Biology subjects

Tetzlaff, C.

Publications and source records attributed to Tetzlaff, C..

At least 19 recordsLinked to original sources

bioRxiv2025

Functional Divergence of Axon-Carrying Dendrite (AcD) and NonAcD Cells in Learning and Stability

Axon-carrying dendrite (AcD) cells are a specialized class of hippocampal neurons where the axon initial segment originates from a basal dendrite rather than the soma, creating a privileged pathway for excitatory inputs on AcD branches to bypass perisomatic inhibition. However, their functional role in learning and synaptic stability remains unclear. To address this question, we modeled a CA3-CA1 network to compare the learning dynamics and synaptic stability of AcD and nonAcD cells. The results revealed that, during learning, these cell types employ distinct mechanisms. AcD cells primarily adopt a single-modal strategy, with all dendritic branches converging to encode inputs from a single assembly, whereas nonAcD cells follow a multi-modal approach, with individual branches encoding inputs from distinct assemblies. Additionally, consistent with experimental findings, our results suggest that during periods of high inhibition (such as ripples), AcD cells maintain stable synaptic weights, unlike the synaptic decay observed in nonAcD cells. These results, in line with experimental evidence, suggest that although the morphological distinction between AcD and nonAcD cells was long overlooked, it proves to be important, as it results in functional differences in learning mechanisms and in the capacity for stable information storage, highlighting their key role in learning and memory consolidation.

neuroscience↗

bioRxiv2025

Robust Input Disentanglement Through Dendritic Calcium-Mediated Action Potentials

In daily life, living beings encounter a continuous stream of mixed information, which has to be disentangled by the brain to form proper representations. Using computational modeling, we demonstrate that the interplay between dendritic calcium-mediated action potentials (dCaAPs) with synaptic plasticity and rewiring can enable single neurons to successfully perform this complex task. Compared to other types of dendritic spikes, dCaAPs exhibit a high triggering threshold, large, but graded spike amplitude, with lower amplitudes for stronger synaptic inputs. We show that these properties enable neurons to successfully learn to represent discrete items from a continuous input stream by facilitating the clustering of synapses with temporally correlated presynaptic activities onto the same dendritic branch. In comparison to NMDA spikes, dendrites generating dCaAPs can form representations of individual items more efficiently, independent of the temporal order of their presentation during learning -- whether randomly, sequentially, as part of a random stream of simultaneously shown input items, or even as items with shared properties. Thus, our results provide further evidence about the critical role of dCaAPs for the computational capabilities of single neurons.

neuroscience↗

bioRxiv2025

Controlling the Formation of Multiple Condensates in the Synapse

The postsynaptic density (PSD) is a molecule rich structure that continuously adapts its organization controlling synaptic physiology and transmission. Experimental studies have shown that this organization is dominated by the formation of condensates or (nano)clusters and by the seamless transition in their numbers as response to synaptic plasticity. In this study, we utilize different computational modeling frameworks to show that variations in the level of local protein concentrations together with the modulation of protein binding strengths can be key molecular factors in controlling the formation and number of clusters in the synapse. Comparison to MINFLUX data of spatial localization of PSD95 in the postsynapse under different activity conditions allowed us to derive predictions about the correlation between these two factors across a population of synapses. Co-variations of the factors, to mimic a simple LTP protocol, shows that a PSD containing a single cluster can reorganize to form multiple clusters that persists for long periods of time, providing a potential explanation of recent experimental data of PSD reorganization.

molecular biology↗

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↗

bioRxiv2024

PyRID: A Brownian dynamics simulator for reacting and interacting particles written in Python

Recent technological developments in molecular biology led to large data sets providing new insights into the molecular organisation of cells. To fully exploit their potential, these developments have to be complemented by computer simulations that allow to gain in-depth understanding on molecular principles. We developed the Python-based, reaction-diffusion simulator PyRID, integrating many features for the efficient simulation of molecular biological systems. Amongst others, PyRID is capable of simulating unimolecular and bimolecular reactions as well as pair-interactions to assess the dynamics resulting from individual interacting proteins to polydisperse substrates consisting of many different molecules. Furthermore, PyRID supports mesh-based compartments and surface diffusion of particles, enabling analyses of the interaction between (trans-)membrane proteins with intra- and extracellular proteins. PyRID is written entirely in Python, which is a programming language being known for its readability and easy accessibility, such that the scientific community can easily extend PyRID to its current and future needs.

