» Articles » PMID: 21085643

Solving Navigational Uncertainty Using Grid Cells on Robots

Overview
Specialty Biology
Date 2010 Nov 19
PMID 21085643
Citations 24
Authors
Affiliations
Soon will be listed here.
Abstract

To successfully navigate their habitats, many mammals use a combination of two mechanisms, path integration and calibration using landmarks, which together enable them to estimate their location and orientation, or pose. In large natural environments, both these mechanisms are characterized by uncertainty: the path integration process is subject to the accumulation of error, while landmark calibration is limited by perceptual ambiguity. It remains unclear how animals form coherent spatial representations in the presence of such uncertainty. Navigation research using robots has determined that uncertainty can be effectively addressed by maintaining multiple probabilistic estimates of a robot's pose. Here we show how conjunctive grid cells in dorsocaudal medial entorhinal cortex (dMEC) may maintain multiple estimates of pose using a brain-based robot navigation system known as RatSLAM. Based both on rodent spatially-responsive cells and functional engineering principles, the cells at the core of the RatSLAM computational model have similar characteristics to rodent grid cells, which we demonstrate by replicating the seminal Moser experiments. We apply the RatSLAM model to a new experimental paradigm designed to examine the responses of a robot or animal in the presence of perceptual ambiguity. Our computational approach enables us to observe short-term population coding of multiple location hypotheses, a phenomenon which would not be easily observable in rodent recordings. We present behavioral and neural evidence demonstrating that the conjunctive grid cells maintain and propagate multiple estimates of pose, enabling the correct pose estimate to be resolved over time even without uniquely identifying cues. While recent research has focused on the grid-like firing characteristics, accuracy and representational capacity of grid cells, our results identify a possible critical and unique role for conjunctive grid cells in filtering sensory uncertainty. We anticipate our study to be a starting point for animal experiments that test navigation in perceptually ambiguous environments.

Citing Articles

New Approaches to 3D Vision.

Linton P, Morgan M, Read J, Vishwanath D, Creem-Regehr S, Domini F Philos Trans R Soc Lond B Biol Sci. 2022; 378(1869):20210443.

PMID: 36511413 PMC: 9745878. DOI: 10.1098/rstb.2021.0443.


xRatSLAM: An Extensible RatSLAM Computational Framework.

de Souza Munoz M, Menezes M, Freitas E, Cheng S, de Almeida Ribeiro P, de Almeida Neto A Sensors (Basel). 2022; 22(21).

PMID: 36366002 PMC: 9657370. DOI: 10.3390/s22218305.


Neurobiologically Inspired Self-Monitoring Systems.

Chiba A, Krichmar J Proc IEEE Inst Electr Electron Eng. 2021; 108(7):976-986.

PMID: 34621081 PMC: 8494143. DOI: 10.1109/JPROC.2020.2979233.


Spiking Neural Networks and Hippocampal Function: A Web-Accessible Survey of Simulations, Modeling Methods, and Underlying Theories.

Sutton N, Ascoli G Cogn Syst Res. 2021; 70:80-92.

PMID: 34504394 PMC: 8423376. DOI: 10.1016/j.cogsys.2021.07.008.


What are grid-like responses doing in the orbitofrontal cortex?.

Raithel C, Gottfried J Behav Neurosci. 2021; 135(2):218-225.

PMID: 33734733 PMC: 8299309. DOI: 10.1037/bne0000453.


References
1.
McNaughton B, Battaglia F, Jensen O, Moser E, Moser M . Path integration and the neural basis of the 'cognitive map'. Nat Rev Neurosci. 2006; 7(8):663-78. DOI: 10.1038/nrn1932. View

2.
Fyhn M, Molden S, Witter M, Moser E, Moser M . Spatial representation in the entorhinal cortex. Science. 2004; 305(5688):1258-64. DOI: 10.1126/science.1099901. View

3.
Hasselmo M . Grid cell mechanisms and function: contributions of entorhinal persistent spiking and phase resetting. Hippocampus. 2008; 18(12):1213-29. PMC: 2614862. DOI: 10.1002/hipo.20512. View

4.
Taube J, Muller R, RANCK Jr J . Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis. J Neurosci. 1990; 10(2):420-35. PMC: 6570151. View

5.
Burgess N, Barry C, OKeefe J . An oscillatory interference model of grid cell firing. Hippocampus. 2007; 17(9):801-12. PMC: 2678278. DOI: 10.1002/hipo.20327. View