Origin and role of path integration in the cognitive representations of the hippocampus: computational insights into open questions

Savelli, Francesco; Knierim, James

doi:10.1242/jeb.188912

Cited by 73 publications

(93 citation statements)

References 187 publications

(247 reference statements)

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“…These results indicate that PI and visual place or route learning do not combine to form a 'cognitive map', a point considered in more detail by Webb (2019). More elaborate interactions between PI and visual cues to location are found in rodents (Savelli and Knierim, 2019). Although PI and visual route circuitry are not intertwined centrally, their outputs come together in setting the direction of an ant's path.…”

Section: Some Uses and Limitations Of Pimentioning

confidence: 86%

Path integration: how details of the honeybee waggle dance and the foraging strategies of desert ants might help in understanding its mechanisms

Collett

2019

Journal of Experimental Biology

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Path integration is a navigational strategy that gives an animal an estimate of its position relative to some starting point. For many decades, ingenious and probing behavioural experiments have been the only window onto the operation of path integration in arthropods. New methods have now made it possible to visualise the activity of neural circuits in Drosophila while they fly or walk in virtual reality. Studies of this kind, as well as electrophysiological recordings from single neurons in the brains of other insects, are revealing details of the neural mechanisms that control an insect's direction of travel and other aspects of path integration. The aim here is first to review the major features of path integration in foraging desert ants and honeybees, the current champion path integrators of the insect world, and second consider how the elaborate behaviour of these insects might be accommodated within the framework of the newly understood neural circuits. The discussion focuses particularly on the ability of ants and honeybees to use a celestial compass to give direction in Earth-based coordinates, and of honeybees to use a landscape panorama to provide directional guidance for path integration. The possibility is raised that well-ordered behaviour might in some cases substitute for complex circuitry. A 5 min Activation Distance from centre (cm) 0 3 6 B C 10 cm REVIEW

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Section: Some Uses and Limitations Of Pimentioning

confidence: 86%

Path integration: how details of the honeybee waggle dance and the foraging strategies of desert ants might help in understanding its mechanisms

Collett

2019

Journal of Experimental Biology

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“…Error accumulation in path integration causes a drift. It was suggested that the drift is corrected by temporarily resetting the grid cell signal generated continuously through path integration by synaptic input from border cells [84]. Border cells are present in MEC, Sb, ParaSb, and PreSb [74].…”

Section: Various Cell-firing Patterns In Hippocampal Formationmentioning

confidence: 99%

“…In experiments in a large and novel environment, place cells did not form spatial firing fields when the medial septum was inactivated [108]. It is essential to examine how animals search in large and novel environments [84,109]. In novel environments, animals immediately adopt a "home base" near noticeable landmarks [110,111].…”

Section: Cue-based Strategiesmentioning

confidence: 99%

Identifying Core Regions for Path Integration on Medial Entorhinal Cortex of Hippocampal Formation

Fukawa

Aizawa

Yamakawa³

et al. 2020

Brain Sciences

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Path integration is one of the functions that support the self-localization ability of animals. Path integration outputs position information after an animal’s movement when initial-position and movement information is input. The core region responsible for this function has been identified as the medial entorhinal cortex (MEC), which is part of the hippocampal formation that constitutes the limbic system. However, a more specific core region has not yet been identified. This research aims to clarify the detailed structure at the cell-firing level in the core region responsible for path integration from fragmentarily accumulated experimental and theoretical findings by reviewing 77 papers. This research draws a novel diagram that describes the MEC, the hippocampus, and their surrounding regions by focusing on the MEC’s input/output (I/O) information. The diagram was created by summarizing the results of exhaustively scrutinizing the papers that are relative to the I/O relationship, the connection relationship, and cell position and firing pattern. From additional investigations, we show function information related to path integration, such as I/O information and the relationship between multiple functions. Furthermore, we constructed an algorithmic hypothesis on I/O information and path-integration calculation method from the diagram and the information of functions related to path integration. The algorithmic hypothesis is composed of regions related to path integration, the I/O relations between them, the calculation performed there, and the information representations (cell-firing pattern) in them. Results of examining the hypothesis confirmed that the core region responsible for path integration was either stellate cells in layer II or pyramidal cells in layer III of the MEC.

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“…The literature on vertebrate navigation suggests that even animals which do seem to build complex spatial maps of their environments for navigation using special neural structures (O'Keefe and Burgess, 1996;Hafting et al, 2005;Savelli and Knierim, 2019) still rely on the coordination of multiple navigation systems (Moser et al, 2008)-perhaps one solving the problem of local navigation with landmarks, and the other providing directional bearing (Jacobs and Menzel, 2014). It has even been suggested that primates navigate available affordances and choose between them in virtue of neurally implemented competing feedback controllers (Cisek, 2007;Pezzulo and Cisek, 2016).…”

Section: Introductionmentioning

confidence: 99%

Examples of Gibsonian Affordances in Legged Robotics Research Using an Empirical, Generative Framework

2020

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Evidence from empirical literature suggests that explainable complex behaviors can be built from structured compositions of explainable component behaviors with known properties. Such component behaviors can be built to directly perceive and exploit affordances. Using six examples of recent research in legged robot locomotion, we suggest that robots can be programmed to effectively exploit affordances without developing explicit internal models of them. We use a generative framework to discuss the examples, because it helps us to separate-and thus clarify the relationship between-description of affordance exploitation from description of the internal representations used by the robot in that exploitation. Under this framework, details of the architecture and environment are related to the emergent behavior of the system via a generative explanation. For example, the specific method of information processing a robot uses might be related to the affordance the robot is designed to exploit via a formal analysis of its control policy. By considering the mutuality of the agent-environment system during robot behavior design, roboticists can thus develop robust architectures which implicitly exploit affordances. The manner of this exploitation is made explicit by a well constructed generative explanation.

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Origin and role of path integration in the cognitive representations of the hippocampus: computational insights into open questions

Cited by 73 publications

References 187 publications

Path integration: how details of the honeybee waggle dance and the foraging strategies of desert ants might help in understanding its mechanisms

Path integration: how details of the honeybee waggle dance and the foraging strategies of desert ants might help in understanding its mechanisms

Identifying Core Regions for Path Integration on Medial Entorhinal Cortex of Hippocampal Formation

Examples of Gibsonian Affordances in Legged Robotics Research Using an Empirical, Generative Framework

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