]. Plos One, 2016, 11(3): e0152041. doi: 10.1371/journal.pone.0152041.

BUSH D, BARRY C, MANSON D, et al. Using grid cells for navigation[J]. Neuron, 2015, 87(3): 507520. doi: 10.1016/j. neuron.2015.07.006.

MCNAUGHTON B L, BATTAGLIA F P, JENSEN O, et al. Path integration and the neural basis of the cognitive map [J]. Neuroscience, 2006, 7(8): 663-678. doi: 10.1038/nrn1932.

GAUSSIER P, REVEL A, BANQUET J P, et al. From view cells and place cells to cognitive map learning: Processing stages of the hippocampal system[J]. Biological Cybernetics, 2002, 86(1): 15-28. doi: 10.1007/s004220100269.

JAUFFRET A, CUPERLIER N, GAUSSIER P, et al. Multimodal integration of visual place cells and grid cells for navigation tasks of a real robot[J]. LNAI, 2012: 136-145. doi: 10.1007/978-3-642-33093-3_14.

WALTERS D M and STRINGER S M. Path integration of head direction: Updating a packet of neural activity at the correct speed using neuronal time constants[J]. Plos One, 2013, 8(3): e58330. doi: 10.1371/journal.pone.0058330.

ERDEM U M and HASSELMO M E. A biologically inspired hierarchical goal directed navigation model[J]. Journal of Physiology-Paris, 2014, 108(1): 28-37. doi: 10.1016/j. jphysparis.2013.07.002.

HAFTING T, FYHN M, MOLDEN S, et al. Microstructure of a spatial map in the entorhinal cortex[J]. Nature, 2005, 436(7052): 801-806. doi: 10.1038/nature03721.

LYTTLE D, LIN K, and FELLOUS J M. Coding, stability, and non-spatial inputs in a modular grid-to-place cell model[J]. BMC Neuroscience, 2012, 13(1): 141-142. doi: 10.1186/1471-2202-13-s1-p141.

JAUFFRET A, CUPERLIER N, and GAUSSIER P. From grid cells and visual place cells to multimodal place cell: A new robotic architecture[J]. Frontiers in Neurorobotics, 2015, 9: 1-20. doi: 10.3389/fnbot.2015.00001.

MOSER E I, KROPFF E, and MOSER M B. Place cells, grid cells, and the brains spatial representation system[J]. Annual Review of Neuroscience, 2008, 31(1): 69-89. doi: 10.1146/ annurev.neuro.31.061307.090723.

GIOCOMO L M, MOSER M B, and MOSER E I. Computational models of grid cells[J]. Neuron, 2011, 71(4): 589-603. doi: 10.1016/j.neuron.2011.07.023.

SI B and TREVES A. The role of competitive learning in the generation of DG fields from EC inputs[J]. Cognitive Neurodynamics, 2009, 3(2): 177-187. doi: 10.1007/s11571- 009-9079-z.

OKEEFE J and BURGES N. Geometrical determinants of the place fields of hippocampal neurons[J]. Nature, 1996, 381(6581): 425-428. doi: 10.1038/381425a0.

Relative Articles

Supplements(0)

Cited By

Proportional views