Tonic dopamine effects in a bio-inspired robot controller enhance expected lifetime

Cristobal J. Nettle, Maria Jose Escobar

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

© 2016 IEEE. The present work extends a two-level decision-making mechanism, modeling the cortico-basal ganglia (CBG) loop. It incorporates an exploration-exploitation control based on D1-type tonic dopamine (DA) effects in the corticostriatal synapses. The resulting model not only supports the previous findings reinforcing the feasibility of controlling the use of past information against exploring new options just by varying the level of D1-type tonic DA, but also shows how such control can increase lifetime while confronting a simple survival task. A MODI (MODular Intelligence) robotic platform is tested performing a standard survival task, proposing a robotics controller that integrates the CBG model as its action selection mechanism. The MODI robot has to deal with a two-resources survival problem, learning on-line which actions most likely offer reward for the agent, for a given time, in order to maximally extend lifetime. The obtained data shows relations between time survived and the level of D1-type tonic DA of the robot.
Original languageEnglish
Pages230-237
Number of pages8
DOIs
Publication statusPublished - 7 Feb 2017
Event2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL-EpiRob 2016 -
Duration: 7 Feb 2017 → …

Conference

Conference2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL-EpiRob 2016
Period7/02/17 → …

Fingerprint Dive into the research topics of 'Tonic dopamine effects in a bio-inspired robot controller enhance expected lifetime'. Together they form a unique fingerprint.

  • Cite this

    Nettle, C. J., & Escobar, M. J. (2017). Tonic dopamine effects in a bio-inspired robot controller enhance expected lifetime. 230-237. Paper presented at 2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL-EpiRob 2016, . https://doi.org/10.1109/DEVLRN.2016.7846824