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 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 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 A general model of hippocampal and dorsal striatal learning and decision making

2020 ◽  
Vol 117 (49) ◽  
pp. 31427-31437
Author(s):  
Jesse P. Geerts ◽  
Fabian Chersi ◽  
Kimberly L. Stachenfeld ◽  
Neil Burgess
Keyword(s):  
Decision Making ◽  
Reference Frame ◽  
Place Cells ◽  
Cognitive Map ◽  
Relational Structure ◽  
Place Learning ◽  
Response Learning ◽  
Adaptive Combination ◽  
Stimulus Response ◽  
Model Free

Humans and other animals use multiple strategies for making decisions. Reinforcement-learning theory distinguishes between stimulus–response (model-free; MF) learning and deliberative (model-based; MB) planning. The spatial-navigation literature presents a parallel dichotomy between navigation strategies. In “response learning,” associated with the dorsolateral striatum (DLS), decisions are anchored to an egocentric reference frame. In “place learning,” associated with the hippocampus, decisions are anchored to an allocentric reference frame. Emerging evidence suggests that the contribution of hippocampus to place learning may also underlie its contribution to MB learning by representing relational structure in a cognitive map. Here, we introduce a computational model in which hippocampus subserves place and MB learning by learning a “successor representation” of relational structure between states; DLS implements model-free response learning by learning associations between actions and egocentric representations of landmarks; and action values from either system are weighted by the reliability of its predictions. We show that this model reproduces a range of seemingly disparate behavioral findings in spatial and nonspatial decision tasks and explains the effects of lesions to DLS and hippocampus on these tasks. Furthermore, modeling place cells as driven by boundaries explains the observation that, unlike navigation guided by landmarks, navigation guided by boundaries is robust to “blocking” by prior state–reward associations due to learned associations between place cells. Our model, originally shaped by detailed constraints in the spatial literature, successfully characterizes the hippocampal–striatal system as a general system for decision making via adaptive combination of stimulus–response learning and the use of a cognitive map.

scholarly journals Optimal multiguidance integration in insect navigation

2018 ◽  
Vol 115 (11) ◽  
pp. 2824-2829 ◽  
Author(s):  
Thierry Hoinville ◽  
Rüdiger Wehner
Keyword(s):  
Path Integration ◽  
Cognitive Map ◽  
Model Organisms ◽  
Terrestrial Environment ◽  
Insect Navigation ◽  
Desert Ants ◽  
Local Vector ◽  
The Individual ◽  
Global Vector ◽  
Global And Local

In the last decades, desert ants have become model organisms for the study of insect navigation. In finding their way, they use two major navigational routines: path integration using a celestial compass and landmark guidance based on sets of panoramic views of the terrestrial environment. It has been claimed that this information would enable the insect to acquire and use a centralized cognitive map of its foraging terrain. Here, we present a decentralized architecture, in which the concurrently operating path integration and landmark guidance routines contribute optimally to the directions to be steered, with “optimal” meaning maximizing the certainty (reliability) of the combined information. At any one time during its journey, the animal computes a path integration (global) vector and landmark guidance (local) vector, in which the length of each vector is proportional to the certainty of the individual estimates. Hence, these vectors represent the limited knowledge that the navigator has at any one place about the direction of the goal. The sum of the global and local vectors indicates the navigator’s optimal directional estimate. Wherever applied, this decentralized model architecture is sufficient to simulate the results of quite a number of diverse cue-conflict experiments, which have recently been performed in various behavioral contexts by different authors in both desert ants and honeybees. They include even those experiments that have deliberately been designed by former authors to strengthen the evidence for a metric cognitive map in bees.

Children five-to-nine years old can use path integration to build a cognitive map without vision

2020 ◽  
Vol 121 ◽  
pp. 101307 ◽  
Author(s):  
Mathilde Bostelmann ◽  
Pierre Lavenex ◽  
Pamela Banta Lavenex
Keyword(s):  
Path Integration ◽  
Cognitive Map

scholarly journals Different encoding of reward location in dorsal and ventral hippocampus

2021 ◽  
Author(s):  
Przemyslaw Jarzebowski ◽  
Y. Audrey Hay ◽  
Benjamin F. Grewe ◽  
Ole Paulsen
Keyword(s):  
Calcium Imaging ◽  
Hippocampal Neurons ◽  
Dorsal Hippocampus ◽  
Spatial Navigation ◽  
Ventral Hippocampus ◽  
Place Cells ◽  
Cognitive Map ◽  
Population Activity ◽  
Reward Seeking ◽  
Seeking Behavior

SummaryHippocampal neurons encode a cognitive map for spatial navigation1. When they fire at specific locations in the environment, they are known as place cells2. In the dorsal hippocampus place cells accumulate at current navigational goals, such as learned reward locations3–6. In the intermediate-to-ventral hippocampus (here collectively referred to as ventral hippocampus), neurons fire across larger place fields7–10 and regulate reward- seeking behavior11–16, but little is known about their involvement in reward-directed navigation. Here, we compared the encoding of learned reward locations in the dorsal and ventral hippocampus during spatial navigation. We used calcium imaging with a head- mounted microscope to track the activity of CA1 cells over multiple days during which mice learned different reward locations. In dorsal CA1 (dCA1), the overall number of active place cells increased in anticipation of reward but the recruited cells changed with the reward location. In ventral CA1 (vCA1), the activity of the same cells anticipated the reward locations. Our results support a model in which the dCA1 cognitive map incorporates a changing population of cells to encode reward proximity through increased population activity, while the vCA1 provides a reward-predictive code in the activity of a specific subpopulation of cells. Both of these location-invariant codes persisted over time, and together they provide a dual hippocampal reward-location code, assisting goal- directed navigation17, 18.

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