In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 10 ( 2022-10-31), p. e1010320-
Abstract:
In general, strategies for spatial navigation could employ one of two spatial reference frames: egocentric or allocentric. Notwithstanding intuitive explanations, it remains unclear however under what circumstances one strategy is chosen over another, and how neural representations should be related to the chosen strategy. Here, we first use a deep reinforcement learning model to investigate whether a particular type of navigation strategy arises spontaneously during spatial learning without imposing a bias onto the model. We then examine the spatial representations that emerge in the network to support navigation. To this end, we study two tasks that are ethologically valid for mammals—guidance, where the agent has to navigate to a goal location fixed in allocentric space, and aiming, where the agent navigates to a visible cue. We find that when both navigation strategies are available to the agent, the solutions it develops for guidance and aiming are heavily biased towards the allocentric or the egocentric strategy, respectively, as one might expect. Nevertheless, the agent can learn both tasks using either type of strategy. Furthermore, we find that place-cell-like allocentric representations emerge preferentially in guidance when using an allocentric strategy, whereas egocentric vector representations emerge when using an egocentric strategy in aiming. We thus find that alongside the type of navigational strategy, the nature of the task plays a pivotal role in the type of spatial representations that emerge.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010320
DOI:
10.1371/journal.pcbi.1010320.g001
DOI:
10.1371/journal.pcbi.1010320.g002
DOI:
10.1371/journal.pcbi.1010320.g003
DOI:
10.1371/journal.pcbi.1010320.g004
DOI:
10.1371/journal.pcbi.1010320.g005
DOI:
10.1371/journal.pcbi.1010320.g006
DOI:
10.1371/journal.pcbi.1010320.g007
DOI:
10.1371/journal.pcbi.1010320.g008
DOI:
10.1371/journal.pcbi.1010320.s001
DOI:
10.1371/journal.pcbi.1010320.s002
DOI:
10.1371/journal.pcbi.1010320.s003
DOI:
10.1371/journal.pcbi.1010320.s004
DOI:
10.1371/journal.pcbi.1010320.r001
DOI:
10.1371/journal.pcbi.1010320.r002
DOI:
10.1371/journal.pcbi.1010320.r003
DOI:
10.1371/journal.pcbi.1010320.r004
DOI:
10.1371/journal.pcbi.1010320.r005
DOI:
10.1371/journal.pcbi.1010320.r006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6