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Inferring Temporal Structure from Predictability in Bumblebee Learning Flight

  • Stefan MeyerEmail author
  • Olivier J. N. Bertrand
  • Martin Egelhaaf
  • Barbara Hammer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11314)

Abstract

Insects are succeeding in remarkable navigational tasks. Bumblebees, for example, are capable of learning their nest location with sophisticated flight manoeuvres, forming a so-called learning flight. The learning flights - thought to be partially pre-programmed - enable the bumblebee to memorise spatial relations between its inconspicuous nest entrance and environmental cues. To date, environmental features (e.g. object positions on the eyes) and learning experience of the insect were used to describe the flights, but its structure, thought to facilitated learning, has not been investigated systematically. In this work, we present a novel approach, to examine whether and in which time span flight behaviour is predictable based on intrinsic properties only rather than external sensory information. We study the temporal composition of learning flights by estimating the smoothness of the underlying process. We then use echo state networks (ESN) and linear models (ARIMA) to predict the bumblebee trajectory from its past motion and identify different time-scales in learning flights using their prediction-power. We found that direct visual information is not necessary within a 200ms time-window to explain the bumblebee behaviour during its learning flight.

Keywords

Time series prediction Modelling Echo state network Learning flight Bumblebee 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Stefan Meyer
    • 1
    Email author
  • Olivier J. N. Bertrand
    • 1
  • Martin Egelhaaf
    • 1
  • Barbara Hammer
    • 2
  1. 1.Department of NeurobiologyUniversity of BielefeldBielefeldGermany
  2. 2.Department of Machine LearningUniversity of BielefeldBielefeldGermany

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