neuroscience↗

bioRxiv2024

Calcium-based input timing learning

AO_SCPLOWBSTRACTC_SCPLOWStimulus-triggered synaptic long-term plasticity is the foundation of learning and other cognitive abilities of the brain. In general, long-term synaptic plasticity is subdivided into two different forms: homosynaptic plasticity describes synaptic changes at stimulated synapses, while heterosynaptic plasticity summarizes synaptic changes at non-stimulated synapses. For homosynaptic plasticity, the Ca2+-hypothesis pinpoints the calcium concentration within a stimulated dendritic spine as key mediator or controller of underlying biochemical and -physical processes. On the other hand, for heterosynaptic plasticity, although theoretical studies attribute important functional roles to it, such as synaptic competition and cooperation, experimental results remain ambiguous regarding its manifestation and biological basis. By integrating insights from Ca2+-dependent homosynaptic plasticity with experimental data of dendritic Ca2+-dynamics, we developed a mathematical model that describes the complex temporal and spatial dynamics of calcium in dendrites.We show that the influx of calcium into a stimulated spine can lead to its diffusion to neighboring spines, triggering heterosynaptic effects such as synaptic competition or cooperation. By considering different input strengths, our model explains the ambiguity of reported experimental results of heterosynaptic plasticity, suggesting that the Ca2+-hypothesis of homosynaptic plasticity can be extended to also model heterosynaptic plasticity. Furthermore, our model predicts thata synapse can modulate the expression of homosynaptic plasticity at a neighboring synapse in an input-timing-dependent manner, without the need of postsynaptic spiking. The resulting sensitivity of synaptic plasticity on input-spike-timing can be influenced by the distance between involved spines as well as the local diffusion properties of the connecting dendritic shaft, providing a new way of dendritic computation.

neuroscience↗

bioRxiv2024

Dynamic control of neural manifolds

In the central nervous system, sequences of neural activity form trajectories on low dimensional neural manifolds. The neural computation underlying flexible cognition and behavior relies on dynamic control of these structures. For example different tasks or behaviors are represented on different subspaces, requiring fast timescale subspace rotation to move from one behavior to the next. For flexibility in a particular behavior, the neural trajectory must be dynamically controllable within that behaviorally determined subspace. To understand how dynamic control of neural trajectories and their underlying subspaces may be implemented in neural circuits, we first characterized the relationship between features of neural activity sequences and aspects of the low dimensional projection. Based on this, we propose neural mechanisms that can act within local circuits to modulate activity sequences thereby controlling neural trajectories in low dimensional subspaces. In particular, we show that gain modulation and transient synaptic currents control the speed and path of neural trajectories and clustered inhibition determines manifold orientation. Together, these neural mechanisms may enable a substrate for fast timescale computation on neural manifolds.

neuroscience↗

bioRxiv2024

A model for cortical activity sequences

Networks of neurons in the brain, that act on a timescale of milliseconds, can intrinsically generate reliable sequential activity on slow behavioral timescales of seconds. A possible mechanism for intrinsic sequence generation based on theoretical evidence points to distance-dependent connectivity with correlated spatial asymmetries, establishing an anisotropic network connectivity. We show that networks with such correlated asymmetric connectivity as well as symmetric distance-dependent connectivity match experimental data of connectivity motifs as well as neuronal activity statistics from rat and monkey cortex. At the network level, however, only the correlated asymmetric connectivity pattern generates spatiotemporal activity sequences on behaviorally relevant timescales, while the symmetric connectivity results in transient but stationary spatial bumps of neural activity. Our results strongly support the role of correlated asymmetries in connectivity for the generation of sequential activity in neural networks.

neuroscience↗

bioRxiv2023

Sparse clustered inhibition projects sequential activity onto unique neural subspaces

