The Path-Integration Properties of Hippocampal Place Cells

2002 ◽  
pp. 41-58 ◽  
Author(s):  
James J. Knierim
Keyword(s):  
Path Integration ◽  
Place Cells

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 Recalibration of path integration in hippocampal place cells

Nature ◽  
2019 ◽  
Vol 566 (7745) ◽  
pp. 533-537 ◽  
Author(s):  
Ravikrishnan P. Jayakumar ◽  
Manu S. Madhav ◽  
Francesco Savelli ◽  
Hugh T. Blair ◽  
Noah J. Cowan ◽  
...  
Keyword(s):  
Path Integration ◽  
Place Cells

scholarly journals Recalibration of path integration in hippocampal place cells

2018 ◽  
Author(s):  
Ravikrishnan P. Jayakumar ◽  
Manu S. Madhav ◽  
Francesco Savelli ◽  
Hugh T. Blair ◽  
Noah J. Cowan ◽  
...  
Keyword(s):  
Path Integration ◽  
Gain Factor ◽  
Place Cells ◽  
Cognitive Map ◽  
Mammalian Brain ◽  
Positional Error ◽  
Visual Landmarks ◽  
Augmented Reality System ◽  
Behavioral Evidence ◽  
Integration Gain

SummaryHippocampal place cells are spatially tuned neurons that serve as elements of a “cognitive map” in the mammalian brain1. To detect the animal’s location, place cells are thought to rely upon two interacting mechanisms: sensing the animal’s position relative to familiar landmarks2,3 and measuring the distance and direction that the animal has travelled from previously occupied locations4–7. The latter mechanism, known as path integration, requires a finely tuned gain factor that relates the animal’s self-movement to the updating of position on the internal cognitive map, with external landmarks necessary to correct positional error that eventually accumulates8,9. Path-integration-based models of hippocampal place cells and entorhinal grid cells treat the path integration gain as a constant9–14, but behavioral evidence in humans suggests that the gain is modifiable15. Here we show physiological evidence from hippocampal place cells that the path integration gain is indeed a highly plastic variable that can be altered by persistent conflict between self-motion cues and feedback from external landmarks. In a novel, augmented reality system, visual landmarks were moved in proportion to the animal’s movement on a circular track, creating continuous conflict with path integration. Sustained exposure to this cue conflict resulted in predictable and prolonged recalibration of the path integration gain, as estimated from the place cells after the landmarks were extinguished. We propose that this rapid plasticity keeps the positional update in register with the animal’s movement in the external world over behavioral timescales (mean 50 laps over 35 minutes). These results also demonstrate that visual landmarks not only provide a signal to correct cumulative error in the path integration system, as has been previously shown4,8,16–19, but also rapidly fine-tune the integration computation itself.

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.

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 Spatial representations of place cells in darkness are supported by path integration and border information

2014 ◽  
Vol 8 ◽  
Author(s):  
Sijie Zhang ◽  
Fabian Schönfeld ◽  
Laurenz Wiskott ◽  
Denise Manahan-Vaughan
Keyword(s):  
Path Integration ◽  
Place Cells ◽  
Spatial Representations

scholarly journals Olfactory Landmarks and Path Integration Converge to Form a Cognitive Spatial Map

2019 ◽  
Author(s):  
Walter M. Fischler ◽  
Narendra R. Joshi ◽  
Virginia Devi-Chou ◽  
Lacey J. Kitch ◽  
Mark J. Schnitzer ◽  
...  
Keyword(s):  
Virtual Environment ◽  
Path Integration ◽  
Iterative Process ◽  
Sensory Information ◽  
Place Cell ◽  
Place Cells ◽  
Virtual Navigation ◽  
Navigation Task ◽  
Odor Cues

AbstractThe convergence of internal path integration with sensory information from external landmarks generates a cognitive spatial map in the hippocampus. We have recorded the activity of cells in CA1 during a virtual navigation task to examine how mice represent, recognize and employ sparse olfactory landmarks to estimate their location. We observe that the presence of odor landmarks at multiple locations in a virtual environment greatly enriches the place cell representation and dramatically improves navigation. Presentation of the same odor at different locations generates distinct place cell representations, indicating that path integration can disambiguate two identical cues on the basis of location. The enhanced place cell representation at one cue location led to the formation of place cells at locations beyond that cue and, ultimately recognition of a second odor cue as a distinct landmark. This suggests an iterative mechanism for extending place cell representations into unknown territory. These results reveal how odor cues can serve as landmarks to guide navigation and suggest a model to explain how the convergence of landmarks and path integration participates in an iterative process that generates a cognitive spatial map.

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.

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