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<p>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.</p>

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Overcoming the limitations of motion sensor models by considering dendritic computations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月17日

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  • Raúl Luna,
  • Ignacio Serrano-Pedraza,
  • Marcelo Bertalmío

摘要

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.