Making memories last: the synaptic tagging and capture hypothesis

By R. Redondo, Richard G. M. Morris. 2010

773 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Molecular Biology

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Citations in the corpus

Synaptic weight decay with selective consolidation enables fast learning without catastrophic forgetting (2019, background): “This justiﬁes the voltage-based consolidation criterion, Synaptic tagging, capture and consolidation We next interpret the consolidation mechanisms in terms of biological quantities arising in the synaptic tagging & capture framework (Frey and Morris, 1997; Redondo and Morris, 2011).”
Improved multitask learning through synaptic intelligence (2017, background): “Chemical synapses, the most abundant synapse in the brain, make use of molecular machinery that endows them with a potentially high-dimensional state space that can affect plasticity at different spatial and temporal scales (Redondo & Morris, 2011).”
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules (2018, background): “Neuromodulators such as dopamine have been identified as the necessary third factor for consolidation (Bailey et al., 2000; Reymann and Frey, 2007; Redondo and Morris, 2011; Lisman, 2017).”
Cellular and Systems Consolidation of Declarative Memory (2017, background): “Behavioural tagging is said to occur when a weak and otherwise transient memory is transformed into a more durable memory when it occurs close in time to other behaviourally relevant experiences that provide PRPs (Moncada et al. 2015).”
BDNF-induced local protein synthesis and synaptic plasticity. (2014, positive): “…locally translated at the synapse in response to BDNF stimulation could function in the capture of the neurotrophininduced RNA granules or proteins traveling along dendrites, as predicted by the synaptic tagging and capture hypothesis (Redondo and Morris, 2011), but this remains to be investigated.”
What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated. (2016, neutral): “The former refers to the gradual integration of knowledge into neocortical circuits, while the latter denotes stabilization of recently formed memories within the hippocampus, perhaps through stabilization of synapses among hippocampal neurons [89].”
Structural and molecular remodeling of dendritic spine substructures during long-term potentiation (2014, background): “This view is supported by recent experiments showing the dissociation between functional and structural LTP (Yang et al., 2008; Gu et al., 2010; Redondo and Morris, 2011).”
Towards deep learning with segregated dendrites (2016, background): “Although this is not an ideal solution, this small degree of temporal non-locality for synaptic update rules is in-line with known biological mechanisms, such as slowly decaying calcium transients or synaptic tags (Redondo and Morris, 2011).”