ArticleeLife2021
Synaptic learning rules for sequence learning.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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.
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Who cites it
16 citing papers in PubMed, 39 citations in OpenAlex.
- Phase-tuned modulation during reward expectancy in human anterior insular cortex.Communications biology · 2026Article
- Neuroplastic effects of transcranial alternating current stimulation (tACS): from mechanisms to clinical trials.Frontiers in human neuroscience · 2025Review
- Theta phase precession supports memory formation and retrieval of naturalistic experience in humans.Nature human behaviour · 2024Article
- Recall tempo of Hebbian sequences depends on the interplay of Hebbian kernel with tutor signal timing.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Induced neural phase precession through exogenous electric fields.Nature communications · 2024Article
- Artificial sensory system based on memristive devices.Exploration (Beijing, China) · 2024Review
- Induced neural phase precession through exogeneous electric fields.bioRxiv : the preprint server for biology · 2023Article
- Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule.Nature communications · 2023Article
- Offline memory replay in recurrent neuronal networks emerges from constraints on online dynamics.The Journal of physiology · 2023Article
- Learning to predict future locations with internally generated theta sequences.PLoS computational biology · 2023Article
- Review
- Article
- Hebbian plasticity in parallel synaptic pathways: A circuit mechanism for systems memory consolidation.PLoS computational biology · 2021Article
- Article
- Article
- Synaptic learning rules for sequence learning.eLife · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Remembering the temporal order of a sequence of events is a task easily performed by humans in everyday life, but the underlying neuronal mechanisms are unclear. This problem is particularly intriguing as human behavior often proceeds on a time scale of seconds, which is in stark contrast to the much faster millisecond time-scale of neuronal processing in our brains. One long-held hypothesis in sequence learning suggests that a particular temporal fine-structure of neuronal activity - termed 'phase precession' - enables the compression of slow behavioral sequences down to the fast time scale of the induction of synaptic plasticity. Using mathematical analysis and computer simulations, we find that - for short enough synaptic learning windows - phase precession can improve temporal-order learning tremendously and that the asymmetric part of the synaptic learning window is essential for temporal-order learning. To test these predictions, we suggest experiments that selectively alter phase precession or the learning window and evaluate memory of temporal order.
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Registered trials
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.