scholarly journals EMULATING THE FUNCTIONALITY OF RODENTS’ NEUROBIOLOGICAL NAVIGATION AND SPATIAL COGNITION CELLS IN A MOBILE ROBOT

2015 ◽  
pp. 77-85
Author(s):  
Peter J. Zeno
Keyword(s):  
Spatial Cognition ◽  
Path Integration ◽  
Grid Cell ◽  
Place Cells ◽  
Brain Cells ◽  
Neural Cells ◽  
Spatial Awareness ◽  
Head Direction ◽  
Field Programmable ◽  
Navigational System

A unique roving robot navigational system is presented here, which is inspired by rats’ navigational and spatial awareness brain cells. Rodents, as well as all mammalians, are capable of exploring their surroundings when foraging or avoiding predators, and remembering their way home or to the closest known shelter through path integration. This is true for other creatures, but the neural cells involved in accomplishing these tasks have been most notably studied in rats, as they share certain similarities with a human’s brain. The robot built in this study, named ratbot, uses characteristics and interpreted functionalities of the specialized navigational and spatial cognition brain cells, which are primarily found in the hippocampus and entorhinal cortex. These cells are the: place cells, head direction cells, boundary cells, and grid cells, as well as memory used for the storage and access of salient distal cues. Similar to a rat, the ratbot uses path integration to navigate from one waypoint to another. This is accomplished through use of vectors and vector mathematics. Additionally, the ratbot uses a field programmable gate array (FPGA) to emulate grid cell inspired functionality for environment mapping and spatial cognition.

scholarly journals Path integration in place cells of developing rats

2018 ◽  
Vol 115 (7) ◽  
pp. E1637-E1646 ◽  
Author(s):  
Tale L. Bjerknes ◽  
Nenitha C. Dagslott ◽  
Edvard I. Moser ◽  
May-Britt Moser
Keyword(s):  
Path Integration ◽  
Grid Cell ◽  
Cell System ◽  
Place Cells ◽  
Postnatal Life ◽  
Motion Information ◽  
Grid Cells ◽  
Adult Rats ◽  
Self Motion ◽  
Made In

Place cells in the hippocampus and grid cells in the medial entorhinal cortex rely on self-motion information and path integration for spatially confined firing. Place cells can be observed in young rats as soon as they leave their nest at around 2.5 wk of postnatal life. In contrast, the regularly spaced firing of grid cells develops only after weaning, during the fourth week. In the present study, we sought to determine whether place cells are able to integrate self-motion information before maturation of the grid-cell system. Place cells were recorded on a 200-cm linear track while preweaning, postweaning, and adult rats ran on successive trials from a start wall to a box at the end of a linear track. The position of the start wall was altered in the middle of the trial sequence. When recordings were made in complete darkness, place cells maintained fields at a fixed distance from the start wall regardless of the age of the animal. When lights were on, place fields were determined primarily by external landmarks, except at the very beginning of the track. This shift was observed in both young and adult animals. The results suggest that preweaning rats are able to calculate distances based on information from self-motion before the grid-cell system has matured to its full extent.

Bio-Inspired Robotics: A Spatial Cognition Model integrating Place Cells, Grid Cells and Head Direction Cells

2018 ◽  
Vol 91 (1) ◽  
pp. 85-99 ◽  
Author(s):  
Gonzalo Tejera ◽  
Martin Llofriu ◽  
Alejandra Barrera ◽  
Alfredo Weitzenfeld
Keyword(s):  
Spatial Cognition ◽  
Place Cells ◽  
Head Direction ◽  
Grid Cells ◽  
Head Direction Cells ◽  
Cognition Model

scholarly journals A spatial code in the dorsal lateral geniculate nucleus

2018 ◽  
Author(s):  
Vincent Hok ◽  
Pierre-Yves Jacob ◽  
Pierrick Bordiga ◽  
Bruno Truchet ◽  
Bruno Poucet ◽  
...  
Keyword(s):  
Spatial Memory ◽  
Spatial Cognition ◽  
Lateral Geniculate Nucleus ◽  
Place Cells ◽  
Dorsal Lateral Geniculate Nucleus ◽  
Head Direction ◽  
Head Direction Cells ◽  
Spatial Selectivity ◽  
Lateral Geniculate ◽  
Dorsal Lateral Geniculate

