Labelling and optical erasure of synaptic memory traces in the motor cortex

By Akiko Hayashi-Takagi, Sho Yagishita, Mayumi Nakamura, Fukutoshi Shirai, Yi I. Wu, Amanda L. Loshbaugh, Brian Kuhlman, K. Hahn, Haruo Kasai. 2015

615 citations recorded by the source corpus.

Dendritic spines are the major loci of synaptic plasticity and are considered as possible structural correlates of memory. Nonetheless, systematic manipulation of specific subsets of spines in the cortex has been unattainable, and thus, the link between spines and memory has been correlational. We developed a novel synaptic optoprobe, AS-PaRac1 (activated synapse targeting photoactivatable Rac1), that can label recently potentiated spines specifically, and induce the selective shrinkage of AS-PaRac1-containing spines. In vivo imaging of AS-PaRac1 revealed that a motor learning task induced substantial synaptic remodelling in a small subset of neurons. The acquired motor learning was disrupted by the optical shrinkage of the potentiated spines, whereas it was not affected by the identical manipulation of spines evoked by a distinct motor task in the same cortical region. Taken together, our results demonstrate that a newly acquired motor skill depends on the formation of a task-specific dense synaptic ensemble.

Subfields: Neuroscience

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Citations in the corpus

Overcoming catastrophic forgetting in neural networks (2016, background): “When these spines are selectively “erased,” the corresponding skill is forgotten (11, 12).”
Incorporating neuro-inspired adaptability for continual learning in artificial intelligence (2023, neutral): “…deeply dissecting its underlying mechanisms from molecular to synaptic structural levels, where activation of the molecular signalling that mediates active forgetting initially leads to rapid growth of synaptic structures, but prolonged activation instead leads to their shrinkage 19,30,42,60–64 .”
NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks (2022, neutral): “(Cichon & Gan, 2015; Hayashi-Takagi et al., 2015).”
Toward an Integration of Deep Learning and Neuroscience (2016, background): “Some models that incorporate dendritic coincidence detection for learning temporal sequences predict that a given axon should make only a small number of synapses on a given dendritic 56 Fluorescent techniques like (Hayashi-Takagi et al., 2015) might be helpful. segment (Hawkins and Ahmad, 2016).”
Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma (2021, background): “In line with experimental evidence (Yang et al., 2009; Hayashi-Takagi et al., 2015), we demonstrate that selectively stabilising important synapses improves lifelong learning.”
Memory engrams: Recalling the past and imagining the future (2020, background): “Finally, shrinking potentiated synapses in primary motor cortex (M1) engram cells supporting a motor memory disrupted subsequent performance of this, and not a similar, motor memory (90).”
The Persistence and Transience of Memory. (2017, background): “Similarly, it is reasonable to suggest that weight elimination corresponds to neurobiological mechanisms that eliminate previously potentiated synaptic connections, including Rac-mediated spine shrinkage (Hayashi-Takagi et al., 2015) and Arp2/3 destabilization (Hadziselimovic et al.”