ArticlePLoS computational biology2015
Formation and Maintenance of Robust Long-Term Information Storage in the Presence of Synaptic Turnover.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
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.
Who cites it
17 citing papers in PubMed.
- A computational framework for epigenetic plasticity in memory.Brain : a journal of neurology · 2026Review
- Statistics of cortical representational drift can enable robust readout.PLoS computational biology · 2026Article
- Information encoded in volumes and areas of dendritic spines is nearly maximal across mammalian brains.Scientific reports · 2023Article
- Dynamics of phase oscillator networks with synaptic weight and structural plasticity.Scientific reports · 2022Article
- Randomly fluctuating neural connections may implement a consolidation mechanism that explains classic memory laws.Scientific reports · 2022Article
- Self-healing codes: How stable neural populations can track continually reconfiguring neural representations.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Homeostatic control of synaptic rewiring in recurrent networks induces the formation of stable memory engrams.PLoS computational biology · 2022Article
- Silent Synapses in Cocaine-Associated Memory and Beyond.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Review
- Spine dynamics in the brain, mental disorders and artificial neural networks.Nature reviews. Neuroscience · 2021Review
- Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission.Biology · 2021Article
- Review
- Stable memory and computation in randomly rewiring neural networks.Journal of neurophysiology · 2019Article
- Intrinsic Spine Dynamics Are Critical for Recurrent Network Learning in Models With and Without Autism Spectrum Disorder.Frontiers in computational neuroscience · 2019Article
- Multicontact Co-operativity in Spike-Timing-Dependent Structural Plasticity Stabilizes Networks.Cerebral cortex (New York, N.Y. : 1991) · 2018Article
- Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks.Scientific reports · 2018Article
- Opposing Effects of Neuronal Activity on Structural Plasticity.Frontiers in neuroanatomy · 2016Review
- Structural Plasticity, Effectual Connectivity, and Memory in Cortex.Frontiers in neuroanatomy · 2016Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A long-standing problem is how memories can be stored for very long times despite the volatility of the underlying neural substrate, most notably the high turnover of dendritic spines and synapses. To address this problem, here we are using a generic and simple probabilistic model for the creation and removal of synapses. We show that information can be stored for several months when utilizing the intrinsic dynamics of multi-synapse connections. In such systems, single synapses can still show high turnover, which enables fast learning of new information, but this will not perturb prior stored information (slow forgetting), which is represented by the compound state of the connections. The model matches the time course of recent experimental spine data during learning and memory in mice supporting the assumption of multi-synapse connections as the basis for long-term storage.
Indexed as
Identifiers
What OpenQuestion holds
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.