Non-linear neuronal responses as an emergent property of afferent networks: a case study of the locust lobula giant movement detector.

In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the loc...

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Autores principales:	Sergi Bermúdez i Badia, Ulysses Bernardet, Paul F M J Verschure
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6dc4a57c362c45bd9907cfda43b63ea6
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spelling	oai:doaj.org-article:6dc4a57c362c45bd9907cfda43b63ea62021-11-25T05:42:36ZNon-linear neuronal responses as an emergent property of afferent networks: a case study of the locust lobula giant movement detector.1553-734X1553-735810.1371/journal.pcbi.1000701https://doaj.org/article/6dc4a57c362c45bd9907cfda43b63ea62010-03-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20300653/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the locust, which is reported to locally perform a functional multiplication. Given the wide ramifications of this suggestion with respect to our understanding of neuronal computations, it is essential that this interpretation of the LGMD as a local multiplication unit is thoroughly tested. Here we evaluate an alternative model that tests the hypothesis that the non-linear responses of the LGMD neuron emerge from the interactions of many neurons in the opto-motor processing structure of the locust. We show, by exposing our model to standard LGMD stimulation protocols, that the properties of the LGMD that were seen as a hallmark of local non-linear operations can be explained as emerging from the dynamics of the pre-synaptic network. Moreover, we demonstrate that these properties strongly depend on the details of the synaptic projections from the medulla to the LGMD. From these observations we deduce a number of testable predictions. To assess the real-time properties of our model we applied it to a high-speed robot. These robot results show that our model of the locust opto-motor system is able to reliably stabilize the movement trajectory of the robot and can robustly support collision avoidance. In addition, these behavioural experiments suggest that the emergent non-linear responses of the LGMD neuron enhance the system's collision detection acuity. We show how all reported properties of this neuron are consistently reproduced by this alternative model, and how they emerge from the overall opto-motor processing structure of the locust. Hence, our results propose an alternative view on neuronal computation that emphasizes the network properties as opposed to the local transformations that can be performed by single neurons.Sergi Bermúdez i BadiaUlysses BernardetPaul F M J VerschurePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 3, p e1000701 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Sergi Bermúdez i Badia Ulysses Bernardet Paul F M J Verschure Non-linear neuronal responses as an emergent property of afferent networks: a case study of the locust lobula giant movement detector.
description	In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the locust, which is reported to locally perform a functional multiplication. Given the wide ramifications of this suggestion with respect to our understanding of neuronal computations, it is essential that this interpretation of the LGMD as a local multiplication unit is thoroughly tested. Here we evaluate an alternative model that tests the hypothesis that the non-linear responses of the LGMD neuron emerge from the interactions of many neurons in the opto-motor processing structure of the locust. We show, by exposing our model to standard LGMD stimulation protocols, that the properties of the LGMD that were seen as a hallmark of local non-linear operations can be explained as emerging from the dynamics of the pre-synaptic network. Moreover, we demonstrate that these properties strongly depend on the details of the synaptic projections from the medulla to the LGMD. From these observations we deduce a number of testable predictions. To assess the real-time properties of our model we applied it to a high-speed robot. These robot results show that our model of the locust opto-motor system is able to reliably stabilize the movement trajectory of the robot and can robustly support collision avoidance. In addition, these behavioural experiments suggest that the emergent non-linear responses of the LGMD neuron enhance the system's collision detection acuity. We show how all reported properties of this neuron are consistently reproduced by this alternative model, and how they emerge from the overall opto-motor processing structure of the locust. Hence, our results propose an alternative view on neuronal computation that emphasizes the network properties as opposed to the local transformations that can be performed by single neurons.
format	article
author	Sergi Bermúdez i Badia Ulysses Bernardet Paul F M J Verschure
author_facet	Sergi Bermúdez i Badia Ulysses Bernardet Paul F M J Verschure
author_sort	Sergi Bermúdez i Badia
title	Non-linear neuronal responses as an emergent property of afferent networks: a case study of the locust lobula giant movement detector.
title_short	Non-linear neuronal responses as an emergent property of afferent networks: a case study of the locust lobula giant movement detector.
title_full	Non-linear neuronal responses as an emergent property of afferent networks: a case study of the locust lobula giant movement detector.
title_fullStr	Non-linear neuronal responses as an emergent property of afferent networks: a case study of the locust lobula giant movement detector.
title_full_unstemmed	Non-linear neuronal responses as an emergent property of afferent networks: a case study of the locust lobula giant movement detector.
title_sort	non-linear neuronal responses as an emergent property of afferent networks: a case study of the locust lobula giant movement detector.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/6dc4a57c362c45bd9907cfda43b63ea6
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Non-linear neuronal responses as an emergent property of afferent networks: a case study of the locust lobula giant movement detector.

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