Biology subjects

Eppler, J.-B.

Publications and source records attributed to Eppler, J.-B..

5 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↗

bioRxiv2026

Random network structure stabilizes neural manifolds

Neuronal activity patterns change continuously over days and weeks, a phenomenon known as representational drift. Despite this, the geometric structure of population representations, namely the pairwise similarities between stimulus-evoked activity patterns, remains remarkably stable. How can ongoing changes in activity be consistent with stable representational similarity? We show that this is a generic consequence of random connectivity: in networks with random connectivity, output similarity is a monotonically increasing function of input similarity, independent of the specific connectivity pattern. Drift, whether driven by random synaptic turnover or Hebbian plasticity, merely transitions the network between random instantiations, leaving similarity intact. This extends to recurrent architectures and to deep neural networks, where continued training beyond performance saturation produces activity drift while preserving representational similarity. Although connectivity in the brain is not random, networks trained on high-dimensional inputs acquire connectivity that behaves statistically like a random projection, making these results broadly applicable to biological neural circuits.

neuroscience↗

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↗

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↗