Ensemble remodeling supports memory-updating

Mau, William; Morales-Rodriguez, Denisse; Dong, Zhe; Pennington, Zachary T.; Francisco, Taylor R; Baxter, Mark G.; Shuman, Tristan; Cai, Denise J.

doi:10.1101/2022.06.02.494530

Cited by 9 publications

(7 citation statements)

References 114 publications

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“…While both groups showed reinstatement of the Turn Right 1 representation during Turn Right 2, reinstatement was stronger in the Two-Maze group. We additionally showed that reorganization of neural activity affects the frame-by-frame coordination of activity among neural ensembles in part independently of remapping observed in session-averaged spatial event rate maps, similar to previous findings indicating this is a crucial, though less-studied, dimension of hippocampal activity (El-Gaby et al, 2021; Mau et al, 2022).…”

Section: Discussionsupporting

confidence: 89%

Hippocampal remapping induced by new behavior is mediated by spatial context

Levy

Hasselmo

2023

Preprint

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The hippocampus plays a central role in episodic memory and spatial navigation. Hippocampal neurons form unique representational codes in different spatial environments, which may provide a neural substrate for context that can trigger memory recall or enable performance of context-guided memory tasks. However, new learning often occurs in a familiar location, requiring that location's representation to be updated without erasing the previously existing memory representations that may be adaptive again in the future. To study how new learning affects a previously acquired spatial memory representation, we trained mice to perform two plus maze tasks across nine days in the sequence Turn Right 1 - Go East - Turn Right 2 (three days each), while we used single-photon calcium imaging to record the activity of hundreds of neurons in dorsal CA1. One cohort of mice performed the entire experiment on the same maze (One-Maze), while the second cohort performed the Go East task on a unique maze (Two-Maze). We hypothesized that CA1 representations in One-Maze mice would exhibit more change in the spatial patterns of neuronal activity on the maze from Turn Right 1 to Turn Right 2 than would be seen in Two-Maze mice. Indeed, changes in single unit activity and in the population code were larger in the One-Maze group. We further show evidence that Two-Maze mice utilize a separate neural representation for each maze environment. Finally, we found that remapping across the two Turn Right epochs did not involve an erasure of the representation for the first Turn Right experience, as many neurons in mice from both groups maintained Turn Right-associated patterns of activity even after performing the Go East rule. These results demonstrate that hippocampal activity patterns remap in response to new learning, that remapping is greater when experiences occur in the same spatial context, and that throughout remapping information from each experience is preserved.

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Section: Discussionsupporting

confidence: 89%

Hippocampal remapping induced by new behavior is mediated by spatial context

Levy

Hasselmo

2023

Preprint

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“…Moreover, excitability is known to fluctuate over timescales from hours to days, in the amygdala (16), the hippocampus (15; 17) or the cortex (18; 19). Subsequent reactivations of a neural ensemble at different time points could therefore be biased by excitability (20), which varies at similar timescales than drift (21). Altogether, this evidence suggest that fluctuations of excitability could act as a cellular mechanism for drift (12).…”

Section: Introductionmentioning

confidence: 83%

Drift of neural ensembles driven by slow fluctuations of intrinsic excitability

Delamare

Zaki

Cai

et al. 2023

Preprint

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Representational drift refers to the dynamic nature of neural representations in the brain despite the behavior being seemingly stable. Although drift has been observed in many different brain regions, the mechanisms underlying it are not known. Since intrinsic neural excitability is suggested to play a key role in regulating memory allocation, fluctuations of excitability could bias the reactivation of previously stored memory ensembles and therefore act as a motor for drift. Here, we propose a rate-based plastic recurrent neural network with slow fluctuations of intrinsic excitability. We first show that subsequent reactivations of a neural ensemble can lead to drift of this ensemble. The model predicts that drift is induced by co-activation of previously active neurons along with neurons with high excitability which leads to remodelling of the recurrent weights. Consistent with previous experimental works, the drifting ensemble is informative about its temporal history. Crucially, we show that the gradual nature of the drift is necessary for decoding temporal information from the activity of the ensemble. Finally, we show that the memory is preserved and can be decoded by an output neuron having plastic synapses with the main region.

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“…Methods development in animal models increasingly allows the individuation and manipulation of specific circuits and networks. For example, mini-scope calcium imaging in rodents can reveal activation and changes in neuronal ensembles resulting from experimental manipulations (Mau et al, 2022). Optogenetic methods enable the targeting of neuronal subpopulations, and methods for genetically sequencing and classifying neurons are developing at a staggering pace (e.g., Krienen et al 2020;BICNN, 2021).…”

Section: Refining Methodsmentioning

confidence: 99%