Biology subjects

Rumpel, S.

Publications and source records attributed to Rumpel, S..

8 recordsLinked to original sources

bioRxiv2026

Boredom and the representation of information content in the neocortex

Boredom - a pervasive mental state - promotes the pursuit of novel information by assigning negative value to monotonous conditions. Yet, how the brain extracts and represents the information content of ongoing sensory experience remains poorly understood. Here, we combine behavioral assays, neurophysiological recordings and computational modeling across humans and mice to investigate how sensory information shapes boredom-related behavior. In a cross-species choice task, both humans and mice robustly avoid monotonous sources of sensory stimulation. We formalize perceived monotony using empirical entropy as a measure of information content and show that monotony avoidance scales directly with low entropy and in humans correlates with boredom experience. Human electroencephalography and mesoscopic calcium imaging in mice reveal that the recruitment of neocortical activity tracks stimulus entropy. Two-photon calcium imaging in the auditory cortex of mice further uncovers a stimulus-invariant population code for entropy, supported by neurons tuned to information content. A recurrent network model reproduced this code through an interplay of afferent depression and recurrent facilitation. Together, we demonstrate how the information content of sensory experience is represented in cortical population activity, providing a basis for boredom-related avoidance behavior. Thus, our findings link synaptic and neuronal dynamics to boredom, acting as a safeguard mechanism to ensure high information input to the brain.

neuroscience↗

bioRxiv2025

STARR-CRAAVT: A platform to design cell type-specific regulatory elements for next-generation gene therapy

Precise control of transgene expression through novel enhancer-promoter combinations is a promising strategy for advancing gene therapy mediated by adeno-associated virus (AAV). We present STARR-CRAAVT, a novel STARR-Seq-derived platform to screen for enhancers in the AAV context. Using HepG2 and HaCaT cells as screening models, we applied STARR-CRAAVT for the identification of cell type-specific enhancers. We integrated epigenetic datasets into an in-silico library of putative HepG2 enhancers and captured corresponding fragments from genomic DNA. The fragments were processed into AAV libraries and applied to HepG2 and HaCaT cells. STARR-CRAAVT analysis revealed a selective activity of the libraries confirming the HepG2-directed in-silico design and the specificity of single enhancer-promoter combinations could be validated using luciferase reporter assays. In addition, we scrutinized the impact of key experimental parameters on enhancer identification and found that the used promoter type had significant influence on the ability of candidates to act as enhancers. Furthermore, switching the location of identified enhancers in reporter assays revealed that the level of enhancer activity is highly dependent on the position in the AAV genome. Taken together, our study yields novel insights into enhancer function and demonstrates that STARR-CRAAVT can be employed to identify cell type-specific enhancers, highlighting promoter preference and enhancer positioning as key considerations for enhancer screening campaigns.

genomics↗

bioRxiv2025

Representational drift reflects ongoing balancing of stochastic changes by Hebbian learning

Recent evidence indicates that even under stable environmental and behavioural conditions responses to sensory stimuli undergo continuous reformatting over the course of days, a condition described as representational drift. However, the processes underlying this phenomenon remain poorly understood. Examining the dynamics of signal and noise correlations among neuron pairs in chronic calcium imaging experiments in the mouse auditory cortex, we investigate how activity-dependent, Hebbian-like plasticity, and activity-independent, stochastic synaptic processes contribute to representational drift. We found that signal correlations predict future noise correlations, suggesting that stimulus-induced co-activation leads to increased effective connectivity between neuron pairs. Moreover, simple linear network models were able to account for the observed temporal dependencies between signal and noise correlations, but only if both Hebbian-like plasticity and stochastic changes of either inputs or recurrent synapses contribute to representational drift. In conclusion, our findings suggest that continuous sensory input-driven Hebbian-like plasticity can balance ongoing stochastic synaptic changes, thereby preventing the networks functional degradation.

neuroscience↗

bioRxiv2024

Revealing Acute Consequences of Rapid Protein Elimination at Individual Synapses using Auxin-Inducible Degron 2 Technology

A powerful approach to assess a protein of interest (POI) function is its specific elimination. Common knock-out and knock-down strategies, however, are protracted and often irreversible, challenging the assessment of acute or temporary consequences in the same cells and tissues. Here we describe the use of Auxin-Inducible Degron 2 (AID2) technology to study the real-time consequences of acute POI elimination in nerve cell synapses. We demonstrate its capacity in cultured neurons and in vivo to rapidly eliminate postsynaptic scaffold proteins fused at N-terminal, C-terminal, or nested sites to GFP derivatives or HaloTag. We show that acute PSD-95 or gephyrin elimination leads to the concomitant loss of AMPA or GABAA receptors at the same synapses, and that, surprisingly, acute GKAP, but not PSD-95 elimination reduces postsynaptic scaffold size. Our findings highlight the utility of AID2 technology for rapidly eliminating synaptic POIs and studying real-time consequences in the same neurons and synapses.

