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Video Experimental Relacionado

Updated: Jan 29, 2026

Measurement of Smooth Muscle Function in the Isolated Tissue Bath-applications to Pharmacology Research
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Inferenica Activa y Parametrización Funcional: Planitud Diferencial y Realización Aleatoria Suave

Hugues Mounier1, Thomas Parr2, Karl Friston3,4

  • 1Laboratoire des Signaux et Systèmes, Université Paris-Saclay, Centre National de la Recherche Scientifique, CentraleSupélec, 3, rue Joliot Curie, 91192 Gif sur Yvette, France.

Entropy (Basel, Switzerland)
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PubMed
Resumen
Este resumen es generado por máquina.

Este estudio vincula la teoría de control no lineal con la inferencia activa, explorando la planitud diferencial para el diseño de modelos generativos. Este enfoque utiliza derivados del sistema y coordenadas generalizadas para aplicaciones de control, demostradas con el control oculomotor.

Palabras clave:
inferencia activaplanitud diferencialformulaciones de trayectoriafunciones aleatorias suaves periódicas

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Área de la Ciencia:

  • Robótica y Sistemas de Control
  • Neurociencia Computacional
  • Aprendizaje Automático

Sus antecedentes:

  • Los modelos de inferencia activa toman decisiones utilizando modelos generativos.
  • La teoría de control no lineal constructiva ofrece métodos de diseño sistemáticos.
  • La vinculación de estos campos es crucial para sistemas autónomos avanzados.

Objetivo del estudio:

  • Explorar la conexión entre la planitud diferencial y los modelos generativos de inferencia activa.
  • Investigar cómo las propiedades de trayectoria de los sistemas diferencialmente planos informan el diseño de control.
  • Aplicar estos conceptos al control oculomotor como caso de estudio.

Principales métodos:

  • Formulación de modelos generativos de tiempo continuo utilizando coordenadas generalizadas.
  • Análisis de propiedades de trayectoria derivadas de derivadas temporales de sistemas diferencialmente planos.
  • Integración de técnicas de control no lineal constructivas dentro del marco de inferencia activa.

Principales resultados:

  • Demostró un marco novedoso que une la teoría de control y la inferencia activa.
  • Destacó la utilidad de la planitud diferencial en el diseño de modelos generativos para el control.
  • Proporcionó una ilustración conceptual utilizando la dinámica del control oculomotor.

Conclusiones:

  • La planitud diferencial ofrece una vía prometedora para diseñar controladores robustos de inferencia activa.
  • La integración facilita el desarrollo de sistemas autónomos más sofisticados.
  • Investigaciones futuras pueden extender este marco a problemas de control más complejos.