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 Both visual and idiothetic cues contribute to head direction cell stability during navigation along complex routes

2011 ◽  
Vol 105 (6) ◽  
pp. 2989-3001 ◽  
Author(s):  
Ryan M. Yoder ◽  
Benjamin J. Clark ◽  
Joel E. Brown ◽  
Mignon V. Lamia ◽  
Stephane Valerio ◽  
...  
Keyword(s):  
Visual Information ◽  
Path Integration ◽  
Visual Cues ◽  
Cell Activity ◽  
Neural Representation ◽  
Head Direction ◽  
Motion Cues ◽  
Cell Stability ◽  
Self Motion ◽  
The Relationship

Successful navigation requires a constantly updated neural representation of directional heading, which is conveyed by head direction (HD) cells. The HD signal is predominantly controlled by visual landmarks, but when familiar landmarks are unavailable, self-motion cues are able to control the HD signal via path integration. Previous studies of the relationship between HD cell activity and path integration have been limited to two or more arenas located in the same room, a drawback for interpretation because the same visual cues may have been perceptible across arenas. To address this issue, we tested the relationship between HD cell activity and path integration by recording HD cells while rats navigated within a 14-unit T-maze and in a multiroom maze that consisted of unique arenas that were located in different rooms but connected by a passageway. In the 14-unit T-maze, the HD signal remained relatively stable between the start and goal boxes, with the preferred firing directions usually shifting <45° during maze traversal. In the multiroom maze in light, the preferred firing directions also remained relatively constant between rooms, but with greater variability than in the 14-unit maze. In darkness, HD cell preferred firing directions showed marginally more variability between rooms than in the lighted condition. Overall, the results indicate that self-motion cues are capable of maintaining the HD cell signal in the absence of familiar visual cues, although there are limits to its accuracy. In addition, visual information, even when unfamiliar, can increase the precision of directional perception.

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.

scholarly journals Learning accurate path integration in a ring attractor model of the head direction system

2021 ◽  
Author(s):  
Pantelis Vafidis ◽  
David Owald ◽  
Tiziano D’Albis ◽  
Richard Kempter
Keyword(s):  
Supervised Learning ◽  
Network Model ◽  
Path Integration ◽  
Internal Representation ◽  
Learning Rule ◽  
Developmental Phase ◽  
Head Direction ◽  
Optogenetic Stimulation ◽  
Attractor Model ◽  
The One

SummaryRing attractor models for angular path integration have recently received strong experimental support. To function as integrators, head-direction (HD) circuits require precisely tuned connectivity, but it is currently unknown how such tuning could be achieved. Here, we propose a network model in which a local, biologically plausible learning rule adjusts synaptic efficacies during development, guided by supervisory allothetic cues. Applied to the Drosophila HD system, the model learns to path-integrate accurately and develops a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading, and where the network remaps to integrate with different gains. Our model predicts that path integration requires supervised learning during a developmental phase. The model setting is general and also applies to architectures that lack the physical topography of a ring, like the mammalian HD system.

scholarly journals Neural dynamics indicate parallel integration of environmental and self-motion information by place and grid cells

2019 ◽  
Author(s):  
Dmitri Laptev ◽  
Neil Burgess
Keyword(s):  
Path Integration ◽  
Temporal Dynamics ◽  
Hippocampal Formation ◽  
Place Cell ◽  
Place Cells ◽  
Motion Information ◽  
Grid Cells ◽  
Attractor Network ◽  
Cell Firing ◽  
Self Motion

AbstractPlace cells and grid cells in the hippocampal formation are thought to integrate sensory and self-motion information into a representation of estimated spatial location, but the precise mechanism is unknown. We simulated a parallel attractor system in which place cells form an attractor network driven by environmental inputs and grid cells form an attractor network performing path integration driven by self-motion, with inter-connections between them allowing both types of input to influence firing in both ensembles. We show that such a system is needed to explain the spatial patterns and temporal dynamics of place cell firing when rats run on a linear track in which the familiar correspondence between environmental and self-motion inputs is changed (Gothard et al., 1996b; Redish et al., 2000). In contrast, the alternative architecture of a single recurrent network of place cells (performing path integration and receiving environmental inputs) cannot reproduce the place cell firing dynamics. These results support the hypothesis that grid and place cells provide two different but complementary attractor representations (based on self-motion and environmental sensory inputs respectively). Our results also indicate the specific neural mechanism and main predictors of hippocampal map realignment and make predictions for future studies.