neuroscience↗

bioRxiv2023

Homeostasis of a representational map in the neocortex

Cortical function in general and the processing of sensory stimuli in particular are remarkably robust against the continuous loss of neurons during aging, and even the accelerated loss during prodromal stages of neurodegeneration1,2. Population activity of neurons in sensory cortices represents the environment in form of a map, which is structured in an informative way for guiding behavior. Here, we used the mouse auditory cortex as a model and tested in how far the structure of the representational map is protected by homeostatic network mechanisms against the removal of neurons. We combined longitudinal two-photon calcium imaging of population responses evoked by a diverse set of sound stimuli with a targeted microablation of functionally characterized neurons. Unilateral microablation of 30 - 40 selected highly sound-responsive neurons in layer 2/3 led to a temporary disturbance of the representational map in the spared population that, however, recovered in subsequent days. At the level of individual neurons, we observed that the recovery of the spared network was predominantly driven by neurons unresponsive to the sounds before microablation which strengthened the correlation structure of the local network after gaining responsiveness. In contrast, selective microablation of inhibitory neurons induced a prolonged disturbance of the representational map that was primarily characterized by a destabilization of sound responses across trials. Together, our findings provide a link between the tuning and plasticity of individual neurons and the structure of a representational map at the population level which reveals homeostatic network mechanisms safeguarding sensory processing in neocortical circuits.

neuroscience↗

bioRxiv2023

Fully automated detection of dendritic spines in 3D live cell imaging data using deep convolutional neural networks

Dendritic spines are considered a morphological proxy for excitatory synapses, rendering them a target of many different lines of research. Over recent years, it has become possible to image simultaneously large numbers of dendritic spines in 3D volumes of neural tissue. In contrast, currently no automated method for spine detection exists that comes close to the detection performance reached by human experts. However, exploiting such datasets requires new tools for the fully automated detection and analysis of large numbers of spines. Here, we developed an efficient analysis pipeline to detect large numbers of dendritic spines in volumetric fluorescence imaging data. The core of our pipeline is a deep convolutional neural network, which was pretrained on a general-purpose image library, and then optimized on the spine detection task. This transfer learning approach is data efficient while achieving a high detection precision. To train and validate the model we generated a labelled dataset using five human expert annotators to account for the variability in human spine detection. The pipeline enables fully automated dendritic spine detection and reaches a near human-level detection performance. Our method for spine detection is fast, accurate and robust, and thus well suited for large-scale datasets with thousands of spines. The code is easily applicable to new datasets, achieving high detection performance, even without any retraining or adjustment of model parameters.

neuroscience↗

bioRxiv2020

Rapid nucleus-scale reorganization of chromatin in neurons enables transcriptional adaptation for memory consolidation

The interphase nucleus is functionally organized in active and repressed territories defining the transcriptional status of the cell. However, it remains poorly understood how the nuclear architecture of neurons adapts in response to behaviorally relevant stimuli that trigger fast alterations in gene expression patterns. Imaging of fluorescently tagged nucleosomes revealed that pharmacological manipulation of neuronal activity in vitro and auditory cued fear conditioning in vivo induce nucleus-scale restructuring of chromatin within minutes. Furthermore, the acquisition of auditory fear memory is impaired after infusion of a drug into auditory cortex which blocks chromatin reorganization in vitro. We propose that active chromatin movements at the nucleus scale act together with local gene-specific modifications to enable transcriptional adaptations at fast time scales. Introducing a transgenic mouse line for photolabeling of histones, we extend the realm of systems available for imaging of chromatin dynamics to living animals.

neuroscience↗

bioRxiv2018

Cortical recruitment determines learning dynamics and strategy

Salience is a broad and widely used concept in neuroscience whose neuronal correlates, however, remain elusive. In behavioral conditioning, salience is used to explain various effects, such as stimulus overshadowing, and refers to how fast and strongly a stimulus can be associated with a conditioned event. Here, we show that sounds of diverse quality, but equal intensity and perceptual detectability, can recruit different levels of population activity in mouse auditory cortex. When using these sounds as cues in a Go/NoGo discrimination task, the degree of cortical recruitment matches the salience parameter of a reinforcement learning model used to analyze learning speed. We test an essential prediction of this model by training mice to discriminate light-sculpted optogenetic activity patterns in auditory cortex, and verify that cortical recruitment causally determines association or overshadowing of the stimulus components. This demonstrates that cortical recruitment underlies major aspects of stimulus salience during reinforcement learning.

neuroscience↗