Role of unconditioned stimulus prediction in the operant learning: A neural network model

A neural network model of operant conditioning for appetitive and aversive stimuli is proposed. From neurobiological and behavioral bases it is assumed that animals are able to compute the prediction of the unconditioned stimulus. The prediction controls the learning of the correct response to obtai...

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Detalles Bibliográficos
Autores principales:	Lew, S.E., Wedemeyer, C., Zanutto, B.S.
Formato:	CONF
Materias:	Cognitive systems Computer simulation Learning systems Neurophysiology Probability Random processes Hebbian learning Operant learning Synaptic weights Neural networks
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_NIS20731_v1_n_p331_Lew
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	todo:paper_NIS20731_v1_n_p331_Lew
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spelling	todo:paper_NIS20731_v1_n_p331_Lew2023-10-03T16:46:18Z Role of unconditioned stimulus prediction in the operant learning: A neural network model Lew, S.E. Wedemeyer, C. Zanutto, B.S. Cognitive systems Computer simulation Learning systems Neurophysiology Probability Random processes Hebbian learning Operant learning Synaptic weights Neural networks A neural network model of operant conditioning for appetitive and aversive stimuli is proposed. From neurobiological and behavioral bases it is assumed that animals are able to compute the prediction of the unconditioned stimulus. The prediction controls the learning of the correct response to obtain reward and to avoid punishment. The model has as inputs: all the conditioned stimuli and the unconditioned stimulus. The outputs are all the possible responses of the animal; each one is computed by one neuron. Based on Hebbian or anti-Hebbian learning, depending on the prediction, the synaptic weights of the response neurons are calculated. The synaptic weights of the neuron computing the prediction are calculated based on the Rescorla-Wagner model. The simulated and experimental data have been compared, showing that the model predicts relevant features of operant conditioning. This model is a theory of operant conditioning and provides principles to design autonomous systems. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_NIS20731_v1_n_p331_Lew
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Cognitive systems Computer simulation Learning systems Neurophysiology Probability Random processes Hebbian learning Operant learning Synaptic weights Neural networks
spellingShingle	Cognitive systems Computer simulation Learning systems Neurophysiology Probability Random processes Hebbian learning Operant learning Synaptic weights Neural networks Lew, S.E. Wedemeyer, C. Zanutto, B.S. Role of unconditioned stimulus prediction in the operant learning: A neural network model
topic_facet	Cognitive systems Computer simulation Learning systems Neurophysiology Probability Random processes Hebbian learning Operant learning Synaptic weights Neural networks
description	A neural network model of operant conditioning for appetitive and aversive stimuli is proposed. From neurobiological and behavioral bases it is assumed that animals are able to compute the prediction of the unconditioned stimulus. The prediction controls the learning of the correct response to obtain reward and to avoid punishment. The model has as inputs: all the conditioned stimuli and the unconditioned stimulus. The outputs are all the possible responses of the animal; each one is computed by one neuron. Based on Hebbian or anti-Hebbian learning, depending on the prediction, the synaptic weights of the response neurons are calculated. The synaptic weights of the neuron computing the prediction are calculated based on the Rescorla-Wagner model. The simulated and experimental data have been compared, showing that the model predicts relevant features of operant conditioning. This model is a theory of operant conditioning and provides principles to design autonomous systems.
format	CONF
author	Lew, S.E. Wedemeyer, C. Zanutto, B.S.
author_facet	Lew, S.E. Wedemeyer, C. Zanutto, B.S.
author_sort	Lew, S.E.
title	Role of unconditioned stimulus prediction in the operant learning: A neural network model
title_short	Role of unconditioned stimulus prediction in the operant learning: A neural network model
title_full	Role of unconditioned stimulus prediction in the operant learning: A neural network model
title_fullStr	Role of unconditioned stimulus prediction in the operant learning: A neural network model
title_full_unstemmed	Role of unconditioned stimulus prediction in the operant learning: A neural network model
title_sort	role of unconditioned stimulus prediction in the operant learning: a neural network model
url	http://hdl.handle.net/20.500.12110/paper_NIS20731_v1_n_p331_Lew
work_keys_str_mv	AT lewse roleofunconditionedstimuluspredictionintheoperantlearninganeuralnetworkmodel AT wedemeyerc roleofunconditionedstimuluspredictionintheoperantlearninganeuralnetworkmodel AT zanuttobs roleofunconditionedstimuluspredictionintheoperantlearninganeuralnetworkmodel
_version_	1807314437871239168

Role of unconditioned stimulus prediction in the operant learning: A neural network model

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