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September 2025
Med
Fan X, Gao Z, Zhong J, Chen Y, Chen X, Landegger LD, Moser T, Zeng FG, Sun Y, Jin X, Nash R, Chien WW, Jiang D, Greinwald JH, Bance M, Rodriguez MM, Lee SY, Feng G, Yang H, Wu CC, Xu L, Yuan W, Feng Y, Zhao Y, Vona BC, Stenzke N, Beutner D, Amin N, Arwyn-Jones J, Chandrasekeharan D, Shi D, Zhang D, Yang J, Qi J, Wang Q, Yin Y, Cheng YF, Tao Y, Yu Y, Wang D, Jiang L, Guo L, Chen L, Cheng X, Cui C, Lv J, Han S, Wang W, Li Y, Gao X, Liu XZ, Zha D, Shi H, Chen B, Wang Q, Yuan H, Yang S, Yin S, Wu H, Wang Z, Li H, Rubinstein JT, Lustig LR, Chai R, Chen ZY, Shu Y
September 2025
Arxiv
Groppe M, Niemöller L, Hundrieser S, Ventzke D, Blob A, Köster S, Munk A
September 2025
Science Advances
Kapoor R, Kim H, Garlick E, Lima MARBF, Ruhwedel T, Moebius W, Wolf F, Moser T
September 2025
Biological Chemistry
Dahal D, Cruz-Zargoza LD, Rehling P
September 2025
The journal of Physical Chemistry Letters
Chizhik AI, Sakhapov DI, Gregor I, Karedla N, Enderlein J
September 2025
The Journal of Physical Chemistry Letters
Chizhik AI, Sakhapov DI, Gregor I, Karedla N, Enderlein J
September 2025
Science
Siegenthaler D, Denny H, Carrasco SS, Mayer JL, Levenstein D, Peyrache A, Trenholm S, Macé E
September 2025
Nature
Sakthivelu V, Schmitt A, Odenthal F, Ndoci K, Touet M, Shaib AH, Chihab A, Wani GA, Nieper P, Hartmann GG, Pintelon I, Kisis I, Boecker M, Eckert NM, Iannicelli Caiaffa M, Ibruli O, Weber J, Maresch R, Bebber CM, Chitsaz A, Lütz A, Kim Alves Carpinteiro M, Morris KM, Franchino CA, Benz J, Pérez-Revuelta L, Soriano-Campos JA, Huetzen MA, Goergens J, Jevtic M, Jahn-Kelleter HM, Zempel H, Placzek A, Hennrich AA, Conzelmann KK, Tumbrink HL, Hunold P, Isensee J, Werr L, Gaedke F, Schauss A, Minère M, Müller M, Fenselau H, Liu Y, Heimsoeth A, Gülcüler Balta GS, Walczak H, Frezza C, Jachimowicz RD, George J, Schmiel M, Brägelmann J, Hucho T, von Karstedt S, Peifer M, Annibaldi A, Hänsel-Hertsch R, Persigehl T, Grüll H, Sos ML, Reifenberger G, Fischer M, Adriaensen D, Büttner R, Sage J, Brouns I, Rad R, Thomas RK, Anstötz M, Rizzoli SO, Bergami M, Motori E, Reinhardt HC, Beleggia F
September 2025
European Heart Journal
Fakuade FE, Gronwald J, Brandes P, Döring Y, Rubio T, Seibertz F, Knierim M, Abu-Taha IH, El-Essawi A, Jebran AF, Danner BC, Baraki H, Kamler M, Kutschka I, Heijman J, Dobrev D, Schmidt C, Kallenberger SM, Voigt N
September 2025
BioRxiv
Vystrčilová M, Sridhar S, Burg MF, Gollisch T, Ecker AS

Authors

Vystrčilová M, Sridhar S, Burg MF, Gollisch T, Ecker AS

Journal

BioRxiv

Citation

bioRxiv 2025.09.08.674839.

Abstract

Wide-field amacrine cells (ACs) play a unique role in retinal processing by integrating visual information across a large spatial area. Their inhibitory influence has been implicated in multiple retinal functions such as differential motion detection and the suppression of retinal activity during eye movements. However, a coherent understanding of their general function is lacking due to difficulties in directly recording from these cells and identifying effective visual stimuli to activate them. In this study, we used convolutional neural networks (CNNs) to investigate wide-field inhibition mediated by wide-field ACs in the marmoset retina. We trained CNNs to mimic the function of the retina by predicting retinal ganglion cell (RGC) population responses to naturalistic movie stimuli and optimising the most exciting inputs (MEIs) to visualise RGCs’ receptive field (RF) structures. We then optimized suppressive surrounds beyond classical RGC RF boundaries, intended to capture the inhibitory effect of wide-field ACs on RGC activity. These optimized surrounds reduced MEI-elicited activity by 10% to 30%, demonstrating that CNNs not only mimic retinal responses but can also reveal hidden computational aspects of wide-field inhibition. However, suppression strength and generalization varied across architectures and datasets, indicating potential model-specific effects, highlighting the importance of cautious interpretation. Overall, our approach illustrates how interpretability methods applied to artificial neural networks can offer new hypotheses regarding biological retinal computation, paving the way for targeted experimental validation.

DOI

10.1101/2025.09.08.674839

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