Homeostatic Structural Plasticity Can Build Critical Networks

Ooyen, Arjen van; Butz‐Ostendorf, Markus

doi:10.1007/978-3-030-20965-0_7

Cited by 10 publications

(7 citation statements)

References 73 publications

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“…56,57 Spine-number-based structural plasticity, however, remains less understood due to divergent experimental observations. 58 Nevertheless, structural plasticity, which allows for synapse formation and rewiring, has been proven to greatly increase the memory capacity of the network 36 and maintain its criticality, 59,60 suggesting its discernible role in network function and potential interaction with functional plasticity. This manuscript elaborated on the synapse-number-based homeostatic structural plasticity rule and its interaction with the synaptic-weight-based homeostatic synaptic scaling rule.…”

Section: Discussionmentioning

confidence: 99%

The interplay between homeostatic synaptic scaling and homeostatic structural plasticity maintains the robust firing rate of neural networks

Han

Diaz-Pier

Lenz

et al. 2023

Preprint

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Critical network states and neural plasticity are essential for flexible behavior in an ever-changing environment, which allows for efficient information processing and experience-based learning. Synaptic-weight-based Hebbian plasticity and homeostatic synaptic scaling were considered the key players in enabling memory while stabilizing network dynamics. However, spine-number-based structural plasticity is not consistently reported as a homeostatic mechanism, leading to an insufficient understanding of its functional impact. Here, we combined live-cell microscopy of eGPF-tagged neurons in organotypic entorhinal-hippocampal tissue cultures and computational modeling to assess the interplay between homeostatic synaptic scaling and structural plasticity. By following individual dendritic segments, we demonstrated that the inhibition of excitatory neurotransmission did not linearly regulate dendritic spine density: Inhibition of AMPA receptors with a low concentration of 2,3-dioxo-6-nitro-7-sulfamoyl-benzo[f]quinoxaline (NBQX, 200 nM) significantly increased the spine density while complete blockade of AMPA receptors with 50 μM NBQX reduced spine density. Motivated by these results, we established network simulations in which a biphasic structural plasticity rule governs the activity-dependent formation of synapses. We showed that this bi-phasic rule maintained neural activity homeostasis upon stimulation and permitted both synapse formation and synapse loss, depending on the degree of activity deprivation. Homeostatic synaptic scaling affected the recurrent connectivity, modulated network activity, and influenced the outcome of structural plasticity. It reduced stimulation-triggered homeostatic synapse loss by downscaling synaptic weights; meanwhile, it rescued silencing-induced synapse degeneration by amplifying recurrent inputs via upscaling to reactivate silent neurons. Their interplay explains divergent results obtained in varied experimental settings. In summary, calcium-based synaptic scaling and homeostatic structural plasticity rules compete and compensate one another other to achieve an economical and robust control of firing rate homeostasis.

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Section: Discussionmentioning

confidence: 99%

The interplay between homeostatic synaptic scaling and homeostatic structural plasticity maintains the robust firing rate of neural networks

Han

Diaz-Pier

Lenz

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Modeling studies have also reported the significance of inhibitory synaptic plasticity in stabilizing networks near criticality (Stepp et al, 2015;Ma et al, 2019). Similarly, homeostatic structural plasticity can alter axonal and dendritic outgrowth to realize network criticality (van Ooyen and Butz-Ostendorf, 2019). These studies suggest that SOC may govern the network microstructural changes mediated through various plasticity mechanisms acting on multiple timescales.…”

Section: Framework To Characterize Network Dynamicsmentioning

confidence: 97%

“…Neurons maintain their target activity level in a homeostatic manner by scaling existing synapses (Turrigiano et al, 1998;Magee and Cook, 2000;Rathour and Narayanan, 2014), changing dendritic spine numbers (Trachtenberg et al, 2002), forming new synapses (Knott et al, 2006;Bastrikova et al, 2008), eliminating existing ones (Bastrikova et al, 2008), and even changing the axonal branching patterns (De Paola et al, 2006). Such homeostatic regulatory mechanisms are hypothesized to drive structural changes in neural networks (Butz et al, 2009;van Ooyen and Butz-Ostendorf, 2019). The mesoscopic network architecture, which is characterized by the connectivity between diverse neuronal types (Rees et al, 2016), also likely supports the emergence of complex dynamics.…”

Section: Introductionmentioning

confidence: 99%

Integrative Models of Brain Structure and Dynamics: Concepts, Challenges, and Methods

Venkadesh

Horn

2021

Front. Neurosci.

