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  The hippocampus as an unpredicted map: Hippocampal traces of uncertainties induced by changes in reward distributions

Tessereau, C., Mellor, J., Dayan, P., Xuan, F., & Dombeck, D. (2024). The hippocampus as an unpredicted map: Hippocampal traces of uncertainties induced by changes in reward distributions. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2024), Lisboa, Portugal.

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Tessereau, C1, Author                 
Mellor, J, Author
Dayan, P1, Author                 
Xuan, F, Author
Dombeck, D, Author
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1Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3017468              

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 Abstract: In volatile environments, humans and animals face different forms of uncertainty to which they must adapt to thrive. However, our understanding of the neural basis of this adaptation is incomplete, despite, for instance, long-standing arguments about its possible dependence on neuromodulation. Here, we take advantage of the well-known spatial remapping of hippocampal place cells in the face of environmental change to interrogate these processes. We performed calcium imaging in CA1 place cells in the Uncertain Reward virtual reality Task (UR- Task) [1]. In this, mice run along a linear track and lick for a water reward whose precise location on any run may be more or less certain in a block (a form of expected uncertainty), and which might also translate without warning (unexpected uncertainty). From limpid changes in reward locations, the place map undergoes remapping that is zone-dependent, affecting most the cells between reward zones, and visible as early as within the first trial after the switch. Given inherently variable reward locations, the place map within the reward zone fractionates, with some cells coding distance from reward, other cells firing reliably in relationship to space, and a third group being co-modulated, with reward and space-related fields. Single cell analysis highlights that, while some cells are uniquely modulated by reward or position, there is a rich interplay between position and reward contributions to the place code. We design generalized linear mixed models that can quantify the unique contribution of position and reward (both predictive and responsive) on each individual cell activity and help cluster the place map. We find more co-modulation between reward and position of the cells under expected than unexpected uncertainty, in which cells seem to cluster as only reward- or position-based.

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 Dates: 2024-03
 Publication Status: Published online
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Title: Computational and Systems Neuroscience Meeting (COSYNE 2024)
Place of Event: Lisboa, Portugal
Start-/End Date: 2024-02-29 - 2024-03-05

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Title: Computational and Systems Neuroscience Meeting (COSYNE 2024)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 1-114 Start / End Page: 110 Identifier: -