AbstractSince their discovery in the early ‘70s1, hippocampal place cells have been studied in numerous animal and human spatial memory paradigms2–4. These pyramidal cells, along with other spatially tuned types of neurons (e.g. grid cells, head direction cells), are thought to provide the mammalian brain a unique spatial signature characterizing a specific environment, and thereby a memory trace of the subject’s place5. While grid and head direction cells are found in various brain regions, only few hippocampal-related structures showing ‘place cell’-like neurons have been identified6,7, thus reinforcing the central role of the hippocampus in spatial memory. Concurrently, it is increasingly suggested that visual areas play an important role in spatial cognition as recent studies showed a clear spatial selectivity of visual cortical (V1) neurons in freely moving rodents8–10. We therefore thought to investigate, in the rat, such spatial correlates in a thalamic structure located one synapse upstream of V1, the dorsal Lateral Geniculate Nucleus (dLGN), and discovered that a substantial proportion (ca. 30%) of neurons exhibits spatio-selective activity. We found that dLGN place cells maintain their spatial selectivity in the absence of visual inputs, presumably relying on odor and locomotor inputs. We also found that dLGN place cells maintain their place selectivity across sessions in a familiar environment and that contextual modifications yield separated representations. Our results show that dLGN place cells are likely to participate in spatial cognition processes, creating as early as the thalamic stage a comprehensive representation of one given environment.

scholarly journals Towards a Predictive Bio-Inspired Navigation Model

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 100
Author(s):  
Simon Gay ◽  
Kévin Le Le Run ◽  
Edwige Pissaloux ◽  
Katerine Romeo ◽  
Christèle Lecomte
Keyword(s):  
Predictive Model ◽  
Large Scale ◽  
Environmental Changes ◽  
Grid Cell ◽  
Visual Navigation ◽  
Place Cells ◽  
Head Direction ◽  
Head Direction Cells ◽  
Visually Impaired People ◽  
Proposed Model

This paper presents a novel bio-inspired predictive model of visual navigation inspired by mammalian navigation. This model takes inspiration from specific types of neurons observed in the brain, namely place cells, grid cells and head direction cells. In the proposed model, place cells are structures that store and connect local representations of the explored environment, grid and head direction cells make predictions based on these representations to define the position of the agent in a place cell’s reference frame. This specific use of navigation cells has three advantages: First, the environment representations are stored by place cells and require only a few spatialized descriptors or elements, making this model suitable for the integration of large-scale environments (indoor and outdoor). Second, the grid cell modules act as an efficient visual and absolute odometry system. Finally, the model provides sequential spatial tracking that can integrate and track an agent in redundant environments or environments with very few or no distinctive cues, while being very robust to environmental changes. This paper focuses on the architecture formalization and the main elements and properties of this model. The model has been successfully validated on basic functions: mapping, guidance, homing, and finding shortcuts. The precision of the estimated position of the agent and the robustness to environmental changes during navigation were shown to be satisfactory. The proposed predictive model is intended to be used on autonomous platforms, but also to assist visually impaired people in their mobility.

Oscillator-Interference Models of Path Integration Do Not Require Theta Oscillations

2015 ◽  
Vol 27 (3) ◽  
pp. 548-560 ◽  
Author(s):  
Jeff Orchard
Keyword(s):  
Path Integration ◽  
Activity Patterns ◽  
Field Potential ◽  
Grid Cell ◽  
Place Cells ◽  
Theta Oscillations ◽  
Grid Cells ◽  
Neural Oscillators ◽  
Theta Frequency ◽  
Interference Models

