Biology subjects

Fauth, M.

Publications and source records attributed to Fauth, M..

6 recordsLinked to original sources

bioRxiv2026

Plasticity Dynamics Improves Reservoir Performance on Mixed-Timescale Memory Tasks

Mixed-timescale signals are ubiquitous in natural stimuli and real-world applications, yet they remain challenging to process with recurrent neural networks. Reservoir computing offers an efficient framework for temporal processing, but extending its temporal range typically requires tuning neuronal timescales, which can compromise the representation of faster dynamics. Here, we propose an alternative approach in which synaptic plasticity provides an additional, slower timescale for computation. We introduce Hebbian plasticity into recurrent networks and show that its dynamics enable the reservoir to simultaneously process fast and slow components of mixed-timescale inputs: neuronal dynamics encode fast fluctuations, while synaptic dynamics support slower components. Remarkably, the resulting computational benefit extends over more than an order of magnitude beyond the intrinsic timescale of the plasticity. We show that this extended processing range arises from the dependence of the plasticity rule on postsynaptic activity, which effectively injects a filtered memory of past states into the network current. These results identify synaptic plasticity as a mechanism for extending the temporal processing capabilities of recurrent networks without altering neuronal timescales, and also suggest a potential computational role for plastic synapses in the processing of slowly varying stimuli in biological neuronal networks.

neuroscience↗

bioRxiv2026

Geometry-based dynamics of the postsynaptic density explain protein capture by an actin-spine-geometry-dependent synaptic tag

The synaptic tagging and capture (STC) hypothesis explains how early-phase plasticity is converted into its late phase through the coincidence of synaptic tagging and plasticity-related protein (PRP) availability. Yet the biophysical basis of this process remains poorly understood. Based on the hypothesis that the interaction of actin and spine geometry implement the synaptic tag, we here investigate the associated PRP capture mechanism. We propose that capture is implemented by PSD remodelling which is gated by local membrane curvature at the PSD periphery. Using computational modelling, we show that curvature variations around the PSD that arise from long-term potentiation (LTP) inducing stimuli indeed enable a PSD growth, reproducing late-phase potentiation and the maintenance of structural LTP. We further explore how the timing of PRP availability relative to tag formation and the initial spine size determine the extent of PSD enlargement, yielding outcomes consistent with experimental findings. Hence, our results support a structural interpretation of synaptic tagging and capture in which a transient, actin-driven geometric state of the spine encodes the tag, and curvature-mediated PRP recruitment stabilises synaptic changes, and thus render spine geometry as a key biophysical regulator of memory consolidation.

neuroscience↗

bioRxiv2025

A biophysical model of synaptic tagging-and-capture based on actin dynamics

According to the synaptic tagging-and-capture hypothesis, long-term synaptic plasticity requires postsynaptic sites to establish a synaptic tag, enabling them to capture plasticity-related products synthesized elsewhere in the neuron. Although electrophysiological studies have provided evidence for the existence of synaptic tags, it remains largely unresolved which biophysical processes or synaptic molecules or properties implement them. In this study, we examine the hypothesis that a mismatch between dendritic spine volume and the size of the postsynaptic density (PSD) inheres the essential characteristics of a synaptic tag. To test this hypothesis, we developed a computational model that integrates established principles of calcium-dependent synaptic plasticity with the complex biochemical dynamics of actin, which is a key structural protein determining dendritic spine geometry. Using this model, we demonstrate the plausibility of our hypothesis by showing that the model can reproduce and explain a broad range of experimental findings across diverse synaptic plasticity protocols at the level of individual synapses as well as heterosynaptic plasticity protocols involving two synapses. Furthermore, the model predicts that the repeated induction of plasticity within a one-hour time window results in a nonlinear accumulation of synaptic changes, reminiscent of the spacing effect observed in psychological studies of learning and memory. These results offer a concise mechanistic framework for understanding critical synaptic processes and suggest how temporal disparities in structural and biochemical dynamics can form a memory trace that could act as a synaptic tag.

neuroscience↗

bioRxiv2025

Stable Actin As Synaptic Tag

According to the tagging and capture hypothesis, long-lasting long-term potentiation (LTP) requires protein synthesis and a synaptic tag, which is a synapse specific memory of the synapse with a so far unclear molecular or biophysical identity. Here we use an interdisciplinary approach to explore the hypothesis that interaction between the dynamics of actin and the spine geometry can provide such a memory. Using a mathematical model, we demonstrate that this implementation of the tag requires an increase in the stable, cross-linked pool of actin filaments, and is not possible without this stable pool. Using FRAP experiments, we show that such an increase in stable actin can indeed be observed after cLTP in vitro. Thus, the interaction between actin dynamics and spine geometry could indeed serve as a synaptic tag for LTP.

neuroscience↗

bioRxiv2020

Actin in Dendritic Spines Self-Organizes into a Critical State

It is known that dendritic spines change their size and shape spontaneously and sometimes to a large degree, but the function of this remains unclear. Here, we quantify these changes using time-series analysis of confocal data and demonstrate that spine size can follow different autoregressive integrated moving average (ARIMA) models and that shape- and size-changes are not correlated. We capture this behavior with a biophysical model, based on the spines actin dynamics, and find the presence of 1/f noise. When investigating its origins, the model predicts that actin in the dendritic spines self-organizes into a critical state, which creates a fine balance between static actin filaments and free monomers. We speculate that such a balance might be functionally beneficially to allow a spine to quickly reconfigure itself after LTP induction.

biophysics↗

bioRxiv2019

Modeling the shape of synaptic spines by their actin dynamics

Dendritic spines are the morphological basis of excitatory synapses in the cortex and their size and shape correlates with functional synaptic properties. Recent experiments show that spines exhibit large shape fluctuations that are not related to activity-dependent plasticity but nonetheless might influence memory storage at their synapses. To investigate the determinants of such spontaneous fluctuations, we propose a mathematical model for the dynamics of the spine shape and analyze it in 2D -- related to experimental microscopic imagery -- and in 3D. We show that the spine shape is governed by a local imbalance between membrane tension and the expansive force from actin bundles that originates from discrete actin polymerization foci. Experiments have shown that only few such polymerization foci co-exist at any time in a spine, each having limited life time. The model shows that the momentarily existing set of such foci pushes the membrane along certain directions until foci are replaced and other directions may now be affected. We explore these relations in depth and use our model to predict shape and temporal characteristics of spines from the different biophysical parameters involved in actin polymerization. Reducing the model further to a single recursive equation we finally demonstrate that the temporal evolution of the number of active foci is sufficient to predict the size of the model-spines. Thus, our model provides the first platform to study the relation between molecular and morphological properties of the spine with a high degree of biophysical detail.\n\nAuthor summarySynaptic spines are post-synaptic contact points for pre-synaptic signals in many cortical neurons and it has been shown that synaptic transmission is correlated with spine size. However, spine size and shape can vary quite strongly on short time scales and it is currently unknown how these shape variations emerge. In this study we present a biophysical model that links spine shape fluctuations to the dynamics of the spines actin-based cytoskeleton. We show that shape fluctuations arise from the fact that fast actin polymerization in a spine is a discrete process happening at only few polymerization foci. Life and death of these foci determine from moment to moment how the membrane bulges or retracts. We provide an in-depth analysis of this effect for a large set of biophysical parameters and quantify the spatial-temporal structure of the spines. Our model, thus, provides a quantitative characterization of the link between spine morphology and the underlying molecular processes, which forms an essential step towards a better understanding of synaptic transmission during steady state but also during synaptic plasticity.

neuroscience↗