Published in bioRxiv, 2026
We show that random connectivity in neural networks preserves representational similarity despite ongoing activity drift.
Recommended citation: J-B Eppler, S Galella, G C Mel, & A Roxin. (2026). "Random network structure stabilizes neural manifolds." bioRxiv. https://doi.org/10.64898/2026.05.21.726949
Published in Creativity Research Journal, 2026
We leverage Marr’s levels of analysis to delineate potential circuit mechanisms underlying creativity and suggest testable hypotheses for creative ideation.
Recommended citation: JP-H Seiler, J-B Eppler, O Dan, J Elpelt, M Kaschube, & S Rumpel. (2026). "Towards Circuit Mechanisms of the Creative Process." Creativity Research Journal, 0(0), 1–20. https://doi.org/10.1080/10400419.2026.2646676
Published in Proceedings of the National Academy of Sciences, 2026
We show that representational drift results from an interplay between Hebbian plasticity and stochastic synaptic changes.
Recommended citation: J-B Eppler, T Lai, DF Aschauer, S Rumpel, & M Kaschube. (2026). "Representational drift reflects ongoing balancing of stochastic changes by Hebbian learning." Proceedings of the National Academy of Sciences, 123(5), e2503046123. https://doi.org/10.1073/pnas.2503046123
Published in Nature Neuroscience, 2025
We show how homeostatic mechanisms in auditory cortex protect representational maps after neuron loss, with recovery driven by plasticity in initially unresponsive neurons.
Recommended citation: T Noda, E Kienle, J-B Eppler, DF Aschauer, M Kaschube, Y Loewenstein, & S Rumpel. (2025). "Homeostasis of a representational map in the neocortex." Nature Neuroscience, 28, 1533–1545. https://doi.org/10.1038/s41593-025-01982-7
Published in Current Opinion in Neurobiology, 2025
We propose that statistical learning maintains stable representational similarities despite continuous neuronal drift, providing a dynamic substrate for memory formation and stability.
Recommended citation: J-B Eppler, M Kaschube, & S Rumpel. (2025). "Statistical learning and representational drift: A dynamic substrate for memories." Current Opinion in Neurobiology, 94, 103107. https://doi.org/10.1016/j.conb.2025.103107
Published in Scientific Reports, 2023
We developed an efficient deep learning pipeline for automated dendritic spine detection in volumetric 2-photon imaging data.
Recommended citation: FW Vogel, S Alipek, J-B Eppler, P Osuna-Vargas, J Triesch, D Bissen, A Acker-Palmer, S Rumpel, & M Kaschube. (2023). "Utilizing 2D-region-based CNNs for automatic dendritic spine detection in 3D live cell imaging." Scientific Reports, 13, 20497. https://doi.org/10.1038/s41598-023-47070-3
Published in Cerebral Cortex, 2023
Despite stability in topographic maps, individual neurons undergo substantial reformatting. We show sensory maps are maintained by drifting subpopulations of neurons.
Recommended citation: AR Chambers, DF Aschauer, J-B Eppler, M Kaschube, & S Rumpel. (2023). "A stable sensory map emerges from a dynamic equilibrium of neurons with unstable tuning properties." Cerebral Cortex, 33(9), 5597–5612. https://doi.org/10.1093/cercor/bhac445
Published in Cell Reports, 2022
We show that learning-induced plasticity in representations of sound stimuli in mouse auditory cortex weaves into ongoing representational drift.
Recommended citation: DF Aschauer, J-B Eppler, L Ewig, AR Chambers, C Pokorny, M Kaschube, & S Rumpel. (2022). "Learning-induced biases in the ongoing dynamics of sensory representations predict stimulus generalization." Cell Reports, 38(6), 110340. https://doi.org/10.1016/j.celrep.2022.110340
Published in Johann Wolfgang Goethe-Universität Frankfurt am Main, 2022
PhD thesis on representational drift in neocortical populations, investigating mechanisms through both experimental data and computational modeling.
Recommended citation: J-B Eppler. (2022). "Ongoing neuronal population activity dynamics in the neocortex - representational drift in experiment and model." PhD Thesis, Johann Wolfgang Goethe-Universität Frankfurt am Main. https://doi.org/10.21248/gups.69221