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@INPROCEEDINGS{Schoepe:1022172,
author = {Schoepe, Thorben and Chicca, Elisabetta},
title = {{F}inding the {G}oal: {I}nsect-{I}nspired {S}piking
{N}eural {N}etwork for {H}eading {E}rror {E}stimation},
publisher = {IEEE},
reportid = {FZJ-2024-01293},
pages = {4727-4733},
year = {2023},
abstract = {Insects have extraordinary navigational abilities.Monarch
butterflies migrate every year to the same forestover
hundreds of kilometers, desert ants find their way backto
the nest tens of meters away and dung beetles maintainthe
same heading direction over meters. The performance ofthese
agents has been optimized by evolution over the last500
million years leading to power-efficient, low-latency
andprecise sensorimotor systems. Research efforts in the
fieldof neuroscience, biology and robotics are instrumental
foruncovering the neural substrate of insect navigation
abilities.The development of models of insect navigation
tightly coupledwith the insect connectome and
neurophysiology and theirembedding in closed loop systems
support the understanding ofembodied animal cognition and
can advance robotic systems.In this work, we focus on insect
navigation because of theefficient insect navigational
apparatus. Furthermore, the recentdiscovery of the central
complex, the neuronal center of insectnavigation,
facilitates the development of new hypotheses aboutinsect
navigation. All navigating insects need to perform somekind
of goal-directed behavior during which they have toreach a
specific goal location or maintain the same
movementdirection over long distances. Such behavior
requires theagent to be aware of its current heading
direction, desiredheading direction, and the error between
them. Building onprevious research in the field, we propose
a novel model forthis error estimation that can in principle
be generalized forall navigating insect species. We
implement the model in aspiking neural network and test its
capabilities on a simulatedrobotic platform. The precision
of the network is comparableto or even better than the
biological role model. Thus, ourimplementation serves as a
working hypothesis for how theheading error might be
computed in the insect brain. Our modelwill help to explain
navigational behavior in fruit flies, orchidbees, bumble
bees and some less researched insect species.Furthermore,
its simplicity in comparison to other models
andimplementation in a spiking neural network makes it
verysuitable for neuromorphic systems, an emerging field of
braininspired hardware.},
month = {Oct},
date = {2023-10-01},
organization = {2023 IEEE/RSJ International Conference
on Intelligent Robots and Systems
(IROS), Detroit (MI), 1 Oct 2023 - 5
Oct 2023},
cin = {PGI-15},
cid = {I:(DE-Juel1)PGI-15-20210701},
pnm = {5234 - Emerging NC Architectures (POF4-523)},
pid = {G:(DE-HGF)POF4-5234},
typ = {PUB:(DE-HGF)8},
UT = {WOS:001133658803087},
doi = {10.1109/IROS55552.2023.10342210},
url = {https://juser.fz-juelich.de/record/1022172},
}