scholarly journals A theoretical account of cue averaging in the rodent head direction system

2014 ◽  
Vol 369 (1635) ◽  
pp. 20130283 ◽  
Author(s):  
Hector J. I. Page ◽  
Daniel M. Walters ◽  
Rebecca Knight ◽  
Caitlin E. Piette ◽  
Kathryn J. Jeffery ◽  
...  
Keyword(s):  
Visual Information ◽  
Path Integration ◽  
Cell Layer ◽  
Neural Systems ◽  
Head Direction ◽  
Sources Of Information ◽  
Cell Responses ◽  
Simulation Results ◽  
Present Simulation ◽  
Cue Conflict

Head direction (HD) cell responses are thought to be derived from a combination of internal (or idiothetic) and external (or allothetic) sources of information. Recent work from the Jeffery laboratory shows that the relative influence of visual versus vestibular inputs upon the HD cell response depends on the disparity between these sources. In this paper, we present simulation results from a model designed to explain these observations. The model accurately replicates the Knight et al. data. We suggest that cue conflict resolution is critically dependent on plastic remapping of visual information onto the HD cell layer. This remap results in a shift in preferred directions of a subset of HD cells, which is then inherited by the rest of the cells during path integration. Thus, we demonstrate how, over a period of several minutes, a visual landmark may gain cue control. Furthermore, simulation results show that weaker visual landmarks fail to gain cue control as readily. We therefore suggest a second longer term plasticity in visual projections onto HD cell areas, through which landmarks with an inconsistent relationship to idiothetic information are made less salient, significantly hindering their ability to gain cue control. Our results provide a mechanism for reliability-weighted cue averaging that may pertain to other neural systems in addition to the HD system.

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 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.

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.

Reproduction of Object Shape is More Accurate without the Continued Availability of Visual Information

Perception ◽  
1998 ◽  
Vol 27 (1) ◽  
pp. 69-86 ◽  
Author(s):  
Michel-Ange Amorim ◽  
Jack M Loomis ◽  
Sergio S Fukusima
Keyword(s):  
Visual Information ◽  
Internal Representation ◽  
Stimulus Configuration ◽  
The Other ◽  
Additional Information ◽  
Distant Stimulus ◽  
Perspective Change ◽  
Visual Presence ◽  
Viewpoint Change ◽  
Actual Change

An unfamiliar configuration lying in depth and viewed from a distance is typically seen as foreshortened. The hypothesis motivating this research was that a change in an observer's viewpoint even when the configuration is no longer visible induces an imaginal updating of the internal representation and thus reduces the degree of foreshortening. In experiment 1, observers attempted to reproduce configurations defined by three small glowing balls on a table 2 m distant under conditions of darkness following ‘viewpoint change’ instructions. In one condition, observers reproduced the continuously visible configuration using three other glowing balls on a nearer table while imagining standing at the distant table. In the other condition, observers viewed the configuration, it was then removed, and they walked in darkness to the far table and reproduced the configuration. Even though the observers received no additional information about the stimulus configuration in walking to the table, they were more accurate (less foreshortening) than in the other condition. In experiment 2, observers reproduced distant configurations on a nearer table more accurately when doing so from memory than when doing so while viewing the distant stimulus configuration. In experiment 3, observers performed both the real and imagined perspective change after memorizing the remote configuration. The results of the three experiments indicate that the continued visual presence of the target configuration impedes imaginary perspective-change performance and that an actual change in viewpoint does not increase reproduction accuracy substantially over that obtained with an imagined change in viewpoint.

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