Publikationen

Home >> Forschung >> Publikationen


Impact Factor


Juni 2025
Cardiovascular Research
Mason FE, Liutkute A, Voigt N
Juni 2025
Nucleic Acids Research
Gather F, Rauleac T, Akol I, Arumugam G, Fullio CL, Müller T, Kleidonas D, Geiss-Friedlander R, Fischer A, Vlachos A, Backofen R, Vogel T
Juni 2025
Science Advances
Karagulyan N, Thirumalai A, Michanski S, Qi Y, Fang Q, Wang H, Ortner NJ, Striessnig J, Strenzke N, Wichmann C, Hua Y, Moser T
Juni 2025
BioRxiv
Hamann TE, Wieland A, Tirincsi A, Vukusic K, Mohseni F, Wardenaar R, Losito M, Goenenc II, Wollnik B, Foijer F, Tolic IM, Storchova Z, Raschle M
Juni 2025
Non-coding RNA
Gisa V, Islam MR, Lbik D, Hofmann RM, Pena T, Krüger DM, Burkhardt S, Schütz AL, Sananbenesi F, Toischer K, Fischer A
Juni 2025
Cell Reports
Harris SS, Rajani RM, Zünkler J, Ellingford R, Yang M, Rowland JM, Schmidt A, Lee BI, Kehring M, Hellmuth M, Lam FKW, Fässler D, Erdinger S, Wolfer DP, Sala Frigerio C, Wolf F, Hyman BT, Müller UC, Busche MA
Juni 2025
Brain, Behavior, and Immunity
Solomon P, Budde M, Kohshour MO, Adorja K, Heilbronner M, Navarro-Flores A, Papiol S, Reich-Erkelenz D, Schulte EC, Senner F, Vogl T, Kaurani L, Krüger DM, Sananbenesi F, Pena T, Burkhardt S, Schütz AL, Anghelescu IG, Arolt V, Baune BT, Dannlowski U, Dietrich DE, Fallgatter AJ, Figge C, Juckel G, Konrad C, Lang FU, Reimer J, Reininghaus EZ, Schmauß M, Spitzer C, Wiltfang J, Zimmermann J, Fischer A, Falkai P, Schulze TG, Heilbronner U, Poschmann J
Juni 2025
BioRxiv
Chandran A, Agarwal A, Wang T, Amaral L, Chaves SR, Outeiro TF, Lautenschlager J
Juni 2025
BioRxiv
Pradhan R, Petrovic Z, Sakib MS, Schroeder S, Krueger DM, Pena T, Diniz E, Burkhardt S, Schuetz AL, Grządzielewska I, Toischer K, Stein TD, Blusztajn JK, Delalle I, Radulovic J, Sananbenesi F, Fischer A
Juni 2025
Arxiv
Nellen NS, Turishcheva P, Vystrčilová M, Sridhar S, Gollisch T, Tolias AS, Ecker AS

Authors

Nellen NS, Turishcheva P, Vystrčilová M, Sridhar S, Gollisch T, Tolias AS, Ecker AS

Journal

Arxiv

Citation

arXiv:2506.03293.

Abstract

Deep neural networks trained to predict neural activity from visual input and behaviour have shown great potential to serve as digital twins of the visual cortex. Per-neuron embeddings derived from these models could potentially be used to map the functional landscape or identify cell types. However, state-of-the-art predictive models of mouse V1 do not generate functional embeddings that exhibit clear clustering patterns which would correspond to cell types. This raises the question whether the lack of clustered structure is due to limitations of current models or a true feature of the functional organization of mouse V1. In this work, we introduce DECEMber — Deep Embedding Clustering via Expectation Maximization-based refinement — an explicit inductive bias into predictive models that enhances clustering by adding an auxiliary t-distribution-inspired loss function that enforces structured organization among per-neuron embeddings. We jointly optimize both neuronal feature embeddings and clustering parameters, updating cluster centers and scale matrices using the EM-algorithm. We demonstrate that these modifications improve cluster consistency while preserving high predictive performance and surpassing standard clustering methods in terms of stability. Moreover, DECEMber generalizes well across species (mice, primates) and visual areas (retina, V1, V4).

DOI

10.48550/arXiv.2506.03293

X
EN DE
X
X