Perception and hierarchical dynamics

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung


  • Stefan J. Kiebel - , Max-Planck-Institut für Kognitions- und Neurowissenschaften (Autor:in)
  • Jean Daunizeau - , University College London (Autor:in)
  • Karl J. Friston - , University College London (Autor:in)


In this paper, we suggest that perception could be modeled by assuming that sensory input is generated by a hierarchy of attractors in a dynamic system. We describe a mathematical model which exploits the temporal structure of rapid sensory dynamics to track the slower trajectories of their underlying causes. This model establishes a proof of concept that slowly changing neuronal states can encode the trajectories of faster sensory signals. We link this hierarchical account to recent developments in the perception of human action; in particular artificial speech recognition. We argue that these hierarchical models of dynamical systems are a plausible starting point to develop robust recognition schemes, because they capture critical temporal dependencies induced by deep hierarchical structure. We conclude by suggesting that a fruitful computational neuroscience approach may emerge from modeling perception as non-autonomous recognition dynamics enslaved by autonomous hierarchical dynamics in the sensorium.


FachzeitschriftFrontiers in neuroinformatics
PublikationsstatusVeröffentlicht - 20 Juli 2009
Extern publiziertJa



  • Bayesian inversion, Biological movement, Birdsong, Dynamic systems theory, Environment, Perception, Recognition, Speech