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Computational design of enhanced learning protocols

Nature Neuroscience volume 15, pages 294–297 (2012)Cite this article

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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.

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Figure 1: Computational model of PKA and ERK pathways.
Figure 2: The enhanced protocol increased the magnitude and duration of LTF.
Figure 3: Enhanced protocol increased the levels of phosphorylated CREB1.
Figure 4: Enhanced protocol induced LTS that persisted for at least 5 d.

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Acknowledgements

We thank M.J. Byrne for his help with implementing the particle swarm optimization algorithm. This work was supported by US National Institutes of Health grants P01 NS038310, R01 NS019895 and R01 NS073974, and by a training fellowship from the Keck Center National Library of Medicine Training Program in Biomedical Informatics of the Gulf Coast Consortia (grant T15LM007093).

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  1. Yili Zhang and Rong-Yu Liu: These authors contributed equally to this work.

Authors and Affiliations

  1. Department 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

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

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Contributions

Y.Z. implemented the computational model and ran all simulations. R.-Y.L. performed the electrophysiological and behavioral experiments. L.J.C. helped design and supervise the behavioral experiments. R.-Y.L. and G.A.H. performed the immunofluorescence experiments. D.A.B. performed the statistical analyses and prepared the illustrations. P.S. and D.A.B. helped design and supervise the computational studies. J.H.B. supervised and contributed to all aspects of these studies. All of the authors discussed the results and contributed to the writing and editing of the manuscript.

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Correspondence to John H Byrne.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1 (PDF 69 kb)

Supplementary Movie 1

Schematic represent of algorithm used to generate training protocols. The QuickTime movie illustrates how 10,000 different protocols were generated by systematically varying ISIs between 5-HT pulses. Protocols are numbered from 1 to 10,000. Protocol 1 is a massed protocol (i.e., 25 min continuous 5-HT application), whereas Protocol 10,000 has uniform 50-min intervals between the start of the pulses. (MOV 10150 kb)

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Zhang, Y., Liu, RY., Heberton, G. et al. Computational design of enhanced learning protocols. Nat Neurosci 15, 294–297 (2012). https://doi.org/10.1038/nn.2990

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