Synaptic Tenacity or Lack Thereof: Spontaneous Remodeling of Synapses

Ziv, Noam; Brenner, Naama

doi:10.1016/j.tins.2017.12.003

Cited by 87 publications

(94 citation statements)

References 71 publications

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“…Such fluctuation have been observed in the here-analyzed data and also in other studies (Tønnesen et al, 2014; Minerbi et al, 2009). In this way, the model is compatible with physiologically relevant findings at individual spines, and also in the neuronal network, that show the importance of PSD changes during the lifetime of a spine (see Ziv & Brenner (2018) for a review).…”

Section: Discussionsupporting

confidence: 80%

Actin in Dendritic Spines Self-Organizes into a Critical State

Bonilla-Quintana

Wörgötter

D’Este³

et al. 2020

Preprint

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12It is known that dendritic spines change their size and shape sponta-13 neously and sometimes to a large degree, but the function of this remains 14 unclear. Here, we quantify these changes using time-series analysis of con-15 focal data and demonstrate that spine size can follow different autoregres-16 sive integrated moving average (ARIMA) models and that shape-and size-17 changes are not correlated. We capture this behavior with a biophysical 18 model, based on the spines' actin dynamics, and find the presence of 1/f 19 noise. When investigating its origins, the model predicts that actin in the 20 dendritic spines self-organizes into a critical state, which creates a fine bal-21 ance between static actin filaments and free monomers. We speculate that 22 such a balance might be functionally beneficially to allow a spine to quickly 23 reconfigure itself after LTP induction. 24 25 Keywords: dendritic spines, actin, 1/f noise, self-organized 26 criticality, shape fluctuations, shape descriptors 27 * Corresponding Author and Lead Contact: mayte.bonilla-quintana@phys.uni-goettingen.deDendritic spines are small protrusions of dendrites where the postsynaptic part of 29 most excitatory synapses is located. It is well known that size and shape changes of 30 these spines are correlated with changes of the strength of excitatory synaptic con-31 nections (Matsuzaki et al., 2004). However, Fischer et al. (1998 observed that the 32 shape of dendritic spines also varies spontaneously during 30-minute recordings at 33 a 1.5-second frame-rate under basal conditions, under which spines are at the min-34 imum level of activity required to maintain their functions. Interestingly, nearly 35 all spines in these recordings survived and their volume and density stayed mostly 36 constant. Similar spine variations were measured by Dunaevsky et al. (1999) in 37 time-lapse images of acute and cultured slices from cerebellar Purkinje cells, and 38 cortical and hippocampal pyramidal cells. Rapid spine shape changes have also 39 been observed in living organotypic hippocampal mouse brain slices (Testa et al., 40 2012) and in the somatosensoty cortex of the adult mouse in vivo (Berning et al., 41 2012). 42Rapid changes of dendritic spine shapes are related to actin dynamics (Fischer 43 et al., 1998; Dunaevsky et al., 1999). Actin is a globular protein that assembles 44 into filaments, which are polar structures that continuously undergo a treadmilling 45 process. In this process, actin monomers are polymerized at the (+) end (barbed 46 end) of the actin filaments, while these filaments are depolymerized at the (-) end. 47Actin polymerization generates a force that moves the cell membrane forward 48 (Mogilner & Oster, 1996). Moreover, blockage of actin polymerization hinders 49 spine motility (Fischer et al., 1998; Dunaevsky et al., 1999). Honkura et al. (2008) 50 observed filamentous actin within a single spine and found that a dynamic pool of 51 actin, which has a fast treadmilling velocity and is mainly localized at the tip of 52 ...

