Navigating with grid and place cells in cluttered environments

Edvardsen, Vegard; Bičanski, Andrej; Burgess, Neil

doi:10.1002/hipo.23147

Cited by 59 publications

(48 citation statements)

References 73 publications

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“…On the one hand, rodents can keep track of where they are within an allocentric (environment-centered) reference frame (Morris, 1981). This sense of position is thought to be integrated into an internal topological map connecting places within the environment, which allows animals to compute subgoal locations whenever a new multi-step route toward a goal is required (Edvardsen et al, 2020;Spiers and Gilbert, 2015;Stachenfeld et al, 2017;Tolman, 1948). This kind of cognitive-map-based reasoning is flexible, is learned by observing the structure of the environment, and depends on the hippocampus (O' Keefe and Nadel, 1978).…”

Section: Introductionmentioning

confidence: 99%

Mice learn multi-step routes by memorizing subgoal locations

Shamash¹,

Olesen²,

Ιορδανίδου³

et al. 2020

Preprint

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Animals must rapidly gather spatial information about new environments so that they can quickly reach food or safety even when direct paths are unavailable. The behavioral strategies used to implement multi-step routes to goals in naturalistic settings are unknown. Here we show that mice spontaneously learn a subgoal memory strategy while escaping to shelter or seeking food in an obstructed environment. We first investigated how mice navigate to shelter in response to threats when the direct path is blocked by a wall. Initially, mice ran straight toward the shelter and circumvented the obstacle using sensory cues. Over the course of 20 minutes, however, they switched to a spatial memory strategy to execute spatially efficient paths. Efficient escape routes were not learned by reinforcing egocentric actions or by constructing an unbiased internal map during exploration. Instead, mice used a hybrid strategy: they memorized specific subgoal locations encountered during previous running movements toward the obstacle. We then found that the same behavioral strategy is also used in a reward-seeking task. These results show that spontaneous memorization of local subgoals is a fundamental strategy by which rodents execute efficient multi-step routes to goals in novel environments.

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Section: Introductionmentioning

confidence: 99%

Mice learn multi-step routes by memorizing subgoal locations

Shamash¹,

Olesen²,

Ιορδανίδου³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The region-based approach entails two planning levels where the subdivision of the urban environment is used to depict a coarse initial plan that guides more granular intra-regional decisions [22,32]. The barrier-based approach contemplates 'course-adjustments' [120] by encouraging navigation along and through rivers and parks, whilst avoiding walking along major roads and railways.…”

Section: Resultsmentioning

confidence: 99%

Perception of urban subdivisions in pedestrian movement simulation

2020

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The perception of urban subdivisions, deriving from regionalisation processes and the identification of separating elements (barriers), has proven to dynamically shape peoples’ cognitive representations of space and route choice behaviour in cities. However, existing Agent-Based Models (ABMs) for pedestrian simulation have not accounted for these particular cognitive mapping processes. The aim of this paper is to explore the behaviour of pedestrian agents endowed with knowledge about urban subdivisions. Drawing from literature in spatial cognition, we adapted a region-based route choice model, which contemplates a high- and a local planning level, and advanced a barrier-based route choice model, wherein the influence of separating elements is manipulated. Finally, we combined these two approaches in a region-barrier based model. The patterns emerging from the movement of agents employing such approaches were examined in the city centres of London and Paris. The introduction of regions in the routing mechanisms reduced the unbalanced concentration of agents across the street network brought up by the widely employed least cumulative angular change model (-.08 Gini coefficient). The inclusion of barriers further raised the dispersal of the agents through secondary roads, while leading agents to walk along waterfronts and across parks; it also yielded a more regular usage of pedestrian roads. Moreover, the region- and the region-barrier based routes showed deviation ratio values from the road distance shortest path (region-based: 1.18 London, 1.16 Paris, region-barrier based: 1.43 London, 1.33 Paris) consistent with empirical observations from pedestrian behaviour research. A further evaluation of the model with macro-level observational data may enhance the understanding of pedestrian dynamics and help tuning the interplay amongst urban salient elements at the agent level. Yet, we consider the movement flows arising from our current implementation insightful for assessing the distribution of pedestrians and testing possible interventions for the design of legible and walkable spaces.

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“…This model relates to other models of rodents (Bicanski & Burgess, 2018;Byrne, Becker, & Burgess, 2007;Edvardsen, Bicanski, & Burgess, 2020) and monkeys (Rolls, 2020) that address how the allocentric representation of the environment depends upon a transformation from egocentric viewpoints of barriers. The egocentric coding of barriers has been shown in recent experimental data in neurons in a range of related structures including dorsomedial striatum (Hinman, Chapman, & Hasselmo, 2019), postrhinal cortex (Gofman et al, 2019;LaChance, Todd, & Taube, 2019), and retrosplenial cortex (Alexander et al, 2020).…”

Section: Data Support Role In Planning Of Spatial Navigationmentioning

confidence: 99%