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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2001
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS20731_v1_n_p331_Lew http://hdl.handle.net/20.500.12110/paper_NIS20731_v1_n_p331_Lew |
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paper:paper_NIS20731_v1_n_p331_Lew |
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paper:paper_NIS20731_v1_n_p331_Lew2023-06-08T16:39:58Z Role of unconditioned stimulus prediction in the operant learning: A neural network model 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. 2001 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS20731_v1_n_p331_Lew 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 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. |
| 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 |
| publishDate |
2001 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS20731_v1_n_p331_Lew http://hdl.handle.net/20.500.12110/paper_NIS20731_v1_n_p331_Lew |
| _version_ |
1769175851602018304 |