Universität zu Köln

Mueller, Jurek, Nawrot, Martin, Menzel, Randolf and Landgraf, Tim ORCID: 0000-0003-4951-5235 (2018). A neural network model for familiarity and context learning during honeybee foraging flights. Biol. Cybern., 112 (1-2). S. 113 - 127. NEW YORK: SPRINGER. ISSN 1432-0770

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Abstract

How complex is the memory structure that honeybees use to navigate? Recently, an insect-inspired parsimonious spiking neural network model was proposed that enabled simulated ground-moving agents to follow learned routes. We adapted this model to flying insects and evaluate the route following performance in three different worlds with gradually decreasing object density. In addition, we propose an extension to the model to enable the model to associate sensory input with a behavioral context, such as foraging or homing. The spiking neural network model makes use of a sparse stimulus representation in the mushroom body and reward-based synaptic plasticity at its output synapses. In our experiments, simulated bees were able to navigate correctly even when panoramic cues were missing. The context extension we propose enabled agents to successfully discriminate partly overlapping routes. The structure of the visual environment, however, crucially determines the success rate. We find that the model fails more often in visually rich environments due to the overlap of features represented by the Kenyon cell layer. Reducing the landmark density improves the agents route following performance. In very sparse environments, we find that extended landmarks, such as roads or field edges, may help the agent stay on its route, but often act as strong distractors yielding poor route following performance. We conclude that the presented model is valid for simple route following tasks and may represent one component of insect navigation. Additional components might still be necessary for guidance and action selection while navigating along different memorized routes in complex natural environments.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Mueller, Jurek UNSPECIFIED UNSPECIFIED UNSPECIFIED Nawrot, Martin UNSPECIFIED UNSPECIFIED UNSPECIFIED Menzel, Randolf UNSPECIFIED UNSPECIFIED UNSPECIFIED Landgraf, Tim UNSPECIFIED orcid.org/0000-0003-4951-5235 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-190849
DOI:	10.1007/s00422-017-0732-z
Journal or Publication Title:	Biol. Cybern.
Volume:	112
Number:	1-2
Page Range:	S. 113 - 127
Date:	2018
Publisher:	SPRINGER
Place of Publication:	NEW YORK
ISSN:	1432-0770
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language BODY-EXTRINSIC NEURONS; MUSHROOM BODY; OLFACTORY SYSTEM; PLASTICITY; SPARSE; REPRESENTATIONS; DYNAMICS; ARCHITECTURE; RECOGNITION; ORIENTATION Multiple languages Computer Science, Cybernetics; Neurosciences Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/19084