Transition between encoding and consolidation/replay dynamics via cholinergic modulation of CAN current: A modeling study

Saravanan, Varun; Arabali, Danial; Jochems, Arthur; Cui, Anja-Xiaoxing; Gootjes-Dreesbach, Luise; Cutsuridis, Vassilis; Yoshida, Masatoshi

doi:10.1002/hipo.22429

Cited by 14 publications

(13 citation statements)

References 103 publications

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“…5). This idea is also supported by a computational study on the contribution of persistent firing on time-cell activity (Hasselmo 2012;Hasselmo and Stern 2014;Howard et al 2014;Saravanan et al 2015). In this way, persistent firing is transmitted to CA1 pyramidal cells to bridge the temporal gap, and then to the amygdala via the EC layer V to coincide with the onset of the US (Fendt and Fanselow 1999) to generate a fear memory engram via Hebbian synaptic strengthening in the amygdala.…”

Section: Discussionmentioning

confidence: 85%

Entorhinal–hippocampal neuronal circuits bridge temporally discontiguous events

2015

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The entorhinal cortex (EC) -hippocampal (HPC) network plays an essential role for episodic memory, which preserves spatial and temporal information about the occurrence of past events. Although there has been significant progress toward understanding the neural circuits underlying the spatial dimension of episodic memory, the relevant circuits subserving the temporal dimension are just beginning to be understood. In this review, we examine the evidence concerning the role of the EC in associating events separated by time-or temporal associative learning-with emphasis on the function of persistent activity in the medial entorhinal cortex layer III (MECIII) and their direct inputs into the CA1 region of HPC. We also discuss the unique role of Island cells in the medial entorhinal cortex layer II (MECII), which is a newly discovered direct feedforward inhibitory circuit to CA1. Finally, we relate the function of these entorhinal cortical circuits to recent findings concerning hippocampal time cells, which may collectively activate in sequence to bridge temporal gaps between discontiguous events in an episode.

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Section: Discussionmentioning

confidence: 85%

Entorhinal–hippocampal neuronal circuits bridge temporally discontiguous events

2015

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“…One example is the ability of rodents to memorize action sequences from past experience, and replay them mentally. While we adopted in this paper an abstract level of modeling, the question of how a realistic hippocampal-like neural network can encode sequences and replay them, has been addressed in the literature, and is still an active research topic (Levy, 1996;Cutsuridis and Hasselmo, 2011;Saravanan et al, 2015;Jahnke et al, 2015). The reader is referred to (Bhalla, 2019;Rennó-Costa et al, 2019), for recent reviews.…”

Section: Discussionmentioning

confidence: 99%

Modeling awake hippocampal reactivations with model-based bidirectional search

Khamassi¹,

Girard²

2020

Biol Cybern

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Hippocampal offline reactivations during reward-based learning, usually categorized as replay events, have been found to be important for performance improvement over time and for memory consolidation. Recent computational work has linked these phenomena to the need to transform reward information into stateaction values for decision-making and to propagate it to all relevant states of the environment. Nevertheless, it is still unclear whether an integrated reinforcement learning mechanism could account for the variety of awake hippocampal reactivations, including variety in order (forward and reverse reactivated trajectories) and variety in the location where they occur (reward site or decision-point). Here we present a model-based bidirectional search model which accounts for a variety of hippocampal reactivations. The model combines forward trajectory sampling from current position and backward sampling through prioritized sweeping from states associated with large reward prediction errors until the two trajectories connect. This is repeated until stabilization of state-action values (convergence), which could explain why hippocampal reactivations drastically diminish when the animal's performance stabilizes. Simulations in a multiple T-maze task show that forward reactivations are prominently found at decision-points while backward reactivations are exclusively generated at reward sites. Finally, the model can generate imaginary trajectories that are not allowed to the agent during task performance. We raise some experimental predictions and implications for future studies of the role of the hippocampo-prefronto-striatal network in learning.

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“…This sequential replay of place cells occurs during sharp wave ripple activity observed in the local field potential in the hippocampus [53,54]. This sharp wave replay occurs during the low levels of cholinergic modulation that occur in quiet waking or slow wave sleep [51,55,56]. These replay events could mediate the consolidation of previously encoded information during periods of quiet waking or slow-wave sleep [6,38,53,54,57].…”

Section: Modeling Of Spatial Memory (Grid Cells Place Cells and Splimentioning

confidence: 99%

“…For example, models have replicated time cells using exponential decay with slow time constants [82,83], and have shown how this could arise from slow decay of a calcium-activated non-specific cation current [55,84] or a change in spiking threshold due to the calcium activated potassium current [83]. These models could be tested by altering the level of neuromodulators such as acetylcholine that regulate these intrinsic properties.…”

Section: Modeling Of Temporal Memory (Time Cells)mentioning

confidence: 99%

Models of spatial and temporal dimensions of memory

Hasselmo

Hinman

Dannenberg

et al. 2017

Current Opinion in Behavioral Sciences

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Episodic memory involves coding of the spatial location and time of individual events. Coding of space and time is also relevant to working memory, spatial navigation, and the disambiguation of overlapping memory representations. Neurophysiological data demonstrate that neuronal activity codes the current, past and future location of an animal as well as temporal intervals within a task. Models have addressed how neural coding of space and time for memory function could arise, with both dimensions coded by the same neurons. Neural coding could depend upon network oscillatory and attractor dynamics as well as modulation of neuronal intrinsic properties. These models are relevant to the coding of space and time involving structures including the hippocampus, entorhinal cortex, retrosplenial cortex, striatum and parahippocampal gyrus, which have been implicated in both animal and human studies.

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Transition between encoding and consolidation/replay dynamics via cholinergic modulation of CAN current: A modeling study

Cited by 14 publications

References 103 publications

Entorhinal–hippocampal neuronal circuits bridge temporally discontiguous events

Entorhinal–hippocampal neuronal circuits bridge temporally discontiguous events

Modeling awake hippocampal reactivations with model-based bidirectional search

Models of spatial and temporal dimensions of memory

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