The role of intrinsic excitability in the evolution of memory: Significance in memory allocation, consolidation, and updating

Chen, Lingxuan; Cummings, Kirstie A.; Mau, William; Zaki, Yosif; Dong, Zhe; Rabinowitz, Sima; Clem, Roger L.; Shuman, Tristan; Cai, Denise J.

doi:10.1016/j.nlm.2020.107266

Cited by 53 publications

(36 citation statements)

References 204 publications

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“…Similar fluctuations have been reported in barrel cortex ( Margolis et al, 2012 ), motor regions ( Liberti et al, 2016 ; Peters et al, 2017 ; Rokni et al, 2007 ), lateral entorhinal cortex ( Tsao et al, 2018 ), and parietal cortex ( Driscoll et al, 2017 ), suggesting that while all these regions differentially contribute to memory, drift may be a ubiquitous feature of neural systems that seems to threaten memory stability. Alternatively, drift could actually reflect the inherent flexibility of the neural code and stem from numerous parallel neurobiological processes including spontaneous synaptic remodeling ( Ziv and Brenner, 2018 ), and dynamic changes in cellular excitability ( Figure 1c and d ; Chen et al, 2020 ; Slomowitz et al, 2015 ). Next, we will consider how these processes could potentially contribute to the way in whichdynamic neural codes can support memory flexibility.…”

Section: Introductionmentioning

confidence: 99%

“…In other words, high spine turnover rates provided a greater number of new spines available for memory encoding but may have also enabled faster sampling across synaptic space and therefore a quicker arrival to a synaptic connectivity pattern that adequately encoded the new information ( Castello-Waldow et al, 2020 ; Frank et al, 2018 ; Rumpel and Triesch, 2016 ; Xu et al, 2009 ). Changes in synaptic connectivity could also heterogeneously influence the likelihood of spiking (intrinsic excitability) in neuronal subpopulations, which would in turn increase their likelihood of participating in future memory-encoding ensembles (i.e., memory allocation; Box 2 ; Buzsáki, 2010 ; Chen et al, 2020 ; Rogerson et al, 2014 ; Yiu et al, 2014 ; Zhou et al, 2009 ). In this way, the brain could prioritize different rosters of neurons to diversify eligibility for memory encoding or updating ( Margolis et al, 2012 ; Rogerson et al, 2014 ; Trouche et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

“…This theory, known as the ‘memory allocation hypothesis’, suggests that the excitability of a neuron predisposes it for encoding a memory ( Box 2 ; Rogerson et al, 2014 ; Zhou et al, 2009 ). Moreover, the excitability and connectivity of neuronal populations are constantly changing, which would suggest that different sets of neurons encode or update memories as these neurons increase in prominence from the perspective of the network ( Buzsáki, 2010 ; Chen et al, 2020 ; Minerbi et al, 2009 ; Slomowitz et al, 2015 ). In other words, drift, as a result of constantly fluctuating excitability ( Chen et al, 2020 ; Rogerson et al, 2014 ; Slomowitz et al, 2015 ), synapses ( Holtmaat and Svoboda, 2009 ; Rumpel and Triesch, 2016 ; Xu et al, 2009 ; Yang et al, 2009 ; Ziv and Brenner, 2018 ), and intracellular proteomes ( Rogerson et al, 2014 ), define the degree to which single neurons compete to participate in encoding a memory ( Han et al, 2007 ) based on their excitability and connectivity to an existing memory engram.…”

Section: Introductionmentioning

confidence: 99%

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The brain in motion: How ensemble fluidity drives memory-updating and flexibility

Mau

Hasselmo

Cai

2020

eLife

Self Cite

105

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While memories are often thought of as flashbacks to a previous experience, they do not simply conserve veridical representations of the past but must continually integrate new information to ensure survival in dynamic environments. Therefore, ‘drift’ in neural firing patterns, typically construed as disruptive ‘instability’ or an undesirable consequence of noise, may actually be useful for updating memories. In our view, continual modifications in memory representations reconcile classical theories of stable memory traces with neural drift. Here we review how memory representations are updated through dynamic recruitment of neuronal ensembles on the basis of excitability and functional connectivity at the time of learning. Overall, we emphasize the importance of considering memories not as static entities, but instead as flexible network states that reactivate and evolve across time and experience.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The brain in motion: How ensemble fluidity drives memory-updating and flexibility

Mau

Hasselmo

Cai

2020

eLife

Self Cite

105

View full text Add to dashboard Cite

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“…Alterations in intrinsic physiological properties of a neuron (i.e., membrane conductance) control the probability of action potential generation ( Hille, 1978 ), thereby favoring or limiting synaptic vesicle release. As such, the excitability state of a neuron influences neurotransmission and contributes to synaptic changes involved in memory encoding and consolidation ( Zhang and Linden, 2003 ; Chen L. et al, 2020 ). Several studies have demonstrated that the excitability state of a neuron affects its probability to be allocated in an engram network ( Zhou et al, 2009 ; Yiu et al, 2014 ; Brebner et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A Synaptic Framework for the Persistence of Memory Engrams

Rao-Ruiz

Visser

Mitrić

et al. 2021

Front. Synaptic Neurosci.

