Biomimetic FPGA-based spatial navigation model with grid cells and place cells

Krishna, A. Vijaya; Mittal, Divyansh; Garudanagiri, Virupaksha, Siri; Nair, Abhishek Ramdas; Narayanan, Rishikesh; Thakur, Chetan Singh

doi:10.1016/j.neunet.2021.01.028

Cited by 7 publications

(2 citation statements)

References 115 publications

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“…Moser et al, 2017;Rowland et al, 2018;Angelaki & Laurens, 2020;Tukker et al, 2022), and the role of visual and other types of information for the peculiar grid cell activity is the subject of hypotheses (Connor & Knierim, 2017;Campbell & Giocomo, 2018;Jacob, Capitano, Poucet, Save, & Sargolini, 2019;Jayakumar et al, 2019;Moon, Gauthier, Park, Faivre & Blanke, 2022;Waaga et al, 2022). Although, there exists a considerable number of models, aimed to answer this questions (Finkel- Naumann et al, 2018;Widloski, Marder & Fiete, 2018;Kang & Balasubramanian, 2019;Mosheiff & Burak, 2019;Park, Jang, Kim & Kwag, 2019;Rodríguez-Domínguez & Caplan, 2019;Spalla, Dubreuil, Rosay, Monasson, & Treves, 2019;Agmon & Burak, 2020;D'Albis & Kempter, 2020;Ekstrom, Harootonian & Huffman, 2020;Vinepinsky, Perchik, & Segev, 2020;Waniek, 2020;Krishna et al, 2021;Rueckemann, Sosa, Giocomo & Buffalo, 2021;T. Wang, Yang, Z. Wang, Zhang & W. Wang, 2021;Tukker et al, 2022 and many others).…”

Section: Entorhinal Patches and Grid Cellsmentioning

confidence: 99%

Five discoveries of Volodymyr Betz. Part one. Betz and the islands of entorhinal cortex

Medvediev

Cherkasov

Vaslovych³

et al. 2023

USMYJ

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in the series of publications, which this article opens, we consider five fundamental neuromorphological observations of our compatriot, the classic of world neuroscience, Volodymr Betz. For four of them, we demonstrate the status of discoveries for the first time, for the other one — the giant pyramidal neurons of the motor cortex — despite its widespread and long-standing recognition, we reveal the limitations of modern knowledge and ideas. Two of the mentioned observations — giant fusiform, or spindle-shaped neurons of the cingulate and insular cortex and islands of the entorhinal cortex — are currently known only to a narrow circle of specialists; to the others two, islands of Calleja and signs of the columnar cortex organization, we pay attention for the first time. In this, the initial part of the series, exposing modern apprehensions about the structure and functions of the entorhinal cortex, we demonstrate the weight of Betz's pioneering observation of extraordinary neuron clusters, which are nowadays known as entorhinal islands, and prove the fact of recognizing his historical priority in the description of these amazing brain structures.

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Section: Entorhinal Patches and Grid Cellsmentioning

confidence: 99%

Five discoveries of Volodymyr Betz. Part one. Betz and the islands of entorhinal cortex

Medvediev

Cherkasov

Vaslovych³

et al. 2023

USMYJ

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“…In 2005, Hafting et al identified another type of cell in the entorhinal cortex of rats that produces periodic firing to specific regions of space grid cells and whose hexagonal firing fields spread throughout the spatial environment with the movement of the rat [11]. Related studies have shown that when rats move freely in a two-dimensional space, grid cells in the entorhinal cortex undergo repetitive firing behavior at specific locations; furthermore, it was noted that their firing activity is highly stable and, as rats continue to explore the environment, the generated grid cells cover the entire environment and complete the spatial representation of the environment [12][13][14]. Barry et al proposed an oscillatory interference (OI) to model the hexagonal firing structure of the grid cell.…”

Section: Introductionmentioning

confidence: 99%

A Spatial Location Representation Method Incorporating Boundary Information

Jiang

Zhang

2023

Applied Sciences

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In response to problems concerning the low autonomous localization accuracy of mobile robots in unknown environments and large cumulative errors due to long time running, a spatial location representation method incorporating boundary information (SLRB) is proposed, inspired by the mammalian spatial cognitive mechanism. In modeling the firing characteristics of boundary cells to environmental boundary information, we construct vector relationships between the mobile robot and environmental boundaries with direction-aware information and distance-aware information. The self-motion information (direction and velocity) is used as the input to the lateral anti-Hebbian network (LAHN) to generate grid cells. In addition, the boundary cell response values are used to update the grid cell distribution law and to suppress the error response of the place cells, thus reducing the localization error of the mobile robot. Meanwhile, when the mobile robot reaches the boundary cell excitation zone, the activated boundary cells are used to correct the accumulated errors that occur due to long running times, which thus improves the localization accuracy of the system. The main contributions of this paper are as follows: 1. We propose a novel method for constructing boundary cell models. 2. An approach is presented that maps the response values of boundary cells to the input layer of LAHN (Location-Adaptive Hierarchical Network), where grid cells are generated through LAHN learning rules, and the distribution pattern of grid cells is adjusted using the response values of boundary cells. 3. We correct the cumulative error caused by long-term operation of place cells through the activation of boundary cells, ensuring that only one place cell responds to the current location at each individual moment, thereby improving the positioning accuracy of the system.

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