Synapse rearrangements upon learning: from divergent–sparse connectivity to dedicated sub-circuits
Abstract
Learning can involve formation of new synapses and loss of synapses, providing memory traces of learned skills. Recent findings suggest that these synapse rearrangements reflect assembly of task-related sub-circuits from initially broadly distributed and sparse connectivity in the brain. These local circuit remodeling processes involve rapid emergence of synapses upon learning, followed by protracted validation involving strengthening of some new synapses, and selective elimination of others. The timing of these consolidation processes can vary. Here, we review these findings, focusing on how molecular/cellular mechanisms of synapse assembly, strengthening, and elimination might interface with circuit/system mechanisms of learning and memory consolidation. An integrated understanding of these learning-related processes should provide a better basis to elucidate how experience, genetic background, and disease influence brain function.
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Reelin signaling activates PI3kinase, which initiates a pathway that induces phosphorylation of LIM kinase-1 (LIMK-1), which in turn phosphorylates cofilin at an inhibitory site, blocking the actin-depolymerizing activity of cofilin.
“Reelin signaling activates PI3kinase, as described above [61]. In turn, PI3kinase initiates a signaling pathway that induces phosphorylation of LIM kinase-1 (LIMK-1), which in turn phosphorylates cofilin at an inhibitory site, blocking the actin-depolymerizing activity of cofilin [62, 63].”
From ApoE, ApoE Receptors, and the Synapse in Alzheimer's Disease (2017) · cites this paper as [62]
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- Authors
- Caroni, P., Chowdhury, A., & Lahr, M.
- Affiliations
- Friedrich Miescher Institute (CH)
- Corresponding author
- Caroni, Pico
- Venue
- Trends in Neurosciences
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 37
- Issue
- 10
- Pages
- 604-614
- ISSN
- 0166-2236 (ISSN-L)
- OpenAlex
- W2012140908
- Cited by
- 85
- References
- 155
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 7 · 2022: 4 · 2021: 7 · 2020: 4 · 2019: 10 · 2018: 15 · 2017: 11 · 2016: 10 · 2015: 12
- FWCI
- 1.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Trends in Neurosciences (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Novartis Foundation
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- synaptic plasticity, synaptic pruning, synaptogenesis, memory consolidation, sparse connectivity, learning, memory traces
- MeSH terms
- Animals, Brain, Humans, Learning, Memory, Nerve Net, Neuronal Plasticity, Synapses
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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