Navigation and path integration in rodents seems to involve place cells, grid cells, and theta oscillations (4–12 Hz) in the local field potential. Two main theories have been proposed to explain the neurological underpinnings of how these phenomena relate to navigation and to each other. Attractor network (AN) models revolve around the idea that local excitation and long-range inhibition connectivity can spontaneously generate grid-cell-like activity patterns. Oscillator interference (OI) models propose that spatial patterns of activity are caused by the interference patterns between neural oscillators. In rats, these oscillators have a frequency close to the theta frequency. Recent studies have shown that bats do not exhibit a theta cycle when they crawl, and yet they still have grid cells. This has been interpreted as a criticism of OI models. However, OI models do not require theta oscillations. We explain why the absence of theta oscillations does not contradict OI models and discuss how the two families of models might be distinguished experimentally.

scholarly journals An Integrated Deep Learning Based Model of Hippocampal Spatial Cells That Combines Self-motion With Sensory Information

2021 ◽  
Author(s):  
Azra Aziz ◽  
S S Sree Harsha Peesap ◽  
N Rohan ◽  
V Srinivasa Chakravar
Keyword(s):  
Deep Learning ◽  
Path Integration ◽  
Hippocampal Neurons ◽  
Building Blocks ◽  
Place Cells ◽  
Head Direction ◽  
Grid Cells ◽  
Modeling Framework ◽  
Cell Responses ◽  
Preferred Direction

Abstract A special class of hippocampal neurons broadly known as the spatial cells, whose subcategories include place cells, grid cells and head direction cells, are considered to be the building blocks of the brain’s map of the spatial world. We present a general, deep learning-based modeling framework that describes the emergence of the spatial cell responses and can also explain behavioral responses that involve a combination of path integration and vision. The first layer of the model consists of Head Direction (HD) cells that code for preferred direction of the agent. The second layer is the path integration (PI) layer with oscillatory neurons: displacement of the agent in a given direction modulates the frequency of these oscillators. Principal Component Analysis (PCA) of the PI cell responses showed emergence of cells with grid-like spatial periodicity. We show that the response of these cells could be described by Bessel functions. The output of PI layer is used to train stack of autoencoders. Neurons of both the layers exhibit responses resembling grid cells and place cells. The paper concludes by suggesting a wider applicability of the proposed modeling framework beyond the two simulated behavioral studies.

scholarly journals Middle-scale navigation: the vertebrate case

1996 ◽  
Vol 199 (1) ◽  
pp. 163-164
Author(s):  
DF Sherry
Keyword(s):  
Path Integration ◽  
Brain Regions ◽  
Place Cells ◽  
Cognitive Map ◽  
Head Direction ◽  
Neural Basis ◽  
Head Direction Cells ◽  
Complete Rejection ◽  
Neural Bases

Few ideas have had a greater impact on the study of navigation at the middle scale than the theory of the cognitive map. As papers in this section show, current views of the cognitive map range from complete rejection of the idea (Bennett, 1996) to new proposals for the behavioural and neural bases of the cognitive map (Gallistel and Cramer, 1996; McNaughton et al. 1996). The papers in this section also make it clear that path integration has taken centre stage in theorizing about navigation at the middle scale. Path integration is the use of information generated by locomotion to determine the current distance and direction to the origin of the path. Etienne (1980) provided one of the first experimental demonstrations of path integration by a vertebrate, and in this section Etienne et al. (1996) describe recent research with animals and humans on the interaction between path integration and landmark information. Path integration is also the fundamental means of navigation in the model described by Gallistel and Cramer (1996). McNaughton et al. (1996) suggest that the neural basis of path integration is found in the place cells and head direction cells of the hippocampus and associated brain regions.

scholarly journals Deciphering the hippocampal polyglot: the hippocampus as a path integration system.