Neural activity in the brain traces sequential trajectories on low dimensional subspaces. For flexible behavior, these neural subspaces must be manipulated and reoriented within short timescales of tens of milliseconds. Using mathematical analysis and simulation of a recurrently connected neural circuit for sequence generation, we report that incorporating a subtype of interneurons that provides spatially heterogeneous inhibition enables the projection of sequential activity onto task- or context-specific neural subspaces. Depending on the sparsity of inhibitory projections, neural subspaces could be arbitrarily rotated, without altering the key aspects of sequence generation. Thus, we propose a circuit motif by which inhibitory interneurons can enable flexible switching between neural subspaces on a fast timescale of milliseconds, controlled by top down signals.

neuroscience↗

bioRxiv2023

Unsupervised Learning of Perceptual Feature Combinations

In many situations it is behaviorally relevant for an animal to respond to co-occurrences of perceptual, possibly polymodal features, while these features alone may have no importance. Thus, it is crucial for animals to learn such feature combinations in spite of the fact that they may occur with variable intensity and occurrence frequency. Here, we present a novel unsupervised learning mechanism that is largely independent of these contingencies and allows neurons in a network to achieve specificity for different feature combinations. This is achieved by a novel correlation-based (Hebbian) learning rule which allows for linear weight growth and which is combined with a mechanism for gradually reducing the learning rate as soon as the neurons response becomes feature combination specific. In a set of control experiments, we show that other existing advanced learning rules cannot satisfactorily form such multi-feature representations. In addition, we show that networks, which use this type of learning always stabilize and converge to subsets of neurons with different feature-combination specificity. Neurons with this property may, thus, serve as an initial stage for the processing of ecologically relevant real world situations for an animal.

neuroscience↗

bioRxiv2022

Synapses learn to utilize pre-synaptic noise for the prediction of postsynaptic dynamics

Synapses in the brain are highly noisy, which leads to a large trial-by-trial variability. Given how costly synapses are in terms of energy consumption these high levels of noise are surprising. Here we propose that synapses use their noise to represent uncertainties about the activity of the post-synaptic neuron. To show this we utilize the free-energy principle (FEP), a well-established theoretical framework to describe the ability of organisms to self-organize and survive in uncertain environments. This principle provides insights on multiple scales, from high-level behavioral functions such as attention or foraging, to the dynamics of single microcircuits in the brain, suggesting that the FEP can be used to describe all levels of brain function. The synapse-centric account of the FEP that is pursued here, suggests that synapses form an internal model of the somatic membrane dynamics, being updated by a synaptic learning rule that resembles experimentally well-established LTP/LTD mechanisms. This approach entails that a synapse utilizes noisy processes like stochastic synaptic release to also encode its uncertainty about the state of the somatic potential. Although each synapse strives for predicting the somatic dynamics of its neuron, we show that the emergent dynamics of many synapses in a neuronal network resolve different learning problems such as pattern classification or closed-loop control in a dynamic environment. Hereby, synapses coordinate their noise processes to represent and utilize uncertainties on the network level in behaviorally ambiguous situations.

neuroscience↗

bioRxiv2022

Differential Hebbian learning with time-continuous signals for active noise reduction

Spike timing-dependent plasticity, related to differential Hebb-rules, has become a leading paradigm in neuronal learning, because weights can grow or shrink depending on the timing of pre- and post-synaptic signals. Here we use this paradigm to reduce unwanted (acoustic) noise. Our system relies on heterosynaptic differential Hebbian learning and we show that it can efficiently eliminate noise by up to -140 dB in multi-microphone setups under various conditions. The system quickly learns, most often within a few seconds, and it is robust with respect to different geometrical microphone configurations, too. Hence, this theoretical study demonstrates that it is possible to successfully transfer differential Hebbian learning, derived from the neurosciences, into a technical domain.

neuroscience↗

bioRxiv2021

Organization and priming of long-term memory representations with two-phase plasticity

