Holtmaat A, Svoboda K. Experience-dependent structural synaptic plasticity in the mammalian brain. Nat Rev Neurosci 10: 647-658

Department of Basic Neurosciences, Medical Faculty, University of Geneva, Switzerland.
Nature Reviews Neuroscience (Impact Factor: 31.43). 10/2009; 10(9):647-58. DOI: 10.1038/nrn2699
Source: PubMed


Synaptic plasticity in adult neural circuits may involve the strengthening or weakening of existing synapses as well as structural plasticity, including synapse formation and elimination. Indeed, long-term in vivo imaging studies are beginning to reveal the structural dynamics of neocortical neurons in the normal and injured adult brain. Although the overall cell-specific morphology of axons and dendrites, as well as of a subpopulation of small synaptic structures, are remarkably stable, there is increasing evidence that experience-dependent plasticity of specific circuits in the somatosensory and visual cortex involves cell type-specific structural plasticity: some boutons and dendritic spines appear and disappear, accompanied by synapse formation and elimination, respectively. This Review focuses on recent evidence for such structural forms of synaptic plasticity in the mammalian cortex and outlines open questions.

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    • "Functional alterations as they occur in many brain disorders are also accompanied by remodeling of neuronal structures, changes in neuronal activity, and loss of neuronal molecules [1] [2] [3]. A number of studies demonstrated that several extrinsic [4] [5] [6] [7] and intrinsic [1–3, 8, 9] changes are associated with alterations in synaptic density or shape, dendritic outgrowth, and even extracellular matrix molecules. "
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    ABSTRACT: The perineuronal net (PN) is a subtype of extracellular matrix appearing as a net-like structure around distinct neurons throughout the whole CNS. PNs surround the soma, proximal dendrites, and the axonal initial segment embedding synaptic terminals on the neuronal surface. Different functions of the PNs are suggested which include support of synaptic stabilization, inhibition of axonal sprouting, and control of neuronal plasticity. A number of studies provide evidence that removing PNs or PN-components results in renewed neurite growth and synaptogenesis. In a mouse model for Purkinje cell degeneration, we examined the effect of deafferentation on synaptic remodeling and modulation of PNs in the deep cerebellar nuclei. We found reduced GABAergic, enhanced glutamatergic innervations at PN-associated neurons, and altered expression of the PN-components brevican and hapln4. These data refer to a direct interaction between ECM and synapses. The altered brevican expression induced by activated astrocytes could be required for an adequate regeneration by promoting neurite growth and synaptogenesis.
    Full-text · Article · Jan 2016
    • "For example, compressed implementations of Willshaw networks can store up to C I = 1 bit per computer bit for almost any non-logarithmic sparse activity with p → 0 and p ∼ log n/n (Knoblauch, 2008). Moreover, networks employing structural plasticity (e.g., by pruning of irrelevant silent synapses) can store up to to C S = log n bits per synapse and provide functional interpretations for structural plasticity and hippocampal memory replay in the brain (Knoblauch et al., 2014; Butz et al., 2009; Holtmaat and Svoboda, 2009; Ji and Wilson, 2007; Sirota et al., 2003). Other non-linear learning models include the pseudo-inverse rule (Kohonen and Ruohonen, 1972; Diederich and Opper, 1987) and Bayesian heuristics to implement maximum-likelihood retrieval of memory contents (Lansner and Ekeberg, 1989; Kononenko, 1989, 1994; MacKay, 1991; Lansner and Holst, 1996). "
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    ABSTRACT: Neural associative networks are a promising computational paradigm for both for modeling neural circuits of the brain and implementing associative memory and Hebbian cell assemblies in parallel VLSI or nanoscale hardware. Previous work has extensively investigated synaptic learning in linear models of the Hopfield type and simple nonlinear models of the Steinbuch/Willshaw type. Optimized Hopfield networks of size n can store a large number of about [Formula: see text] memories of size k (or associations between them) but require real-valued synapses, which are expensive to implement and can store at most [Formula: see text] bits per synapse. Willshaw networks can store a much smaller number of about [Formula: see text] memories but get along with much cheaper binary synapses. Here I present a learning model employing synapses with discrete synaptic weights. For optimal discretization parameters, this model can store, up to a factor [Formula: see text] close to one, the same number of memories as for optimized Hopfield-type learning-for example, [Formula: see text] for binary synapses, [Formula: see text] for 2 bit (four-state) synapses, [Formula: see text] for 3 bit (8-state) synapses, and [Formula: see text] for 4 bit (16-state) synapses. The model also provides the theoretical framework to determine optimal discretization parameters for computer implementations or brainlike parallel hardware, including structural plasticity. In particular, as recently shown for the Willshaw network, it is possible to store [Formula: see text] bit per computer bit and up to [Formula: see text] bits per nonsilent synapse, whereas the absolute number of stored memories can be much larger than for the Willshaw model.
    No preview · Article · Nov 2015 · Neural Computation
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    • "Experience-driven neural plasticity shapes neural circuits during brain development, but declines after an early postnatal critical period, and only limited plasticity remains in the adult brain (Issa et al. 1999; Grutzendler et al. 2002; Morishita and Hensch 2008; Feldman 2009; Holtmaat and Svoboda 2009; Bavelier et al. 2010). This restricted plasticity limits functional recovery after central nervous system damage in adulthood. "
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    ABSTRACT: Experience-dependent plasticity is limited in the adult brain, and its molecular and cellular mechanisms are poorly understood. Removal of the myelin-inhibiting signaling protein, Nogo receptor (NgR1), restores adult neural plasticity. Here we found that, in NgR1-deficient mice, whisker experience-driven synaptic α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) insertion in the barrel cortex, which is normally complete by 2 weeks after birth, lasts into adulthood. In vivo live imaging by two-photon microscopy revealed more AMPAR on the surface of spines in the adult barrel cortex of NgR1-deficient than on those of wild-type (WT) mice. Furthermore, we observed that whisker stimulation produced new spines in the adult barrel cortex of mutant but not WT mice, and that the newly synthesized spines contained surface AMPAR. These results suggest that Nogo signaling limits plasticity by restricting synaptic AMPAR delivery in coordination with anatomical plasticity.
    Preview · Article · Oct 2015 · Cerebral Cortex
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