Grid-like Neural Representations Support Olfactory Navigation of a Two-Dimensional Odor Space

Bao, Xiaojun; Gjorgieva, Eva; Shanahan, Laura K.; Howard, James D.; Kahnt, Thorsten; Gottfried, Jay A.

doi:10.1016/j.neuron.2019.03.034

Cited by 151 publications

(177 citation statements)

References 54 publications

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“…While past studies have identified neural signals in the HC and EC indicative of a cognitive map primarily using continuous task dimensions with online sensory feedback during task performance (e.g. visual, auditory, vestibular) (Aronov, Nevers, & Tank, 2017;Bao et al, 2019;Constantinescu et al, 2016;Doeller, Barry, & Burgess, 2010;Howard Eichenbaum & Cohen, 2014;Hafting et al, 2005;Nau, Navarro Schröder, Bellmund, & Doeller, 2018;O'Keefe & Nadel, 1978;Theves, Fernandez, & Doeller, 2019), many important everyday decisions involve discrete entities that vary along multiple abstract dimensions that are sampled piecemeal, one experience at a time, in the absence of continuous sensory feedback, such as with whom to collaborate or where to eat. How the brain constructs a cognitive map of abstract relationships between discrete entities from piecemeal experiences is unclear.…”

Section: Introductionmentioning

confidence: 99%

Map making: Constructing, combining, and inferring on abstract cognitive maps

Park

Miller

Nili

et al. 2019

Preprint

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Cognitive maps are thought to enable model-based inferences from limited experience that can guide novel decisions-a hallmark of goal-directed behavior. We tested whether the hippocampus (HC), entorhinal cortex (EC), and ventromedial prefrontal cortex (vmPFC)/medial orbitofrontal cortex (mOFC) organize abstract and discrete relational information into a cognitive map to guide novel choices. Subjects learned the status of people in two separate unseen 2-D social hierarchies defined by competence and popularity piecemeal from binary comparisons. Although only one dimension was ever behaviorally relevant, multivariate activity patterns in HC, EC and mOFC were linearly related to the Euclidian distance between people in the mentally reconstructed 2-D space. Hubs created unique comparisons between the two hierarchies, enabling inferences between novel pairs of people. We found that both behavior and neural activity in EC and vmPFC/mOFC reflected the Euclidian distance to the retrieved hub, which was reinstated in HC. These findings reveal how abstract and discrete relational structures are represented, combined, and navigated in the human brain.Kriete, T., Noelle, D. C., Cohen, J. D., & O'Reilly, R. C. (2013). Indirection and symbol-like processing in the prefrontal cortex and basal ganglia.

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Section: Introductionmentioning

confidence: 99%

Map making: Constructing, combining, and inferring on abstract cognitive maps

Park

Miller

Nili

et al. 2019

Preprint

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“…This activity pattern resembles the patterns of transition matrix eigenvectors of hexagonal graphs 33,34 . Further, grid-like representations emerge in enviroments/tasks that are not spatial but share similar statistical structure [35][36][37] . These observations may suggest that grid cells can be used as basis functions for all environments in which the associations between the states are governed by the rules of 2D Euclidean space.…”

Section: Discussionmentioning

confidence: 99%

“…We further assumed, for simplicity, that the two dimensions of the hexagonal grid are equal and the number of nodes in a community is also equal, hence θ defines a vector of possible number of nodes in a graph. For hexagonal graphs we considred N = [25,36,49], for a graph with underlying community structure N = [28,35,42] with equal prior probability. We emphasis that the basis set ( ) for each transition structure within a structural form is a scaled or trancated version of a general basis set for that structural form.…”