1996 ◽  
Vol 199 (1) ◽  
pp. 173-185 ◽  
Author(s):  
B L McNaughton ◽  
C A Barnes ◽  
J L Gerrard ◽  
K Gothard ◽  
M W Jung ◽  
...  
Keyword(s):  
Visual Information ◽  
Path Integration ◽  
Dimensional Space ◽  
Internal Representation ◽  
Place Cells ◽  
Head Direction ◽  
Integration System ◽  
Spatial Reference ◽  
Self Motion ◽  
Path Integrator

Hippocampal 'place' cells and the head-direction cells of the dorsal presubiculum and related neocortical and thalamic areas appear to be part of a preconfigured network that generates an abstract internal representation of two-dimensional space whose metric is self-motion. It appears that viewpoint-specific visual information (e.g. landmarks) becomes secondarily bound to this structure by associative learning. These associations between landmarks and the preconfigured path integrator serve to set the origin for path integration and to correct for cumulative error. In the absence of familiar landmarks, or in darkness without a prior spatial reference, the system appears to adopt an initial reference for path integration independently of external cues. A hypothesis of how the path integration system may operate at the neuronal level is proposed.

scholarly journals A structured scaffold underlies activity in the hippocampus

2021 ◽  
Author(s):  
Dounia Mulders ◽  
Man Yi Yim ◽  
Jae Sung Lee ◽  
Albert K. Lee ◽  
Thibaud Taillefumier ◽  
...  
Keyword(s):  
Random Process ◽  
Grid Cell ◽  
Place Cell ◽  
Place Cells ◽  
Head Direction ◽  
Cell Responses ◽  
Neural Populations ◽  
External Cues ◽  
One Step ◽  
Design Experiments

Place cells are believed to organize memory across space and time, inspiring the idea of the cognitive map. Yet unlike the structured activity in the associated grid and head-direction cells, they remain an enigma: their responses have been difficult to predict and are complex enough to be statistically well-described by a random process. Here we report one step toward the ultimate goal of understanding place cells well enough to predict their fields. Within a theoretical framework in which place fields are derived as a conjunction of external cues with internal grid cell inputs, we predict that even apparently random place cell responses should reflect the structure of their grid inputs and that this structure can be unmasked if probed in sufficiently large neural populations and large environments. To test the theory, we design experiments in long, locally featureless spaces to demonstrate that structured scaffolds undergird place cell responses. Our findings, together with other theoretical and experimental results, suggest that place cells build memories of external inputs by attaching them to a largely prespecified grid scaffold.

scholarly journals An Oscillatory Network Model of Head Direction, Spatially Periodic Cells and Place Cells Using Locomotor Inputs

2016 ◽  
Author(s):  
Karthik Soman ◽  
Vignesh Muralidharan ◽  
V. Srinivasa Chakravarthy
Keyword(s):  
Path Integration ◽  
Place Cells ◽  
Border Cells ◽  
Head Direction ◽  
Grid Cells ◽  
Head Direction Cells ◽  
Modeling Approach ◽  
Spatially Periodic ◽  
Locomotor Rhythms ◽  
Oscillatory Network

AbstractWe propose a computational modeling approach that explains the formation of a range of spatial cells like head direction cells, grid cells, border cells and place cells which are believed to play a pivotal role in the spatial navigation of an animal. Most existing models insert special symmetry conditions in the models in order to obtain such symmetries in the outcome; our models do not require such symmetry assumptions. Our modeling approach is embodied in two models: a simple one (Model #1) and a more detailed version (Model #2). In Model #1, velocity input is presented to a layer of Head Direction cells, with no special topology requirements, the outputs of which are presented to a layer of Path Integration neurons. A variety of spatially periodic responses resembling grid cells, are obtained using the Principal Components of Path Integration layer. In Model #2, the input consists of the locomotor rhythms from the four legs of a virtual animal. These rhythms are integrated into the phases of a layer of oscillatory neurons, whose outputs drive a layer of Head Direction cells. The Head Direction cells in turn drive a layer of Path Integration neurons, which in turn project to two successive layers of Lateral Anti Hebbian Networks (LAHN). Cells in the first LAHN resemble grid cells (with both hexagonal and square gridness), and border cells. Cells in the second LAHN exhibit place cell behaviour and a new cell type known as corner cell. Both grid cells and place cells exhibit phase precession in 1D and 2D spaces. The models outline the neural hierarchy necessary to obtain the complete range of spatial cell responses found in the hippocampal system.

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