Using an artificial neural network for studying the interneuronal layer of a leech neuronal circuit

We have modeled the neuronai circuit that conveys mechanosensory input onto a pair of serotonergic neurons in the nervous system of the leech. The objective of this work is to use an artificial neural networks (ANN) in the investigation of the interneuronal layer of this circuit, which represents an...

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Detalles Bibliográficos
Autores principales:	Santos, J.M., Lidia, S.
Formato:	SER
Materias:	Neural networks Physiological models Biological parameter Hidden units Neuronal circuits Saturation function Three-layer perceptron Neurons
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_03029743_v1240LNCS_n_p207_Santos
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	todo:paper_03029743_v1240LNCS_n_p207_Santos
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spelling	todo:paper_03029743_v1240LNCS_n_p207_Santos2023-10-03T15:18:49Z Using an artificial neural network for studying the interneuronal layer of a leech neuronal circuit Santos, J.M. Lidia, S. Neural networks Physiological models Biological parameter Hidden units Neuronal circuits Saturation function Three-layer perceptron Neurons We have modeled the neuronai circuit that conveys mechanosensory input onto a pair of serotonergic neurons in the nervous system of the leech. The objective of this work is to use an artificial neural networks (ANN) in the investigation of the interneuronal layer of this circuit, which represents an unknown population of cells. The ANN is a three layer perceptron, whose input units represent the mechanosensory neurons, the output unit represents the serotonergic neurons and the hidden units represent the unknown intemeuronal layer. The connections between input and hidden units were represented as a saturation function rather than as linear one. The weights and thresholds were adjusted using the backpropagation algorithm. The ANN parameters were correlated with specific biological parameters enabling to test and classify the resuIting configurations according to physiological and experimental considerations. We obtained a finite and restricted number of solutions that can be experimentally tested. SER info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_03029743_v1240LNCS_n_p207_Santos
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Neural networks Physiological models Biological parameter Hidden units Neuronal circuits Saturation function Three-layer perceptron Neurons
spellingShingle	Neural networks Physiological models Biological parameter Hidden units Neuronal circuits Saturation function Three-layer perceptron Neurons Santos, J.M. Lidia, S. Using an artificial neural network for studying the interneuronal layer of a leech neuronal circuit
topic_facet	Neural networks Physiological models Biological parameter Hidden units Neuronal circuits Saturation function Three-layer perceptron Neurons
description	We have modeled the neuronai circuit that conveys mechanosensory input onto a pair of serotonergic neurons in the nervous system of the leech. The objective of this work is to use an artificial neural networks (ANN) in the investigation of the interneuronal layer of this circuit, which represents an unknown population of cells. The ANN is a three layer perceptron, whose input units represent the mechanosensory neurons, the output unit represents the serotonergic neurons and the hidden units represent the unknown intemeuronal layer. The connections between input and hidden units were represented as a saturation function rather than as linear one. The weights and thresholds were adjusted using the backpropagation algorithm. The ANN parameters were correlated with specific biological parameters enabling to test and classify the resuIting configurations according to physiological and experimental considerations. We obtained a finite and restricted number of solutions that can be experimentally tested.
format	SER
author	Santos, J.M. Lidia, S.
author_facet	Santos, J.M. Lidia, S.
author_sort	Santos, J.M.
title	Using an artificial neural network for studying the interneuronal layer of a leech neuronal circuit
title_short	Using an artificial neural network for studying the interneuronal layer of a leech neuronal circuit
title_full	Using an artificial neural network for studying the interneuronal layer of a leech neuronal circuit
title_fullStr	Using an artificial neural network for studying the interneuronal layer of a leech neuronal circuit
title_full_unstemmed	Using an artificial neural network for studying the interneuronal layer of a leech neuronal circuit
title_sort	using an artificial neural network for studying the interneuronal layer of a leech neuronal circuit
url	http://hdl.handle.net/20.500.12110/paper_03029743_v1240LNCS_n_p207_Santos
work_keys_str_mv	AT santosjm usinganartificialneuralnetworkforstudyingtheinterneuronallayerofaleechneuronalcircuit AT lidias usinganartificialneuralnetworkforstudyingtheinterneuronallayerofaleechneuronalcircuit
_version_	1782030039878991872

Using an artificial neural network for studying the interneuronal layer of a leech neuronal circuit

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