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The ability to store and retrieve learned information over prolonged periods of time is an essential and intriguing property of the brain. Insight into the neurobiological mechanisms that underlie memory consolidation is of utmost importance for our understanding of memory persistence and how this is affected in memory disorders. Recent evidence indicates that a given memory is encoded by sparsely distributed neurons that become highly activated during learning, so-called engram cells. Research by us and others confirms the persistent nature of cortical engram cells by showing that these neurons are required for memory expression up to at least 1 month after they were activated during learning. Strengthened synaptic connectivity between engram cells is thought to ensure reactivation of the engram cell network during retrieval. However, given the continuous integration of new information into existing neuronal circuits and the relatively rapid turnover rate of synaptic proteins, it is unclear whether a lasting learning-induced increase in synaptic connectivity is mediated by stable synapses or by continuous dynamic turnover of synapses of the engram cell network. Here, we first discuss evidence for the persistence of engram cells and memory-relevant adaptations in synaptic plasticity, and then propose models of synaptic adaptations and molecular mechanisms that may support memory persistence through the maintenance of enhanced synaptic connectivity within an engram cell network.

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“…Multiplexing implemented through periodic modulation of firing-rate population codes enables flexible reconfiguration of effective connectivity. Memory retrieval is a dynamic process continuously regulated by both synaptic and intrinsic neural mechanisms, e.g., intrinsic excitability, synaptic plasticity, and interactions among brain areas ( Chen et al, 2020 ). The findings suggest that neural dynamics underlying accuracy are different from those undeserving memory retrieval speed.…”

Section: Discussionmentioning

confidence: 99%

Gauging Working Memory Capacity From Differential Resting Brain Oscillations in Older Individuals With A Wearable Device

Borhani

Zhao

Kelly

et al. 2021

Front. Aging Neurosci.

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Working memory is a core cognitive function and its deficits is one of the most common cognitive impairments. Reduced working memory capacity manifests as reduced accuracy in memory recall and prolonged speed of memory retrieval in older adults. Currently, the relationship between healthy older individuals’ age-related changes in resting brain oscillations and their working memory capacity is not clear. Eyes-closed resting electroencephalogram (rEEG) is gaining momentum as a potential neuromarker of mild cognitive impairments. Wearable and wireless EEG headset measuring key electrophysiological brain signals during rest and a working memory task was utilized. This research’s central hypothesis is that rEEG (e.g., eyes closed for 90 s) frequency and network features are surrogate markers for working memory capacity in healthy older adults. Forty-three older adults’ memory performance (accuracy and reaction times), brain oscillations during rest, and inter-channel magnitude-squared coherence during rest were analyzed. We report that individuals with a lower memory retrieval accuracy showed significantly increased alpha and beta oscillations over the right parietal site. Yet, faster working memory retrieval was significantly correlated with increased delta and theta band powers over the left parietal sites. In addition, significantly increased coherence between the left parietal site and the right frontal area is correlated with the faster speed in memory retrieval. The frontal and parietal dynamics of resting EEG is associated with the “accuracy and speed trade-off” during working memory in healthy older adults. Our results suggest that rEEG brain oscillations at local and distant neural circuits are surrogates of working memory retrieval’s accuracy and processing speed. Our current findings further indicate that rEEG frequency and coherence features recorded by wearable headsets and a brief resting and task protocol are potential biomarkers for working memory capacity. Additionally, wearable headsets are useful for fast screening of cognitive impairment risk.

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The role of intrinsic excitability in the evolution of memory: Significance in memory allocation, consolidation, and updating

Cited by 53 publications

References 204 publications

The brain in motion: How ensemble fluidity drives memory-updating and flexibility

The brain in motion: How ensemble fluidity drives memory-updating and flexibility

A Synaptic Framework for the Persistence of Memory Engrams

Gauging Working Memory Capacity From Differential Resting Brain Oscillations in Older Individuals With A Wearable Device

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