Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

A dynamical systems view of neuroethology: uncovering stateful computation in natural behaviors

MPG-Autoren
/persons/resource/persons241750

Robson,  DN
Research Group Systems Neuroscience & Neuroengineering, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons241746

Li,  JM
Research Group Systems Neuroscience & Neuroengineering, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Robson, D., & Li, J. (2022). A dynamical systems view of neuroethology: uncovering stateful computation in natural behaviors. Current Opinion in Neurobiology, 73: 102517. doi:10.1016/j.conb.2022.01.002.


Zitierlink: https://hdl.handle.net/21.11116/0000-0009-A552-0
Zusammenfassung
State-dependent computation is key to cognition in both biological and artificial systems. Alan Turing recognized the power of stateful computation when he created the Turing machine with theoretically infinite computational capacity in 1936. Independently, by 1950, ethologists such as Tinbergen and Lorenz also began to implicitly embed rudimentary forms of state-dependent computation to create qualitative models of internal drives and naturally occurring animal behaviors. Here, we reformulate core ethological concepts in explicitly dynamical systems terms for stateful computation. We examine, based on a wealth of recent neural data collected during complex innate behaviors across species, the neural dynamics that determine the temporal structure of internal states. We will also discuss the degree to which the brain can be hierarchically partitioned into nested dynamical systems and the need for a multi-dimensional state-space model of the neuromodulatory system that underlies motivational and affective states.