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Section: Discussionsupporting

confidence: 80%

Actin in Dendritic Spines Self-Organizes into a Critical State

Bonilla-Quintana

Wörgötter

D’Este³

et al. 2020

Preprint

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“…Excitability changes could arise from the spontaneous remodeling of synaptic connections onto a neuron, whether from remodeling of dendritic spines (Fu et al, 2012;Hayashi-Takagi et al, 2015), or changes of receptor and protein expression within a synapse (Wolff et al, 1995;Ziv and Brenner, 2018). Alternatively, these changes could arise from long-lasting effects on neuron excitability of neuromodulators accumulated in mPFC during training (Seamans and Yang, 2004;Tierney et al, 2008;Dembrow et al, 2010;Benchenane et al, 2011).…”

Section: Excitability Drives Constant Population Plasticitymentioning

confidence: 99%

Medial prefrontal cortex population activity is plastic irrespective of learning

2019

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The prefrontal cortex (PFC) is thought to learn the relationships between actions and their outcomes. But little is known about what changes to population activity in PFC are specific to learning these relationships. Here we characterize the plasticity of population activity in the medial PFC (mPFC) of male rats learning rules on a Y-maze. First, we show that the population always changes its patterns of joint activity between the periods of sleep either side of a training session on the maze, regardless of successful rule learning during training. Next, by comparing the structure of population activity in sleep and training, we show that this population plasticity differs between learning and nonlearning sessions. In learning sessions, the changes in population activityin post-training sleep incorporate the changes to the population activity during training on the maze. In nonlearning sessions, the changes in sleep and training are unrelated. Finally, we show evidence that the nonlearning and learning forms of population plasticity are driven by different neuron-level changes, with the nonlearning form entirely accounted for by independent changes to the excitability of individual neurons, and the learning form also including changes to firing rate couplings between neurons. Collectively, our results suggest two different forms of population plasticity in mPFC during the learning of action-outcome relationships: one a persistent change in population activity structure decoupled from overt rule-learning, and the other a directional change driven by feedback during behavior. The PFC is thought to represent our knowledge about what action is worth doing in which context. But we do not know how the activity of neurons in PFC collectively changes when learning which actions are relevant. Here we show, in a trial-and-error task, that population activity in PFC is persistently changing, regardless of learning. Only during episodes of clear learning of relevant actions are the accompanying changes to population activity carried forward into sleep, suggesting a long-lasting form of neural plasticity. Our results suggest that representations of relevant actions in PFC are acquired by reward imposing a direction onto ongoing population plasticity.

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“…But perhaps we can recast the perspective somewhat. As an adaptive system, the brain is in a constant state of testing new configurations to enhance capacity (Minerbi et al, 2009;Ziv & Brenner, 2018). While this is generically considered plasticity, these reconfigurations seem to be spontaneous and persist when the outcome is adaptive, which is property of complex adaptive systems.…”

Section: Modeling Sfm In Epilepsymentioning

confidence: 99%

The Hidden Repertoire of Brain Dynamics and Dysfunction

McIntosh

Jirsa

2019

Preprint

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The purpose of this paper is to describe a framework for the understanding of rules that govern how neural system dynamics are coordinated to produce behavior. The framework, structured flows on manifolds (SFM), posits that neural processes are flows depicting system interactions that occur on relatively low-dimension manifolds, which constrain possible functional configurations. Although this is a general framework, we focus on the application to brain disorders. We first explain the Epileptor, a phenomenological computational model showing fast and slow dynamics, but also a hidden repertoire whose expression is similar to refractory status epilepticus. We suggest that epilepsy represents an innate brain state whose potential may be realized only under certain circumstances. Conversely, deficits from damage or disease processes, such as stroke or dementia, may reflect both the disease process per se and the adaptation of the brain. SFM uniquely captures both scenarios.

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Synaptic Tenacity or Lack Thereof: Spontaneous Remodeling of Synapses

Cited by 87 publications

References 71 publications

Actin in Dendritic Spines Self-Organizes into a Critical State

Actin in Dendritic Spines Self-Organizes into a Critical State

Medial prefrontal cortex population activity is plastic irrespective of learning

The Hidden Repertoire of Brain Dynamics and Dysfunction

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