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Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron

Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron
Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron
Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to displacement of a sensory organ, the femoral chordotonal organ, which monitors movements of the tibia relative to the femur of the leg. The aim of the study was threefold: first to determine the potential value of ANNs as tools to model and investigate neural networks, second to understand the generalisation properties of ANNs across individuals and to different input signals and third, to understand individual differences in responses of an identified neuron. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results suggest that ANNs are significantly better than LNL and Wiener models in predicting specific neural responses to Gaussian White Noise, but not significantly different when tested with sinusoidal inputs. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model, although notable differences between some individuals were evident.
artificial neural network, metaheuristic algorithm, proprioception, grasshopper, motor neuron, individual differences
56-65
Costalago Meruelo, Alicia
7525af96-0dfd-46f6-b3f6-52c07b73f55b
Simpson, David
53674880-f381-4cc9-8505-6a97eeac3c2a
Veres, Sandor
86567288-28f8-48ed-adb4-a27e706d0696
Newland, Philip
7a018c0e-37ba-40f5-bbf6-49ab0f299dbb
Costalago Meruelo, Alicia
7525af96-0dfd-46f6-b3f6-52c07b73f55b
Simpson, David
53674880-f381-4cc9-8505-6a97eeac3c2a
Veres, Sandor
86567288-28f8-48ed-adb4-a27e706d0696
Newland, Philip
7a018c0e-37ba-40f5-bbf6-49ab0f299dbb

Costalago Meruelo, Alicia, Simpson, David, Veres, Sandor and Newland, Philip (2016) Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron. Neural Networks, 75, 56-65. (doi:10.1016/j.neunet.2015.12.002).

Record type: Article

Abstract

Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to displacement of a sensory organ, the femoral chordotonal organ, which monitors movements of the tibia relative to the femur of the leg. The aim of the study was threefold: first to determine the potential value of ANNs as tools to model and investigate neural networks, second to understand the generalisation properties of ANNs across individuals and to different input signals and third, to understand individual differences in responses of an identified neuron. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results suggest that ANNs are significantly better than LNL and Wiener models in predicting specific neural responses to Gaussian White Noise, but not significantly different when tested with sinusoidal inputs. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model, although notable differences between some individuals were evident.

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Accepted/In Press date: 4 December 2015
e-pub ahead of print date: 9 December 2015
Published date: March 2016
Keywords: artificial neural network, metaheuristic algorithm, proprioception, grasshopper, motor neuron, individual differences
Organisations: Faculty of Engineering and the Environment

Identifiers

Local EPrints ID: 385581
URI: http://eprints.soton.ac.uk/id/eprint/385581
PURE UUID: 25d92b6f-198c-4db2-8aae-850e90cb2828
ORCID for Philip Newland: ORCID iD orcid.org/0000-0003-4124-8507

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Date deposited: 20 Jan 2016 16:10
Last modified: 24 Jun 2020 00:27

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Contributors

Author: Alicia Costalago Meruelo
Author: David Simpson
Author: Sandor Veres
Author: Philip Newland ORCID iD

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