Mice in a labyrinth show rapid learning, sudden insight, and efficient exploration

Rosenberg, Matthew; Zhang, Tony; Perona, Pietro; Meister, Markus

doi:10.7554/elife.66175

Cited by 79 publications

(126 citation statements)

References 42 publications

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“… (A) Three tasks and their corresponding graph representations: i) Gridworld of Fig 2 with 3 goal nodes (home, water, and food). ii) A binary tree labyrinth used in mouse navigation experiments [12], with 2 goals (home and water). iii) Tow er of Hanoi game, with 2 goals (the configurations of disks that solve the game).…”

Section: Performance Of the Endotaxis Algorithmmentioning

confidence: 99%

“…We performed a similar simulation for a complex labyrinth used in a recent study of mouse navigation [12]. The topology of the maze was a binary tree with a single entrance, 63 T-junctions, and 64 end nodes (Fig 3A ii).…”

Section: Performance Of the Endotaxis Algorithmmentioning

confidence: 99%

“…A single source of water was located at one of the end nodes. In these experiments mice learned the shortest path to the water source after visiting it ~10 times; they also performed error-free paths back to entrance on the first attempt [12]. Again the simulated agent explored the labyrinth with a random walk.…”

Section: Performance Of the Endotaxis Algorithmmentioning

confidence: 99%

“…In some ways the simulations perform better than real animals. For example in the binary maze the agent can navigate to a reward location flawlessly after discovering it the first time (Fig 3B), whereas real mice solve that problem after ~10 experiences [12]. This inspires confidence that as one adds realistic “bells and whistles” to the model the additional degrees of freedom will not break its operation.…”

Section: Supplementmentioning

confidence: 99%

“…In these experiments mice learned the shortest path to the water source after visiting it ∼10 times; they also performed . ii) A binary tree labyrinth used in mouse navigation experiments [12], with 2 goals (home and water). iii) Tower of Hanoi game, with 2 goals (the configurations of disks that solve the game).…”

Section: Simultaneous Acquisition Of Map and Targets During Explorationmentioning

confidence: 99%

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Endotaxis: A neuromorphic algorithm for mapping, goal-learning, navigation, and patrolling

Zhang

Rosenberg

Perona

et al. 2021

Preprint

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An animal entering a new environment typically faces three challenges: explore the space for resources, memorize their locations, and navigate towards those targets as needed. Experimental work on exploration, mapping, and navigation has mostly focused on simple environments - such as an open arena, a pond [1], or a desert [2] - and much has been learned about neural signals in diverse brain areas under these conditions [3,4]. However, many natural environments are highly constrained, such as a system of burrows, or of paths through the underbrush. More generally, many cognitive tasks are equally constrained, allowing only a small set of actions at any given stage in the process. Here we propose an algorithm that learns the structure of an arbitrary environment, discovers useful targets during exploration, and navigates back to those targets by the shortest path. It makes use of a behavioral module common to all motile animals, namely the ability to follow an odor to its source [5]. We show how the brain can learn to generate internal "virtual odors'" that guide the animal to any location of interest. This endotaxis algorithm can be implemented with a simple 3-layer neural circuit using only biologically realistic structures and learning rules. Several neural components of this scheme are found in brains from insects to humans. Nature may have evolved a general mechanism for search and navigation on the ancient backbone of chemotaxis.

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Section: Performance Of the Endotaxis Algorithmmentioning

confidence: 99%

Section: Performance Of the Endotaxis Algorithmmentioning

confidence: 99%

Section: Performance Of the Endotaxis Algorithmmentioning

confidence: 99%

Section: Supplementmentioning

confidence: 99%

Section: Simultaneous Acquisition Of Map and Targets During Explorationmentioning

confidence: 99%

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Endotaxis: A neuromorphic algorithm for mapping, goal-learning, navigation, and patrolling

Zhang

Rosenberg

Perona

et al. 2021

Preprint

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Computational cross‐species views of the hippocampal formation

2023

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The discovery of place cells and head direction cells in the hippocampal formation of freely foraging rodents has led to an emphasis of its role in encoding allocentric spatial relationships. In contrast, studies in head-fixed primates have additionally found representations of spatial views. We review recent experiments in freely moving monkeys that expand upon these findings and show that postural variables such as eye/head movements strongly influence neural activity in the hippocampal formation, suggesting that the function of the hippocampus depends on where the animal looks. We interpret these results in the light of recent studies in humans performing challenging navigation tasks which suggest that depending on the context, eye/head movements serve one of two roles-gathering information about the structure of the environment (active sensing) or externalizing the contents of internal beliefs/ deliberation (embodied cognition). These findings prompt future experimental investigations into the information carried by signals flowing between the hippocampal formation and the brain regions controlling postural variables, and constitute a basis for updating computational theories of the hippocampal system to accommodate the influence of eye/head movements.

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Home Run: Finding Your Way Home by Imagining Trajectories

Daria¹,

Mazzaglia²,

Verbelen³

et al. 2023

Communications in Computer and Information Science

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When studying unconstrained behavior and allowing mice to leave their cage to navigate a complex labyrinth, the mice exhibit foraging behavior in the labyrinth searching for rewards, returning to their home cage now and then, e.g. to drink. Surprisingly, when executing such a "home run", the mice do not follow the exact reverse path, in fact, the entry path and home path have very little overlap. Recent work proposed a hierarchical active inference model for navigation, where the low level model makes inferences about hidden states and poses that explain sensory inputs, whereas the high level model makes inferences about moving between locations, effectively building a map of the environment. However, using this "map" for planning, only allows the agent to find trajectories that it previously explored, far from the observed mice's behaviour. In this paper, we explore ways of incorporating before-unvisited paths in the planning algorithm, by using the low level generative model to imagine potential, yet undiscovered paths. We demonstrate a proof of concept in a grid-world environment, showing how an agent can accurately predict a new, shorter path in the map leading to its starting point, using a generative model learnt from pixel-based observations.

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Mice in a labyrinth show rapid learning, sudden insight, and efficient exploration

Cited by 79 publications

References 42 publications

Endotaxis: A neuromorphic algorithm for mapping, goal-learning, navigation, and patrolling

Endotaxis: A neuromorphic algorithm for mapping, goal-learning, navigation, and patrolling

Computational cross‐species views of the hippocampal formation

Home Run: Finding Your Way Home by Imagining Trajectories

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