Serrano Pedraza, IgnacioLuna, RaúlBertalmío, Marcelo2025-10-102025-10-102025-03-17Luna, R., Serrano Pedraza, I. & Bertalmío, M. Overcoming the limitations of motion sensor models by considering dendritic computations. Scientific Reports 15, 9213 (2025). https://doi.org/10.1038/s41598-025-90095-z10.1038/s41598-025-90095-zhttps://hdl.handle.net/20.500.14352/124788Raúl Luna was supported by a Juan de la Cierva-Formación fellowship (FJC2020-044084-I) funded by Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación (Spain) and by the European Union NextGenerationEU/PRTR. Ignacio Serrano Pedraza was supported by grant PID2021-122245NB-I00, from Ministerio de Ciencia e Innovación (Spain). Marcelo Bertalmío was supported by project VIS4NN, Programa Fundamentos 2022, Fundación BBVA, and by grant PID2021-127373NB-I00, Ministerio de Ciencia e Innovación (Spain).The estimation of motion is an essential process for any sighted animal. Computational models of motion sensors have a long and successful history but they still suffer from basic shortcomings, as they disagree with physiological evidence and each model is dedicated to a specific type of motion, which is controversial from a biological standpoint. In this work, we propose a new approach to modeling motion sensors that considers dendritic computations, a key aspect for predicting single-neuron responses that had previously been absent from motion models. We show how, by taking into account the dynamic and input-dependent nature of dendritic nonlinearities, our motion sensor model is able to overcome the fundamental limitations of standard approaches.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/journal articlehttps://doi.org/10.1038/s41598-025-90095-z40097493open accessMotion sensorsMotion perceptionDendritic computationsPercepción6106.09 Procesos de Percepción