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NOBEL PRIZE 2026

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NIHNATIONAL INSTITUTE OF MENTAL HEALTH

Dissecting the circuits and population dynamics that represent unconditioned and conditioned stimuli in the ventral hippocampus

Active
Institution
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO, CA
Principal investigator
Mazen A Kheirbek
Funding
$781K
Duration
2025–2029
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Abstract

Project Summary Neural population dynamics are in constant flux, with patterns of activity across neurons being updated to account for changes in the relationships between important stimuli in the environment. Recent work from our group has shown that ensembles of CA1 neurons can represent the associations between stimuli and their outcomes, multiplexing stimulus and outcome representations into a format that is stored for days. However, the circuit mechanisms by which the ventral hippocampus performs these computations remains unknown. In this proposal we will leverage anatomical targeting of discrete cell classes, large-scale population recording, computational tools, and cell-type specific manipulation to understand how classes of ventral CA1 neuron acquire, update and store information during associative learning. In Aim 1 we will understand how vCA1 neurons that project to the nucleus accumbens, amygdala and/or mPFC acquire responses to conditioned and unconditioned cues through learning. In Aim 2 we will determine how these classes of neurons flexibly change their responses with switches in stimulus or outcome valences. In Aim 3 we will ask if information related to conditioned and unconditioned stimuli converge in vCA1 and how this process facilitates acquisition and storage of associative memories. Understanding these basic principles of vCA1 encoding properties will be crucial to efforts to understand the circuit mechanisms that contribute cognitive and emotional disorders with underlying dysfunction in associative learning processes.

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NATIONAL INSTITUTE OF MENTAL HEALTHR01Learning, Memory and Decision Neuroscience Study Section[LMDN]stimuluspopulation dynamicsassociativestimuliconditionedcircuit mechanismslearning aimventral

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