Sublayer-Specific Coding Dynamics during Spatial Navigation and Learning in Hippocampal Area CA1.

Autor:	Danielson NB; Department of Neuroscience, Columbia University, New York, NY 10032, USA; Doctoral Program in Neurobiology and Behavior, Columbia University, New York, NY 10032, USA. Electronic address: nbd2111@cumc.columbia.edu., Zaremba JD; Department of Neuroscience, Columbia University, New York, NY 10032, USA; Doctoral Program in Neurobiology and Behavior, Columbia University, New York, NY 10032, USA., Kaifosh P; Department of Neuroscience, Columbia University, New York, NY 10032, USA; Doctoral Program in Neurobiology and Behavior, Columbia University, New York, NY 10032, USA., Bowler J; Department of Neuroscience, Columbia University, New York, NY 10032, USA; Doctoral Program in Neurobiology and Behavior, Columbia University, New York, NY 10032, USA., Ladow M; Department of Neuroscience, Columbia University, New York, NY 10032, USA., Losonczy A; Department of Neuroscience, Columbia University, New York, NY 10032, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10032, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10032, USA. Electronic address: al2856@cumc.columbia.edu.
Jazyk:	angličtina
Zdroj:	Neuron [Neuron] 2016 Aug 03; Vol. 91 (3), pp. 652-65. Date of Electronic Publication: 2016 Jul 07.
DOI:	10.1016/j.neuron.2016.06.020
Abstrakt:	The mammalian hippocampus is critical for spatial information processing and episodic memory. Its primary output cells, CA1 pyramidal cells (CA1 PCs), vary in genetics, morphology, connectivity, and electrophysiological properties. It is therefore possible that distinct CA1 PC subpopulations encode different features of the environment and differentially contribute to learning. To test this hypothesis, we optically monitored activity in deep and superficial CA1 PCs segregated along the radial axis of the mouse hippocampus and assessed the relationship between sublayer dynamics and learning. Superficial place maps were more stable than deep during head-fixed exploration. Deep maps, however, were preferentially stabilized during goal-oriented learning, and representation of the reward zone by deep cells predicted task performance. These findings demonstrate that superficial CA1 PCs provide a more stable map of an environment, while their counterparts in the deep sublayer provide a more flexible representation that is shaped by learning about salient features in the environment. VIDEO ABSTRACT. (Copyright © 2016 Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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