RT Journal Article
T1 Overcoming the limitations of motion sensor models by considering dendritic computations
A1 Serrano Pedraza, Ignacio
A1 Luna, Raúl
A1 Bertalmío, Marcelo
AB 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.
PB Nature Portfolio
YR 2025
FD 2025-03-17
LK https://hdl.handle.net/20.500.14352/124788
UL https://hdl.handle.net/20.500.14352/124788
LA eng
NO Luna, 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-z
NO Raúl 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).
NO Ministerio de Ciencia e Innovación (España)
NO Agencia Estatal de Investigación (España)
NO Unión Europea
NO Fundación BBVA (España)
DS Docta Complutense
RD 19 may 2026