The Hippocampus Converts Volatile Entorhinal Inputs into Stable Spatial Maps

Cholvin, Thibault; Hainmueller, Thomas; Bartos, Marlene

doi:10.2139/ssrn.3778933

Cited by 1 publication

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“…Inputs from ECIII were also random, sparse, and strong in the same sense, and EC cell activity was assumed to be spatially untuned. Although many entorhinal cells exhibit spatial tuning, EC inputs to CA1 pyramidal cells have been found to be largely weakly spatially tuned 25,26 . Inactivation of Schaeffer collateral inputs via optogenetic stimulation largely eliminated place fields in CA1, suggesting a majority contribution of spatial tuning from CA3 27 , although see 28 .…”

Section: A Spiking Network Model Reproduces Drift Statistics In Ca1mentioning

confidence: 99%

Network mechanisms underlying representational drift in area CA1 of hippocampus

Devalle

Roxin

2022

Preprint

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Chronic imaging experiments in mice have revealed that the hippocampal code drifts over long time scales. Specifically, the subset of cells which are active on any given session in a familiar environment changes over the course of days and weeks. While some cells transition into or out of the code after a few sessions, others are stable over the entire experiment. Similar representational drift has also been observed in other cortical areas, raising the possibility of a common underlying mechanism, which, however, remains unknown. Here we show, through quantitative fitting of a network model to experimental data, that the statistics of representational drift in CA1 pyramidal cells are consistent with ongoing synaptic turnover in the main excitatory inputs to a neuronal circuit operating in the balanced regime. We find two distinct time-scales of drift: a fast shift in overall excitability with characteristic time-scale of two days, and a slower drift in spatially modulated input on the order of about one month. The observed heterogeneity in single-cell properties, including long-term stability, are explained by variability arising from random changes in the number of active inputs to cells from one session to the next. We furthermore show that these changes are, in turn, consistent with an ongoing process of learning via a Hebbian plasticity rule. We conclude that representational drift is the hallmark of a memory system which continually encodes new information.

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