Abstract
A key characteristic of learning and neural plasticity is state-dependent acquisition dynamics reflected by the non-linear learning curve that links increase in learning with practice. Here we propose that the manner by which epigenetic states of individual cells change during learning contributes to the shape of the neural and behavioral learning curve. We base our suggestion on recent studies showing that epigenetic mechanisms such as DNA methylation, histone acetylation, and RNA-mediated gene regulation are intimately involved in the establishment and maintenance of long-term neural plasticity, reflecting specific learning-histories and influencing future learning. Our model, which is the first to suggest a dynamic molecular account of the shape of the learning curve, leads to several testable predictions regarding the link between epigenetic dynamics at the promoter, gene-network, and neural-network levels. This perspective opens up new avenues for therapeutic interventions in neurological pathologies.
Highlights
At the very outset of modern learning and memory research it was established that changes in behavior are usually acquired through a dynamic, gradual process of iterative experience, and that the relation between learning-strength and practice—i.e., the learning curve—is usually non-linear (Thorndike, 1898)
We propose an elaboration and an extension of the first proposal, and suggest that the increased global levels of DNA methylation is related to the suppressor of learning-suppressing sequences, but that the class of suppressors are not protein coding genes but rather the genomically widespread clusters coding for precursor sequences of miRNAs, which, when processed, interfere with the gene-expression necessary for plasticity
Due to its mnemonic-like, self-sustaining activity (Miller and Sweatt, 2007; Ginsburg and Jablonka, 2009), the state of the epigenome can both represent neuronal history and affect future gene activity, bridging future and past learning. If this is the case, we predict that epigenetic learning profiles that describe the manner by which the epigenome cumulatively changes as a function of learning will contribute to the shape of the cell’s learning curve and to the overall learning curve of the neural networks that are involved in learning
Summary
At the very outset of modern learning and memory research it was established that changes in behavior are usually acquired through a dynamic, gradual process of iterative experience, and that the relation between learning-strength and practice—i.e., the learning curve—is usually non-linear (Thorndike, 1898). The vast majority of learning curves were described at the behavioral level it was realized that any behavioral manifestation of learning and memory entails some form of persistent neural plasticity This suggested that there must be neural learning curves that reflect changes in neural plasticity as a function of practice. Cooke and Bear (2010) observed a diminishing neural learning curve in the visual cortex following a 5-day visual-exposure, showing that the stronger the current synaptic state, the smaller the increment upon future learning These results are consistent with most behavioral learning-curves (Anderson, 2000). The majority of studies showed, as expected, that genes that support neural plasticity (i.e., genes whose expression levels increase following learning) are associated with positive regulatory epigenetic changes such as reduced DNA methylation and enhanced histone acetylation. A study showing that a gain-of-function mutation in the Mecp gene (a gene that produces a protein that binds to methylated DNA and contributes to the inhibition of transcription) increases its binding to methylated DNA and enhances learning (Li et al, 2011) points in the same direction
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