Linking spontaneous and stimulated spine dynamics

Eggl, Maximilian F.; Chater, Thomas E.; Petkovic, Janko; Goda, Yukiko; Tchumatchenko, Tatjana

doi:10.1038/s42003-023-05303-1

Cited by 3 publications

(2 citation statements)

References 60 publications

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“…This is therefore an alternative solution to the Stability vs Neuronal Specialization dilemma [19]. Here we have focused on weights between zero and one, and have excluded the possibility of further representing the correlation structure using long-tail distributions, which could be obtained via intrinsic noise in the synaptic plasticity [20,21]. A similar two-stage learning could be implemented by imposing a parameterized log dependence such that filopodia follow add-STDP whilst spines follow log-STDP.…”

Section: Discussionmentioning

confidence: 99%

Learning with filopodia and spines: Complementary strong and weak competition lead to specialized, graded, and protected receptive fields

Albesa-González,

Clopath

2024

PLoS Comput Biol

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Filopodia are thin synaptic protrusions that have been long known to play an important role in early development. It has recently been found that they are more abundant in the adult cortex than previously thought, and more plastic than spines (button-shaped mature synapses). Inspired by these findings, we introduce a new model of synaptic plasticity that jointly describes learning of filopodia and spines. The model assumes that filopodia exhibit strongly competitive learning dynamics -similarly to additive spike-timing-dependent plasticity (STDP). At the same time it proposes that, if filopodia undergo sufficient potentiation, they consolidate into spines. Spines follow weakly competitive learning, classically associated with multiplicative, soft-bounded models of STDP. This makes spines more stable and sensitive to the fine structure of input correlations. We show that our learning rule has a selectivity comparable to additive STDP and represents input correlations as well as multiplicative models of STDP. We also show how it can protect previously formed memories and act as a synaptic consolidation mechanism. Overall, our results can be seen as a phenomenological description of how filopodia and spines could cooperate to overcome the difficulties that strong and weak competition each have.

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Section: Discussionmentioning

confidence: 99%

Learning with filopodia and spines: Complementary strong and weak competition lead to specialized, graded, and protected receptive fields

Albesa-González,

Clopath

2024

PLoS Comput Biol

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“…However, with respect to intrinsic vs. extrinsic growth dynamics, it has recently become clear that the (proportionate, i.e., multiplicative) intrinsic synaptic dynamics are sufficient to generate lognormallike synapse size distributions (Hazan and Ziv 2020;Rößler et al 2023). Therefore, new computational models include a combination of intrinsic and extrinsic synaptic dynamics (Eggl et al 2023;Rößler et al 2023).…”

Section: Lognormality Depends On a Large Number Of Independent Variablesmentioning

confidence: 99%

Maintenance of Lognormal‐Like Skewed Dendritic Spine Size Distributions in Dentate Granule Cells of TNF, TNF‐R1, TNF‐R2, and TNF‐R1/2‐Deficient Mice

Rößler,

Smilovic,

Vuksic

et al. 2024

J of Comparative Neurology

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Dendritic spines are sites of synaptic plasticity and their head size correlates with the strength of the corresponding synapse. We recently showed that the distribution of spine head sizes follows a lognormal‐like distribution even after blockage of activity or plasticity induction. As the cytokine tumor necrosis factor (TNF) influences synaptic transmission and constitutive TNF and receptor (TNF‐R)‐deficiencies cause changes in spine head size distributions, we tested whether these genetic alterations disrupt the lognormality of spine head sizes. Furthermore, we distinguished between spines containing the actin‐modulating protein synaptopodin (SP‐positive), which is present in large, strong and stable spines and those lacking it (SP‐negative). Our analysis revealed that neither TNF‐deficiency nor the absence of TNF‐R1, TNF‐R2 or TNF‐R 1 and 2 (TNF‐R1/R2) degrades the general lognormal‐like, skewed distribution of spine head sizes (all spines, SP‐positive spines, SP‐negative spines). However, TNF, TNF‐R1 and TNF‐R2‐deficiency affected the width of the lognormal distribution, and TNF‐R1/2‐deficiency shifted the distribution to the left. Our findings demonstrate the robustness of the lognormal‐like, skewed distribution, which is maintained even in the face of genetic manipulations that alter the distribution of spine head sizes. Our observations are in line with homeostatic adaptation mechanisms of neurons regulating the distribution of spines and their head sizes.

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SpyDen: Automating molecular and structural analysis across spines and dendrites

Eggl,

Wagle,

Filling

et al. 2024

Preprint

Self Cite

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Investigating the molecular composition across neural compartments such as axons, dendrites, or synapses is critical for our understanding of learning and memory. State-of-the-art microscopy techniques can now resolve individual molecules and pinpoint their position with micrometre or even nanometre resolution across tens or hundreds of micrometres, allowing the labelling of multiple structures of interest simultaneously. Algorithmically, tracking individual molecules across hundreds of micrometres and determining whether they are inside any cellular compartment of interest can be challenging. Historically, microscopy images are annotated manually, often using multiple software packages to detect fluorescence puncta (e.g. labelled mRNAs) and then trace and quantify cellular compartments of interest. Advanced ANN-based automated tools, while powerful, are often able to help only with selected parts of the data analysis pipeline, may be optimised for specific spatial resolutions or cell preparations or may not be fully open source and open access to be sufficiently customisable. To address these challenges, we developed SpyDen. SpyDen is a Python package based upon three principles:i)ease of use for multi-task scenarios,ii)open-source accessibility and data export to a common, open data format,iii)the ability to edit any software-generated annotation and generalise across spatial resolutions. Equipped with a graphical user interface and accompanied by video tutorials, SpyDen provides a collection of powerful algorithms that can be used for neurite and synapse detection as well as fluorescent puncta and intensity analysis. We validated SpyDen using expert annotation across numerous use cases to prove a powerful, integrated platform for efficient and reproducible molecular imaging analysis.

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Linking spontaneous and stimulated spine dynamics

Cited by 3 publications

References 60 publications

Learning with filopodia and spines: Complementary strong and weak competition lead to specialized, graded, and protected receptive fields

Learning with filopodia and spines: Complementary strong and weak competition lead to specialized, graded, and protected receptive fields

Maintenance of Lognormal‐Like Skewed Dendritic Spine Size Distributions in Dentate Granule Cells of TNF, TNF‐R1, TNF‐R2, and TNF‐R1/2‐Deficient Mice

SpyDen: Automating molecular and structural analysis across spines and dendrites

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