Synaptic tagging and capture (STC) is a molecular mechanism that accounts for the consolidation of synaptic changes induced by plasticity. To link this mechanism to long-term memory and thereby to the level of behavior, its dynamics on the level of recurrent networks have to be understood. To this end, we employ a biologically detailed neural network model of spiking neurons featuring STC, which models the learning and consolidation of long-term memory representations. Using this model, we investigate the effects of different organizational paradigms of multiple memory representations, and demonstrate a proof of principle for priming on long timescales. We examine these effects considering the spontaneous activation of memory representations as the network is driven by background noise. Our first finding is that the order in which the memory representations are learned significantly biases the likelihood of spontaneous activation towards more recently learned memory representations. Secondly, we find that hub-like structures counter this learning order effect for representations with less overlaps. We show that long-term depression is the mechanism underlying these findings, and that intermediate consolidation in between learning the individual representations strongly alters the described effects. Finally, we employ STC to demonstrate the priming of a long-term memory representation on a timescale of minutes to hours. As shown by these findings, our model provides a mechanistic synaptic and neuronal basis for known behavioral effects.

neuroscience↗

bioRxiv2020

The biophysical basis underlying the maintenance of early phase long-term potentiation

The maintenance of synaptic changes resulting from long-term potentiation (LTP) is essential for brain function such as memory and learning. Different LTP phases have been associated with diverse molecular processes and pathways, and the molecular underpinnings of LTP on the short, as well as long time scales, are well established. However, the principles on the intermediate time scale of 1-6 hours that mediate the early phase of LTP (E-LTP) remain elusive. We hypothesize that the interplay between specific features of postsynaptic receptor trafficking is responsible for sustaining synaptic changes during this LTP phase. We test this hypothesis by formalizing a biophysical model that integrates several experimentally-motivated mechanisms. The model captures a wide range of experimental findings and predicts that synaptic changes are preserved for hours when the receptor dynamics are shaped by the interplay of structural changes of the spine in conjunction with increased trafficking from recycling endosomes and the cooperative binding of receptors. Furthermore, our model provides several predictions to verify our findings experimentally. Author summaryThe cognitive ability of learning is associated with plasticity-induced changes in synaptic transmission efficacy mediated by AMPA receptors. Synaptic changes depend on a multitude of molecular and physiological mechanisms, building complex interaction networks. By formalizing and employing a biophysical model of AMPAR trafficking, we unravel and evaluate the interplay between key mechanisms such as receptor binding, exocytosis, morphological changes, and cooperative receptor binding. Our findings indicate that cooperative receptor binding in conjunction with morphological changes of the spine and increased trafficking from recycling endosomes leads to the maintenance of synaptic changes on behaviorally relevant time spans. Characterizing the principles underlying synaptic changes will provide insight into the role of synaptic dynamics in neurodegenerative diseases.

neuroscience↗

bioRxiv2020

Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks

The synaptic-tagging-and-capture (STC) hypothesis formulates that at each synapse the concurrence of a tag with protein synthesis yields the maintenance of changes induced by synaptic plasticity. This hypothesis provides a biological principle underlying the synaptic consolidation of memories that is not verified for recurrent neural circuits. We developed a theoretical model integrating the mechanisms underlying the STC hypothesis with calcium-based synaptic plasticity in a recurrent spiking neural network. In the model, calcium-based synaptic plasticity yields the formation of strongly interconnected cell assemblies encoding memories, followed by consolidation through the STC mechanisms. Furthermore, we find that the STC mechanisms have an up to now undiscovered effect on memories - with the passage of time they modify the storage of memories, such that after several hours memory recall is significantly improved. This kind of memory enhancement can provide a new principle for storing information in biological and artificial neural circuits.

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↗

bioRxiv2019

Hey, look over there: Distraction effects on rapid sequence recall

In the course of everyday life, the brain must store and recall a huge variety of representations of stimuli which are presented in an ordered or sequential way. The processes by which the ordering of these various things is stored and recalled are moderately well understood. We use here a computational model of a cortex-like recurrent neural network adapted by a multitude of plasticity mechanisms. We first demonstrate the learning of a sequence. Then, we examine the influence of different types of distractors on the network dynamics during the recall of the encoded ordered information being ordered in a sequence. We are able to broadly arrive at two distinct effect-categories for distractors, arrive at a basic understanding of why this is so, and predict what distractors will fall into each category.

neuroscience↗