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Jauffret A, Cuperlier N, Gaussier P. From grid cells and visual place cells to multimodal place cell: a new robotic architecture. Front Neurorobot. 2015;9:1doi: 10.3389/fnbot.2015.00001.
Jauffret, A., Cuperlier, N., & Gaussier, P. (2015). From grid cells and visual place cells to multimodal place cell: a new robotic architecture. Frontiers in neurorobotics, 91. https://doi.org/10.3389/fnbot.2015.00001
Jauffret, Adrien, et al. "From grid cells and visual place cells to multimodal place cell: a new robotic architecture." Frontiers in neurorobotics vol. 9 (2015): 1. doi: https://doi.org/10.3389/fnbot.2015.00001
Jauffret A, Cuperlier N, Gaussier P. From grid cells and visual place cells to multimodal place cell: a new robotic architecture. Front Neurorobot. 2015 Apr 07;9:1. doi: 10.3389/fnbot.2015.00001. eCollection 2015. PMID: 25904862; PMCID: PMC4388131.
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Front Neurorobot. 2015 Apr 07;9:1. doi: 10.3389/fnbot.2015.00001. eCollection 2015.

From grid cells and visual place cells to multimodal place cell: a new robotic architecture.

Frontiers in neurorobotics

Adrien Jauffret, Nicolas Cuperlier, Philippe Gaussier

Affiliations

  1. ETIS, UMR 8051/ENSEA, Université Cergy-Pontoise, CNRS Cergy, France.

PMID: 25904862 PMCID: PMC4388131 DOI: 10.3389/fnbot.2015.00001

Abstract

In the present study, a new architecture for the generation of grid cells (GC) was implemented on a real robot. In order to test this model a simple place cell (PC) model merging visual PC activity and GC was developed. GC were first built from a simple "several to one" projection (similar to a modulo operation) performed on a neural field coding for path integration (PI). Robotics experiments raised several practical and theoretical issues. To limit the important angular drift of PI, head direction information was introduced in addition to the robot proprioceptive signal coming from the wheel rotation. Next, a simple associative learning between visual place cells and the neural field coding for the PI has been used to recalibrate the PI and to limit its drift. Finally, the parameters controlling the shape of the PC built from the GC have been studied. Increasing the number of GC obviously improves the shape of the resulting place field. Yet, other parameters such as the discretization factor of PI or the lateral interactions between GC can have an important impact on the place field quality and avoid the need of a very large number of GC. In conclusion, our results show our GC model based on the compression of PI is congruent with neurobiological studies made on rodent. GC firing patterns can be the result of a modulo transformation of PI information. We argue that such a transformation may be a general property of the connectivity from the cortex to the entorhinal cortex. Our model predicts that the effect of similar transformations on other kinds of sensory information (visual, tactile, auditory, etc…) in the entorhinal cortex should be observed. Consequently, a given EC cell should react to non-contiguous input configurations in non-spatial conditions according to the projection from its different inputs.

Keywords: entorhinal cortex modeling; grid cells; mobile robot; neural network; place cells

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