Section: Inferring Graph Sizementioning

confidence: 99%

Transferring structural knowledge across cognitive maps in humans and models

Mark

Moran²,

Parr

et al. 2019

Preprint

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Relations between task elements often follow hidden underlying structural forms such as periodicities or hierarchies, whose inferences fosters performance. However, transferring structural knowledge to novel environments requires flexible representations that are generalizable over particularities of the current environment, such as its stimuli and size. We suggest that humans represent structural forms as abstract basis sets and that in novel tasks, the structural form is inferred and the relevant basis set is transferred. Using a computational model, we show that such representation allows inference of the underlying structural form, important task states, effective behavioural policies and the existence of unobserved state-trajectories. In two experiments, participants learned three abstract graphs during two successive days. We tested how structural knowledge acquired on Day-1 affected Day-2 performance. In line with our model, participants who had a correct structural prior were able to infer the existence of unobserved state-trajectories and appropriate behavioural policies.

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“…Grid cells, typically recorded from freely navigating rats and humans (Hafting, Fyhn, Molden, Moser, & Moser, ; Sargolini et al, ; Jacobs et al, ), display a regularly spaced pattern of neural firing (“grids”) that span the environment. In addition to their presence during spatial navigation, studies have also observed such grid coding during tasks involving representation of conceptual categories, imagination, and scene processing (Bao et al, ; Bellmund, Deuker, Schroder, & Doeller, ; Constantinescu, O'reilly, & Behrens, ; Garvert, Dolan, & Behrens, ; Horner, Bisby, Zotow, Bush, & Burgess, ; Killian, Jutras, & Buffalo, ). One theoretical interpretation of such grid coding is that it forms the basis of a universal spatial metric representation that underlies many, if not all, forms of spatial and nonspatial representations (Behrens et al, ; Bellmund, Gardenfors, Moser, & Doeller, ; Bush, Barry, Manson, & Burgess, ; Hasselmo, Giocomo, Brandon, & Yoshida, ; Hawkins, Lewis, Klukas, Purdy, & Ahmad, ).…”

Section: Introductionmentioning

confidence: 99%

“…Grid cells, typically recorded from freely navigating rats and humans (Hafting, Fyhn, Molden, Moser, & Moser, 2005;Sargolini et al, 2006;Jacobs et al, 2013), display a regularly spaced pattern of neural firing ("grids") that span the environment. In addition to their presence during spatial navigation, studies have also observed such grid coding during tasks involving representation of conceptual categories, imagination, and scene processing (Bao et al, 2019;Bellmund, Deuker, Schroder, & Doeller, 2016;Constantinescu, O'reilly, & Behrens, 2016;Garvert, Dolan, & Behrens, 2017;Horner, Bisby, Zotow, Bush, & Burgess, 2016;Killian, Jutras, & Buffalo, 2012).…”

mentioning

confidence: 99%

Grid coding, spatial representation, and navigation: Should we assume an isomorphism?

2019

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Grid cells provide a compelling example of a link between cellular activity and an abstract and difficult to define concept like space. Accordingly, a representational perspective on grid coding argues that neural grid coding underlies a fundamentally spatial metric. Recently, some theoretical proposals have suggested extending such a framework to nonspatial cognition as well, such as category learning. Here, we provide a critique of the frequently employed assumption of an isomorphism between patterns of neural activity (e.g., grid cells), mental representation, and behavior (e.g., navigation). Specifically, we question the strict isomorphism between these three levels and suggest that human spatial navigation is perhaps best characterized by a wide variety of both metric and nonmetric strategies. We offer an alternative perspective on how grid coding might relate to human spatial navigation, arguing that grid coding is part of a much larger conglomeration of neural activity patterns that dynamically tune to accomplish specific behavioral outputs.

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Grid-like Neural Representations Support Olfactory Navigation of a Two-Dimensional Odor Space

Cited by 151 publications

References 54 publications

Map making: Constructing, combining, and inferring on abstract cognitive maps

Map making: Constructing, combining, and inferring on abstract cognitive maps

Transferring structural knowledge across cognitive maps in humans and models

Grid coding, spatial representation, and navigation: Should we assume an isomorphism?

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