<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Zhang Y</submitter><funding>NINDS NIH HHS</funding><funding>NLM NIH HHS</funding><pagination>294-7</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC3267874</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>15(2)</volume><pubmed_abstract>Learning and memory are influenced by the temporal pattern of training stimuli. However, the mechanisms that determine the effectiveness of a particular training protocol are not well understood. We tested the hypothesis that the efficacy of a protocol is determined in part by interactions among biochemical cascades that underlie learning and memory. Previous findings suggest that the protein kinase A (PKA) and extracellular signal-regulated kinase (ERK) cascades are necessary to induce long-term synaptic facilitation (LTF) in Aplysia, a neuronal correlate of memory. We developed a computational model of the PKA and ERK cascades and used it to identify a training protocol that maximized PKA and ERK interactions. In vitro studies confirmed that the protocol enhanced LTF. Moreover, the protocol enhanced the 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.</pubmed_abstract><journal>Nature neuroscience</journal><pubmed_title>Computational design of enhanced learning protocols.</pubmed_title><pmcid>PMC3267874</pmcid><funding_grant_id>P01 NS038310-10</funding_grant_id><funding_grant_id>R01 NS073974</funding_grant_id><funding_grant_id>R01 NS019895</funding_grant_id><funding_grant_id>P01 NS038310</funding_grant_id><funding_grant_id>R01 NS019895-29</funding_grant_id><funding_grant_id>T15LM007093</funding_grant_id><funding_grant_id>R01 NS073974-02</funding_grant_id><funding_grant_id>T15 LM007093-10</funding_grant_id><funding_grant_id>T15 LM007093</funding_grant_id><pubmed_authors>Liu RY</pubmed_authors><pubmed_authors>Cleary LJ</pubmed_authors><pubmed_authors>Smolen P</pubmed_authors><pubmed_authors>Heberton GA</pubmed_authors><pubmed_authors>Zhang Y</pubmed_authors><pubmed_authors>Byrne JH</pubmed_authors><pubmed_authors>Baxter DA</pubmed_authors></additional><is_claimable>false</is_claimable><name>Computational design of enhanced learning protocols.</name><description>Learning and memory are influenced by the temporal pattern of training stimuli. However, the mechanisms that determine the effectiveness of a particular training protocol are not well understood. We tested the hypothesis that the efficacy of a protocol is determined in part by interactions among biochemical cascades that underlie learning and memory. Previous findings suggest that the protein kinase A (PKA) and extracellular signal-regulated kinase (ERK) cascades are necessary to induce long-term synaptic facilitation (LTF) in Aplysia, a neuronal correlate of memory. We developed a computational model of the PKA and ERK cascades and used it to identify a training protocol that maximized PKA and ERK interactions. In vitro studies confirmed that the protocol enhanced LTF. Moreover, the protocol enhanced the 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.</description><dates><release>2011-01-01T00:00:00Z</release><publication>2011 Dec</publication><modification>2020-10-31T09:26:39Z</modification><creation>2019-03-27T00:48:31Z</creation></dates><accession>S-EPMC3267874</accession><cross_references><pubmed>22197829</pubmed><doi>10.1038/nn.2990</doi></cross_references></HashMap>