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The anatomical architecture of the brain constrains the dynamics of interactions between various regions. On a microscopic scale, neural plasticity regulates the connections between individual neurons. This microstructural adaptation facilitates coordinated dynamics of populations of neurons (mesoscopic scale) and brain regions (macroscopic scale). However, the mechanisms acting on multiple timescales that govern the reciprocal relationship between neural network structure and its intrinsic dynamics are not well understood. Studies empirically investigating such relationships on the whole-brain level rely on macroscopic measurements of structural and functional connectivity estimated from various neuroimaging modalities such as Diffusion-weighted Magnetic Resonance Imaging (dMRI), Electroencephalography (EEG), Magnetoencephalography (MEG), and functional Magnetic Resonance Imaging (fMRI). dMRI measures the anisotropy of water diffusion along axonal fibers, from which structural connections are estimated. EEG and MEG signals measure electrical activity and magnetic fields induced by the electrical activity, respectively, from various brain regions with a high temporal resolution (but limited spatial coverage), whereas fMRI measures regional activations indirectly via blood oxygen level-dependent (BOLD) signals with a high spatial resolution (but limited temporal resolution). There are several studies in the neuroimaging literature reporting statistical associations between macroscopic structural and functional connectivity. On the other hand, models of large-scale oscillatory dynamics conditioned on network structure (such as the one estimated from dMRI connectivity) provide a platform to probe into the structure-dynamics relationship at the mesoscopic level. Such investigations promise to uncover the theoretical underpinnings of the interplay between network structure and dynamics and could be complementary to the macroscopic level inquiries. In this article, we review theoretical and empirical studies that attempt to elucidate the coupling between brain structure and dynamics. Special attention is given to various clinically relevant dimensions of brain connectivity such as the topological features and neural synchronization, and their applicability for a given modality, spatial or temporal scale of analysis is discussed. Our review provides a summary of the progress made along this line of research and identifies challenges and promising future directions for multi-modal neuroimaging analyses.

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“…Experimental observations in developing neural cultures suggest that connections between neurons grow in a way such that the dynamics of the network eventually self-organizes to a critical point (i.e., observation of scale-free avalanches) [18,19]. Motivated by this observation, different models have been developed to explain how neural networks can grow connections to achieve and maintain such critical dynamics using homeostatic structural plasticity [19,[153][154][155][156][157][158] (for a review see [159]). In addition to homeostatic plasticity, other rewiring rules inspired by Hebbian learning were also proposed to bring the network dynamics toward criticality [160][161][162].…”

Section: Network Rewiring and Growthmentioning

confidence: 99%

Self-Organization Toward Criticality by Synaptic Plasticity

2021

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Self-organized criticality has been proposed to be a universal mechanism for the emergence of scale-free dynamics in many complex systems, and possibly in the brain. While such scale-free patterns were identified experimentally in many different types of neural recordings, the biological principles behind their emergence remained unknown. Utilizing different network models and motivated by experimental observations, synaptic plasticity was proposed as a possible mechanism to self-organize brain dynamics toward a critical point. In this review, we discuss how various biologically plausible plasticity rules operating across multiple timescales are implemented in the models and how they alter the network’s dynamical state through modification of number and strength of the connections between the neurons. Some of these rules help to stabilize criticality, some need additional mechanisms to prevent divergence from the critical state. We propose that rules that are capable of bringing the network to criticality can be classified by how long the near-critical dynamics persists after their disabling. Finally, we discuss the role of self-organization and criticality in computation. Overall, the concept of criticality helps to shed light on brain function and self-organization, yet the overall dynamics of living neural networks seem to harnesses not only criticality for computation, but also deviations thereof.

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Homeostatic Structural Plasticity Can Build Critical Networks

Cited by 10 publications

References 73 publications

The interplay between homeostatic synaptic scaling and homeostatic structural plasticity maintains the robust firing rate of neural networks

The interplay between homeostatic synaptic scaling and homeostatic structural plasticity maintains the robust firing rate of neural networks

Integrative Models of Brain Structure and Dynamics: Concepts, Challenges, and Methods

Self-Organization Toward Criticality by Synaptic Plasticity

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