Dataset Information

The role of non-coding RNAs in the formation of long-term associative memory after single-trial learning in Lymnaea.

ABSTRACT: Investigations of the molecular mechanisms of long-term associative memory have revealed key roles for a number of highly evolutionarily conserved molecular pathways in a variety of different vertebrate and invertebrate model systems. One such system is the pond snail Lymnaea stagnalis, in which, like in other systems, the transcription factors CREB1 and CREB2 and the enzyme NOS play essential roles in the consolidation of long-term associative memory. More recently, epigenetic control mechanisms, such as DNA methylation, histone modifications, and control of gene expression by non-coding RNAs also have been found to play important roles in all model systems. In this minireview, we will focus on how, in Lymnaea, even a single episode of associative learning can activate CREB and NO dependent cascades due to the training-induced up- or downregulation of the expression levels of recently identified short and long non-coding RNAs.

SUBMITTER: Kemenes G

PROVIDER: S-EPMC9639457 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

The role of non-coding RNAs in the formation of long-term associative memory after single-trial learning in <i>Lymnaea</i>.

Kemenes György G Benjamin Paul R PR Kemenes Ildikó I

Frontiers in behavioral neuroscience 20221014

Investigations of the molecular mechanisms of long-term associative memory have revealed key roles for a number of highly evolutionarily conserved molecular pathways in a variety of different vertebrate and invertebrate model systems. One such system is the pond snail <i>Lymnaea stagnalis</i>, in which, like in other systems, the transcription factors CREB1 and CREB2 and the enzyme NOS play essential roles in the consolidation of long-term associative memory. More recently, epigenetic control me ...[more]

PMID: 36353518

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Project description:Because of the relative simplicity of its nervous system, Caenorhabditis elegans is a useful model organism to study learning and memory at cellular and molecular levels. For appetitive conditioning in C. elegans, food has exclusively been used as an unconditioned stimulus (US). It may be difficult to analyze neuronal circuits for associative memory since food is a multimodal combination of olfactory, gustatory, and mechanical stimuli. Here, we report classical appetitive conditioning and associative memory in C. elegans, using 1-nonanol as a conditioned stimulus (CS), and potassium chloride (KCl) as a US. Before conditioning, C. elegans innately avoided 1-nonanol, an aversive olfactory stimulus, and was attracted by KCl, an appetitive gustatory stimulus, on assay agar plates. Both massed training without an intertrial interval (ITI) and spaced training with a 10-min ITI induced significant levels of memory of association regarding the two chemicals. Memory induced by massed training decayed within 6 h, while that induced by spaced training was retained for more than 6 h. Animals treated with inhibitors of transcription or translation formed the memory induced by spaced training less efficiently than untreated animals, whereas the memory induced by massed training was not significantly affected by such treatments. By definition, therefore, memories induced by massed training and spaced training are classified as short-term memory (STM) and long-term memory (LTM), respectively. When animals conditioned by spaced training were exposed to 1-nonanol alone, their learning index was lower than that of untreated animals, suggesting that extinction learning occurs in C. elegans. In support of these results, C. elegans mutants defective in nmr-1, encoding an NMDA receptor subunit, formed both STM and LTM less efficiently than wild-type animals, while mutations in crh-1, encoding a ubiquitous transcription factor CREB required for memory consolidation, affected LTM, but not STM. The paradigm established in the present study should allow us to elucidate neuronal circuit plasticity for appetitive learning and memory in C. elegans.

Project description:The computational principles adopted by the hippocampus in associative memory (AM) tasks have been one of the most studied topics in computational and theoretical neuroscience. Recent theories suggested that AM and the predictive activities of the hippocampus could be described within a unitary account, and that predictive coding underlies the computations supporting AM in the hippocampus. Following this theory, a computational model based on classical hierarchical predictive networks was proposed and was shown to perform well in various AM tasks. However, this fully hierarchical model did not incorporate recurrent connections, an architectural component of the CA3 region of the hippocampus that is crucial for AM. This makes the structure of the model inconsistent with the known connectivity of CA3 and classical recurrent models such as Hopfield Networks, which learn the covariance of inputs through their recurrent connections to perform AM. Earlier PC models that learn the covariance information of inputs explicitly via recurrent connections seem to be a solution to these issues. Here, we show that although these models can perform AM, they do it in an implausible and numerically unstable way. Instead, we propose alternatives to these earlier covariance-learning predictive coding networks, which learn the covariance information implicitly and plausibly, and can use dendritic structures to encode prediction errors. We show analytically that our proposed models are perfectly equivalent to the earlier predictive coding model learning covariance explicitly, and encounter no numerical issues when performing AM tasks in practice. We further show that our models can be combined with hierarchical predictive coding networks to model the hippocampo-neocortical interactions. Our models provide a biologically plausible approach to modelling the hippocampal network, pointing to a potential computational mechanism during hippocampal memory formation and recall, which employs both predictive coding and covariance learning based on the recurrent network structure of the hippocampus.

Dataset Information

The role of non-coding RNAs in the formation of long-term associative memory after single-trial learning in Lymnaea.

Publications

The role of non-coding RNAs in the formation of long-term associative memory after single-trial learning in <i>Lymnaea</i>.

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