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Spatial representation by ramping activity of neurons in the retrohippocampal cortex.

Authors :
Tennant SA
Clark H
Hawes I
Tam WK
Hua J
Yang W
Gerlei KZ
Wood ER
Nolan MF
Source :
Current biology : CB [Curr Biol] 2022 Oct 24; Vol. 32 (20), pp. 4451-4464.e7. Date of Electronic Publication: 2022 Sep 12.
Publication Year :
2022

Abstract

Neurons in the retrohippocampal cortices play crucial roles in spatial memory. Many retrohippocampal neurons have firing fields that are selectively active at specific locations, with memory for rewarded locations associated with reorganization of these firing fields. Whether this is the sole strategy for representing spatial memories is unclear. Here, we demonstrate that during a spatial memory task retrohippocampal neurons encode location through ramping activity that extends across segments of a linear track approaching and following a reward, with the rewarded location represented by offsets or switches in the slope of the ramping activity. Ramping representations could be maintained independently of trial outcome and cues marking the reward location, indicating that they result from recall of the track structure. When recorded in an open arena, neurons that generated ramping activity during the spatial memory task were more numerous than grid or border cells, with a majority showing spatial firing that did not meet criteria for classification as grid or border representations. Encoding of rewarded locations through offsets and switches in the slope of ramping activity also emerged in recurrent neural network models trained to solve a similar spatial memory task. Impaired performance of model networks following disruption of outputs from ramping neurons is consistent with this coding strategy supporting navigation to recalled locations of behavioral significance. Our results suggest that encoding of learned spaces by retrohippocampal networks employs both discrete firing fields and continuous ramping representations. We hypothesize that retrohippocampal ramping activity mediates readout of learned models for goal-directed navigation.<br />Competing Interests: Declaration of interests The authors declare no competing interests.<br /> (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Details

Language :
English
ISSN :
1879-0445
Volume :
32
Issue :
20
Database :
MEDLINE
Journal :
Current biology : CB
Publication Type :
Academic Journal
Accession number :
36099915
Full Text :
https://doi.org/10.1016/j.cub.2022.08.050