Multicompartment simulations of NMDA receptor based facilitation in an insect target tracking neuron

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding


Computational modelling of neurons on different scales provides not only methods to explore mechanisms observed in vivo but also for testing hypotheses that would be impossible physiologically. In this paper we present initial computational analysis of insect lobula small target motion detector (STMD) neurons. We simulate a multicompartment model in combination with a bioinspired model for front-end processing. This combination of different simulation environments enables a combination of scale and detail not possible otherwise. The addressed hypothesis is that facilitation involves N-methyl-D-aspartate (NMDA) synapses which map retinotopically onto the dendritic tree of the STMD neuron. Our results show that a stronger response (facilitation) is generated when using continuous visual stimuli as opposed to random jumps. We observe two levels of facilitation which may be involved in selective attention.


External organisations
  • University of Adelaide
  • San Diego State University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Bioinformatics (Computational Biology)


  • Bioinspired, Computational neuroscience, Facilitation, Model, Multicompartment, Selective attention, Simulation, Small target motion detection
Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings
Number of pages8
Volume10613 LNCS
ISBN (Print)9783319685991
Publication statusPublished - 2017
Publication categoryResearch
Event26th International Conference on Artificial Neural Networks, ICANN 2017 - Alghero, Italy
Duration: 2017 Sep 112017 Sep 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10613 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference26th International Conference on Artificial Neural Networks, ICANN 2017