Dataset Information

The honeycomb maze provides a novel test to study hippocampal-dependent spatial navigation.

ABSTRACT: Here we describe the honeycomb maze, a behavioural paradigm for the study of spatial navigation in rats. The maze consists of 37 platforms that can be raised or lowered independently. Place navigation requires an animal to go to a goal platform from any of several start platforms via a series of sequential choices. For each, the animal is confined to a raised platform and allowed to choose between two of the six adjacent platforms, the correct one being the platform with the smallest angle to the goal-heading direction. Rats learn rapidly and their choices are influenced by three factors: the angle between the two choice platforms, the distance from the goal, and the angle between the correct platform and the direction of the goal. Rats with hippocampal damage are impaired in learning and their performance is affected by all three factors. The honeycomb maze represents a marked improvement over current spatial navigation tests, such as the Morris water maze, because it controls the choices of the animal at each point in the maze, provides the ability to assess knowledge of the goal direction from any location, enables the identification of factors influencing task performance and provides the possibility for concomitant single-cell recording.

SUBMITTER: Wood RA

PROVIDER: S-EPMC6342259 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The honeycomb maze provides a novel test to study hippocampal-dependent spatial navigation.

Wood Ruth A RA Bauza Marius M Krupic Julija J Burton Stephen S Delekate Andrea A Chan Dennis D O'Keefe John J

Nature 20180124 7690

Here we describe the honeycomb maze, a behavioural paradigm for the study of spatial navigation in rats. The maze consists of 37 platforms that can be raised or lowered independently. Place navigation requires an animal to go to a goal platform from any of several start platforms via a series of sequential choices. For each, the animal is confined to a raised platform and allowed to choose between two of the six adjacent platforms, the correct one being the platform with the smallest angle to th ...[more]

PMID: 29364869

Similar Datasets

Project description:Humans and non-human animals show great flexibility in spatial navigation, including the ability to return to specific locations based on as few as one single experience. To study spatial navigation in the laboratory, watermaze tasks, in which rats have to find a hidden platform in a pool of cloudy water surrounded by spatial cues, have long been used. Analogous tasks have been developed for human participants using virtual environments. Spatial learning in the watermaze is facilitated by the hippocampus. In particular, rapid, one-trial, allocentric place learning, as measured in the delayed-matching-to-place variant of the watermaze task, which requires rodents to learn repeatedly new locations in a familiar environment, is hippocampal dependent. In this article, we review some computational principles, embedded within a reinforcement learning framework, that utilise hippocampal spatial representations for navigation in watermaze tasks. We consider which key elements underlie their efficacy, and discuss their limitations in accounting for hippocampus-dependent navigation, both in terms of behavioural performance (i.e. how well do they reproduce behavioural measures of rapid place learning) and neurobiological realism (i.e. how well do they map to neurobiological substrates involved in rapid place learning). We discuss how an actor-critic architecture, enabling simultaneous assessment of the value of the current location and of the optimal direction to follow, can reproduce one-trial place learning performance as shown on watermaze and virtual delayed-matching-to-place tasks by rats and humans, respectively, if complemented with map-like place representations. The contribution of actor-critic mechanisms to delayed-matching-to-place performance is consistent with neurobiological findings implicating the striatum and hippocampo-striatal interaction in delayed-matching-to-place performance, given that the striatum has been associated with actor-critic mechanisms. Moreover, we illustrate that hierarchical computations embedded within an actor-critic architecture may help to account for aspects of flexible spatial navigation. The hierarchical reinforcement learning approach separates trajectory control via a temporal-difference error from goal selection via a goal prediction error and may account for flexible, trial-specific, navigation to familiar goal locations, as required in some arm-maze place memory tasks, although it does not capture one-trial learning of new goal locations, as observed in open field, including watermaze and virtual, delayed-matching-to-place tasks. Future models of one-shot learning of new goal locations, as observed on delayed-matching-to-place tasks, should incorporate hippocampal plasticity mechanisms that integrate new goal information with allocentric place representation, as such mechanisms are supported by substantial empirical evidence.

Project description:Prior knowledge structures (or schemas) confer multiple behavioral benefits. First, when we encounter information that fits with prior knowledge structures, this information is generally better learned and remembered. Second, prior knowledge can support prospective planning. In humans, memory enhancements related to prior knowledge have been suggested to be supported, in part, by computations in prefrontal and medial temporal lobe (MTL) cortex. Moreover, animal studies further implicate a role for the hippocampus in schema-based facilitation and in the emergence of prospective planning signals following new learning. To date, convergence across the schema-enhanced learning and memory literature may be constrained by the predominant use of hippocampally dependent spatial navigation paradigms in rodents, and non-spatial list-based learning paradigms in humans. Here, we targeted this missing link by examining the effects of prior knowledge on human navigational learning in a hippocampally dependent virtual navigation paradigm that closely relates to foundational studies in rodents. Outside the scanner, participants overlearned Old Paired Associates (OPA- item-location associations) in multiple spatial environments, and they subsequently learned New Paired Associates (NPA-new item-location associations) in the environments while undergoing fMRI. We hypothesized that greater OPA knowledge precision would positively affect NPA learning, and that the hippocampus would be instrumental in translating this new learning into prospective planning of navigational paths to NPA locations. Behavioral results revealed that OPA knowledge predicted one-shot learning of NPA locations, and neural results indicated that one-shot learning was predicted by the rapid emergence of performance-predictive prospective planning signals in hippocampus. Prospective memory relationships were not significant in parahippocampal cortex and were marginally dissociable from the primary hippocampal effect. Collectively, these results extend understanding of how schemas impact learning and performance, showing that the precision of prior spatial knowledge is important for future learning in humans, and that the hippocampus is involved in translating this knowledge into new goal-directed behaviors.

Dataset Information

The honeycomb maze provides a novel test to study hippocampal-dependent spatial navigation.

Publications

The honeycomb maze provides a novel test to study hippocampal-dependent spatial navigation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets