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Computational Design of Enhanced Learning Protocols
Learning and memory are influenced by the temporal pattern of training stimuli. The mechanisms that determine the effectiveness of a particular training protocol are not well understood, however. The hypothesis that the efficacy of a protocol is determined, in part, by interactions among biochemical...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3267874/ https://www.ncbi.nlm.nih.gov/pubmed/22197829 http://dx.doi.org/10.1038/nn.2990 |
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author | Zhang, Yili Liu, Rong-Yu Heberton, George A. Smolen, Paul Baxter, Douglas A. Cleary, Leonard J. Byrne, John H. |
author_facet | Zhang, Yili Liu, Rong-Yu Heberton, George A. Smolen, Paul Baxter, Douglas A. Cleary, Leonard J. Byrne, John H. |
author_sort | Zhang, Yili |
collection | PubMed |
description | Learning and memory are influenced by the temporal pattern of training stimuli. The mechanisms that determine the effectiveness of a particular training protocol are not well understood, however. The hypothesis that the efficacy of a protocol is determined, in part, by interactions among biochemical cascades that underlie learning and memory was examined. Previous studies suggest that the PKA and ERK cascades are necessary to induce long-term synaptic facilitation (LTF) in Aplysia, a neuronal correlate of memory. A computational model of the PKA and ERK cascades was developed, and used the model to identify a novel training protocol that maximized PKA/ERK interactions. In vitro studies confirmed that the protocol enhanced LTF. Moreover, the protocol enhanced levels of phosphorylation of the transcription factor CREB1. Behavioral training confirmed that long-term memory also was enhanced by the protocol. These results illustrate the feasibility of using computational models to design training protocols that improve memory. |
format | Online Article Text |
id | pubmed-3267874 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
record_format | MEDLINE/PubMed |
spelling | pubmed-32678742012-08-01 Computational Design of Enhanced Learning Protocols Zhang, Yili Liu, Rong-Yu Heberton, George A. Smolen, Paul Baxter, Douglas A. Cleary, Leonard J. Byrne, John H. Nat Neurosci Article Learning and memory are influenced by the temporal pattern of training stimuli. The mechanisms that determine the effectiveness of a particular training protocol are not well understood, however. The hypothesis that the efficacy of a protocol is determined, in part, by interactions among biochemical cascades that underlie learning and memory was examined. Previous studies suggest that the PKA and ERK cascades are necessary to induce long-term synaptic facilitation (LTF) in Aplysia, a neuronal correlate of memory. A computational model of the PKA and ERK cascades was developed, and used the model to identify a novel training protocol that maximized PKA/ERK interactions. In vitro studies confirmed that the protocol enhanced LTF. Moreover, the protocol enhanced levels of phosphorylation of the transcription factor CREB1. Behavioral training confirmed that long-term memory also was enhanced by the protocol. These results illustrate the feasibility of using computational models to design training protocols that improve memory. 2011-12-25 /pmc/articles/PMC3267874/ /pubmed/22197829 http://dx.doi.org/10.1038/nn.2990 Text en Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Zhang, Yili Liu, Rong-Yu Heberton, George A. Smolen, Paul Baxter, Douglas A. Cleary, Leonard J. Byrne, John H. Computational Design of Enhanced Learning Protocols |
title | Computational Design of Enhanced Learning Protocols |
title_full | Computational Design of Enhanced Learning Protocols |
title_fullStr | Computational Design of Enhanced Learning Protocols |
title_full_unstemmed | Computational Design of Enhanced Learning Protocols |
title_short | Computational Design of Enhanced Learning Protocols |
title_sort | computational design of enhanced learning protocols |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3267874/ https://www.ncbi.nlm.nih.gov/pubmed/22197829 http://dx.doi.org/10.1038/nn.2990 |
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