Evidence map›Paper›PMID 22197829›Full record

ArticleNature neuroscience2011

Computational design of enhanced learning protocols.

Yili Zhang, Rong-Yu Liu, George A Heberton, Paul Smolen, Douglas A Baxter, Leonard J Cleary, John H Byrne

Abstract read
In one paragraph

Article in Nature neuroscience, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
3.3field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

50 citing papers in PubMed, 81 citations in OpenAlex.

  1. All cells have the capacity to time.Journal of experimental psychology. Animal learning and cognition · 2026
    Article
  2. The Right Time for a Synapse to Change: Windows and Mechanisms of Multiday Training Trials.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  3. Article
  4. What could evolve in the evolution of memory?Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025
    Article
  5. Article
  6. Article
  7. Network motifs exhibiting a differential response to spaced and massed inputs.Learning & memory (Cold Spring Harbor, N.Y.) · 2024
    Article
  8. Article
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  13. Review
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  18. The neural circuit linking mushroom body parallel circuits induces memory consolidation inProceedings of the National Academy of Sciences of the United States of America · 2019
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Yili ZhangDepartment of Neurobiology and Anatomy, W.M. Keck Center for the Neurobiology of Learning and Memory, The University of Texas Medical School at Houston, Houston, Texas, USA.
Rong-Yu Liu
George A Heberton
Paul Smolen
Douglas A Baxter
Leonard J Cleary
John H Byrne
The University of Texas at Austin · USThe University of Texas Health Science Center at Houston · US

Funding

TRAINING PROGRAM IN COMPUTATIONAL BIOLOGY AND MEDICINET15LM007093 · NLM · RICE UNIVERSITY · PI Lydia E. Kavraki · 1992 to 2026
$20.8M
Neural Models of Plasticity: Molecules to NetworksP01NS038310 · NINDS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI BYRNE, JOHN H · 1999 to 2009
$9.3M
NEURAL CONTROL OF BEHAVIORR01NS019895 · NINDS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI BYRNE, JOHN H · 1985 to 2022
$8.3M
Modeling Gene Regulation Essential for Long-Term PlasticityR01NS073974 · NINDS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI BYRNE, JOHN H · 2011 to 2015
$1.5M
NINDS NIH HHS P01 NS038310NINDS NIH HHS R01 NS019895NINDS NIH HHS R01 NS073974NLM NIH HHS T15 LM007093NLM NIH HHS T15LM007093
6 · The paper itself

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.

Indexed as

Computer SimulationModels, BiologicalAnalysis of VarianceAnimalsAplysiaCells, CulturedCoculture TechniquesCREB-Binding ProteinCyclic AMP-Dependent Protein KinasesElectric StimulationExtracellular Signal-Regulated MAP KinasesFunctional LateralityGanglia, InvertebrateHumansLearningLong-Term PotentiationCREB-Binding ProteinCyclic AMP-Dependent Protein KinasesExtracellular Signal-Regulated MAP KinasesSerotonin

Identifiers

PMID22197829
PMCPMC3267874
OpenAlexW2067549555

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.