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Predictive control of intersegmental tarsal movements in an insect

Predictive control of intersegmental tarsal movements in an insect
Predictive control of intersegmental tarsal movements in an insect
In many animals intersegmental reflexes are important for postural and movement control but are still poorly undesrtood. Mathematical methods can be used to model the responses to stimulation, and thus go beyond a simple description of responses to specific inputs. Here we analyse an intersegmental reflex of the foot (tarsus) of the locust hind leg, which raises the tarsus when the tibia is flexed and depresses it when the tibia is extended. A novel method is described to measure and quantify the intersegmental responses of the tarsus to a stimulus to the femoro-tibial chordotonal organ. An Artificial Neural Network, the Time Delay Neural Network, was applied to understand the properties and dynamics of the reflex responses. The aim of this study was twofold: first to develop an accurate method to record and analyse the movement of an appendage and second, to apply methods to model the responses using Artificial Neural Networks. The results show that Artificial Neural Networks provide accurate predictions of tarsal movement when trained with an average reflex response to Gaussian White Noise stimulation compared to linear models. Furthermore, the Artificial Neural Network model can predict the individual responses of each animal and responses to others inputs such as a sinusoid. A detailed understanding of such a reflex response could be included in the design of orthoses or functional electrical stimulation treatments to improve walking in patients with neurological disorders as well as the bio/inspired design of robots.
0929-5313
Costalago-meruelo, Alicia
7525af96-0dfd-46f6-b3f6-52c07b73f55b
Simpson, David M.
53674880-f381-4cc9-8505-6a97eeac3c2a
Veres, Sandor M.
909c60a0-56a3-4eb6-83e4-d52742ecd304
Newland, Philip L.
7a018c0e-37ba-40f5-bbf6-49ab0f299dbb
Costalago-meruelo, Alicia
7525af96-0dfd-46f6-b3f6-52c07b73f55b
Simpson, David M.
53674880-f381-4cc9-8505-6a97eeac3c2a
Veres, Sandor M.
909c60a0-56a3-4eb6-83e4-d52742ecd304
Newland, Philip L.
7a018c0e-37ba-40f5-bbf6-49ab0f299dbb

Costalago-meruelo, Alicia, Simpson, David M., Veres, Sandor M. and Newland, Philip L. (2017) Predictive control of intersegmental tarsal movements in an insect. Journal of Computational Neuroscience. (doi:10.1007/s10827-017-0644-x).

Record type: Article

Abstract

In many animals intersegmental reflexes are important for postural and movement control but are still poorly undesrtood. Mathematical methods can be used to model the responses to stimulation, and thus go beyond a simple description of responses to specific inputs. Here we analyse an intersegmental reflex of the foot (tarsus) of the locust hind leg, which raises the tarsus when the tibia is flexed and depresses it when the tibia is extended. A novel method is described to measure and quantify the intersegmental responses of the tarsus to a stimulus to the femoro-tibial chordotonal organ. An Artificial Neural Network, the Time Delay Neural Network, was applied to understand the properties and dynamics of the reflex responses. The aim of this study was twofold: first to develop an accurate method to record and analyse the movement of an appendage and second, to apply methods to model the responses using Artificial Neural Networks. The results show that Artificial Neural Networks provide accurate predictions of tarsal movement when trained with an average reflex response to Gaussian White Noise stimulation compared to linear models. Furthermore, the Artificial Neural Network model can predict the individual responses of each animal and responses to others inputs such as a sinusoid. A detailed understanding of such a reflex response could be included in the design of orthoses or functional electrical stimulation treatments to improve walking in patients with neurological disorders as well as the bio/inspired design of robots.

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tarsal_paper_reviewed - Accepted Manuscript
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Accepted/In Press date: 31 March 2017
e-pub ahead of print date: 22 April 2017

Identifiers

Local EPrints ID: 412083
URI: http://eprints.soton.ac.uk/id/eprint/412083
ISSN: 0929-5313
PURE UUID: fed60625-138c-4327-ad99-c9757d4e6e00
ORCID for Philip L. Newland: ORCID iD orcid.org/0000-0003-4124-8507

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Date deposited: 06 Jul 2017 16:30
Last modified: 07 Oct 2020 06:21

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Author: Alicia Costalago-meruelo
Author: Sandor M. Veres

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