Hippocampal network reorganization underlies the formation of a temporal association memory

Ahmed, Mohsin; Priestley, James B.; Castro, Angel; Balough, Elizabeth M.; Lavoie, Erin; Mazzucato, Luca; Fusi, Stefano; Losonczy, Attila

doi:10.1101/613638

Cited by 15 publications

(23 citation statements)

References 61 publications

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“…As in Ahmed et al (2020), we discarded any events whose amplitude was below 4 median absolute deviations of the raw trace, and discretized the resulting signal for all subsequent analysis.…”

Section: Neural Data Analysismentioning

confidence: 99%

Signatures of rapid synaptic learning in the hippocampus during novel experiences

Priestley

Bowler

Rolotti

et al. 2021

Preprint

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Neurons in the hippocampus exhibit striking selectivity for specific combinations of sensory features, forming representations which are thought to subserve episodic memory. Even during a completely novel experience, ensembles of hippocampal ``place cells'' are rapidly configured such that the population sparsely encodes visited locations, stabilizing within minutes of the first exposure to a new environment. What cellular mechanisms enable this fast encoding of experience? Here we leverage virtual reality and large scale neural recordings to dissect the effects of novelty and experience on the dynamics of place field formation. We show that the place fields of many CA1 neurons transiently shift locations and modulate the amplitude of their activity immediately after place field formation, consistent with rapid plasticity mechanisms driven by plateau potentials and somatic burst spiking. These motifs were particularly enriched during initial exploration of a novel context and decayed with experience. Our data suggest that novelty modulates the effective learning rate in CA1, favoring burst-driven field formation to support fast synaptic updating during new experience.

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Section: Neural Data Analysismentioning

confidence: 99%

Signatures of rapid synaptic learning in the hippocampus during novel experiences

Priestley

Bowler

Rolotti

et al. 2021

Preprint

Self Cite

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“…Figure 1C). Furthermore, we simulated variations of the original TUNL task that controlled for (1) the memory demands during the delay period and (2) the temporal structure of the episodes in order to investigate the effects of task demand and task structure on the emergence and informativeness of sequence cells, which had been the subject of discrepancies in rodent studies 8,9,26 . We found that sequence cells emerged across task demands and task structures (Figure 3B, 4B).…”

Section: Discussionmentioning

confidence: 99%

“…Previous research on rodent hippocampal time cells has led to disputes on whether these sequential activity patterns indeed contribute to stimulus-encoding in memory, or are merely an artifact 8,9,26 . Here, we addressed these discrepancies and investigated the effect of task demand on temporal representations by developing a non-mnemonic version of the TUNL task (NoMem TUNL) wherein, during the choice phase, the agent may choose either the match or the nonmatch location to receive a reward, thus eliminating the demand for the agent to remember the sample location across the delay period (Figure 3A).…”

Section: The Emergence Of Stimulus-encoding Sequences Depends On Cognitive Demandsmentioning

confidence: 99%

“…Sabariego et al 8 reported that sequences formed by the hippocampal time cells during a spatial working memory task did not distinguish between different trial types. More recently, Ahmed et al 26 showed that CA1 activity patterns were neither consistent nor informative during the delay period in trace fear conditioning. These findings hint at the possibility that the observations of so-called time cells may merely be an epiphenomenon as a consequence of assumption-based analyses, rather than a mechanism by which the brain bridges mnemonic gaps between events.…”

Section: Introductionmentioning

confidence: 99%

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Time cell encoding in deep reinforcement learning agents depends on mnemonic demands

Lin

Richards

2021

Preprint

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The representation of "what happened when" is central to encoding episodic and working memories. Recently discovered hippocampal time cells are theorized to provide the neural substrate for such representations by forming distinct sequences that both encode time elapsed and sensory content. However, little work has directly addressed to what extent cognitive demands and temporal structure of experimental tasks affect the emergence and informativeness of these temporal representations. Here, we trained deep reinforcement learning (DRL) agents on a simulated trial-unique nonmatch-to-location (TUNL) task, and analyzed the activities of artificial recurrent units using neuroscience-based methods. We show that, after training, representations resembling both time cells and ramping cells (whose activity increases or decreases monotonically over time) simultaneously emerged in the same population of recurrent units. Furthermore, with simulated variations of the TUNL task that controlled for (1) memory demands during the delay period and (2) the temporal structure of the episodes, we show that memory demands are necessary for the time cells to encode information about the sensory stimuli, while the temporal structure of the task only affected the encoding of "what" and "when" by time cells minimally. Our findings help to reconcile current discrepancies regarding the involvement of time cells in memory-encoding by providing a normative framework. Our modelling results also provide concrete experimental predictions for future studies.

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“…end, some argue that hippocampal ensembles are flexible in regard to the content of their inputs and, consequently, will ordinally map any feature space (though one should consider that dense innervation of the hippocampus from regions carrying head direction (HD) information may bias the system to respond to the spatial domain in rodents). An important question may be if there is any continuous feature of the task space that would not be reliably mapped by hippocampal sequences (Ahmed et al, 2020;Aronov et al, 2017). Time cell responses have been shown in a wide range of different structures, including hippocampal region CA1 (Kraus et al, 2013;Mau et al, 2018;Pastalkova et al, 2008), hippocampal region CA3 (Salz et al, 2016), and the entorhinal cortex (Heys and Dombeck, 2018;Kraus et al, 2015;Tsao et al, 2018).…”

Section: Coding Of Time (Time Cells)mentioning

confidence: 99%

Neurophysiological coding of space and time in the hippocampus, entorhinal cortex, and retrosplenial cortex

Alexander

Robinson

Dannenberg

et al. 2020

Brain and Neuroscience Advances

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Neurophysiological recordings in behaving rodents demonstrate neuronal response properties that may code space and time for episodic memory and goal-directed behaviour. Here, we review recordings from hippocampus, entorhinal cortex, and retrosplenial cortex to address the problem of how neurons encode multiple overlapping spatiotemporal trajectories and disambiguate these for accurate memory-guided behaviour. The solution could involve neurons in the entorhinal cortex and hippocampus that show mixed selectivity, coding both time and location. Some grid cells and place cells that code space also respond selectively as time cells, allowing differentiation of time intervals when a rat runs in the same location during a delay period. Cells in these regions also develop new representations that differentially code the context of prior or future behaviour allowing disambiguation of overlapping trajectories. Spiking activity is also modulated by running speed and head direction, supporting the coding of episodic memory not as a series of snapshots but as a trajectory that can also be distinguished on the basis of speed and direction. Recent data also address the mechanisms by which sensory input could distinguish different spatial locations. Changes in firing rate reflect running speed on long but not short time intervals, and few cells code movement direction, arguing against path integration for coding location. Instead, new evidence for neural coding of environmental boundaries in egocentric coordinates fits with a modelling framework in which egocentric coding of barriers combined with head direction generates distinct allocentric coding of location. The egocentric input can be used both for coding the location of spatiotemporal trajectories and for retrieving specific viewpoints of the environment. Overall, these different patterns of neural activity can be used for encoding and disambiguation of prior episodic spatiotemporal trajectories or for planning of future goal-directed spatiotemporal trajectories.

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Hippocampal network reorganization underlies the formation of a temporal association memory

Cited by 15 publications

References 61 publications

Signatures of rapid synaptic learning in the hippocampus during novel experiences

Signatures of rapid synaptic learning in the hippocampus during novel experiences

Time cell encoding in deep reinforcement learning agents depends on mnemonic demands

Neurophysiological coding of space and time in the hippocampus, entorhinal cortex, and retrosplenial cortex

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