Trends in Neurosciences
Volume 37, Issue 10, October 2014, Pages 604-614
Journal home page for Trends in Neurosciences

Feature Review
Special Issue: Circuit Development and Remodeling
Synapse rearrangements upon learning: from divergent–sparse connectivity to dedicated sub-circuits

https://doi.org/10.1016/j.tins.2014.08.011Get rights and content

Highlights

  • Learning involves synapse gains and losses to provide memory traces of learned skills.

  • Synapse rearrangements produce dedicated sub-circuits that support adaptive behavior.

  • Synapse stabilization involves 12–15 hours of cellular and network plasticity reactions.

  • Memory consolidation is coupled to synapse validation and synapse elimination.

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.

Section snippets

From acquisition to memory consolidation

Learning can be associated with the establishment of new synapses that provide long-lasting memory traces of learned knowledge. In support of this notion, repeated imaging studies of spines in situ have provided evidence that sensory adjustments, skill learning, and Pavlovian conditioning induce new synapses and loss of pre-existing synapses 1, 2, 3, 4. Moreover, subsequent behavioral retrieval of the same skill does not induce more new synapses but does induce re-strengthening of previously

Role of connectivity rearrangements upon learning

When considering rearrangements of synaptic connectivity in the adult it is important to clarify the logic and circumstances under which learning leads to circuit remodeling processes. Given that learning occurs as a daily by-product of experience and that flexibility is a core feature of circuit function, it is unlikely that all learning involves structural rearrangements of synaptic connections. Indeed, neuronal circuits can effectively learn from experience through short- and long-term

Time line of synapse rearrangements upon learning

Any learning-related rearrangement of synaptic connections starts with plasticity induced at subsets of pre-existing synapses and neurons at the time of acquisition (Figure 1). At least some of the plasticity must be enhanced through mechanisms linked to incentives in order to distinguish it from less consequential incidental learning. Whether, at acquisition, incentive-driven plasticity occurs at all systems involved in the particular learning process is not well understood. One possibility is

Molecular mechanisms of synapse assembly and stabilization upon learning

Activity- and incentive-related signaling triggers cascades of cellular and molecular plasticity responses that last for >12 hours and induce synaptic growth (1–2 hours), followed by strengthening and stabilization of some newly formed synapses (Figure 1, Figure 2). Growing synaptic functionality and structure are thereby closely linked to synapse retention [48]. Neurotransmitters [59], growth factors [60], and trans-synaptic cell adhesion molecules are involved in initiating synaptic plasticity

Mechanisms of synapse retention and elimination

Several lines of evidence support the notion that most learning-related synapse rearrangements do not lead to detectable long-lasting changes in total synapse numbers 1, 8, 99. For example, new spines were induced in barrel cortex upon whisker trimming, but their stabilization was accompanied by loss of comparable numbers of pre-existing spines [100]. Likewise, spine densities in the hippocampus and cortical areas increased transiently upon learning of the relevant tasks, but they soon returned

Roles of synapse rearrangements involving inhibitory neurons

Unlike excitatory neurons, the role of most inhibitory neuron subpopulations is to modulate and gate the activity of excitatory neurons and their synapses, either directly or through disinhibition 128, 129, 130. Reflecting these profoundly different roles, the functional rationales of synapse rearrangements onto or by inhibitory neurons differ from those of excitatory synapses onto excitatory neurons 130, 131, 132. These considerations might not apply to GABAergic neurons such as cerebellar

Concluding remarks

Recent results from three research areas investigating learning in the adult (operant conditioning in brain–machine–interface studies, rearrangements of synaptic connectivity upon learning, and connectomics) suggest that brain circuits consist to a large extent of distributed, sparse, and non-selective local connectivity, and that learning of new skills involves selective expansion of some of that connectivity to establish specialized local sub-circuits that support adaptive behavior. These

Acknowledgments

We thank Peter Scheiffele (Biozentrum, University of Basel) and Silvia Arber (Biozentrum and FMI, Basel) for valuable input and comments. A.C. was partially supported by the National Center of Competence in Research Synapsy. The Friedrich Miescher Institut is part of and supported by the Novartis Research Foundation.

Glossary

Incentive-driven learning
the term incentive-driven learning is used here to designate learning forms in which a behavioral output is associated with positive or negative reinforcers (reward or punishment). Examples include Pavlovian conditioning, operant conditioning, and trial-and-error reinforced learning forms (e.g., skill learning, song learning, maze learning). Incentive-driven learning involves the assembly of new synapses, whereas incidental learning probably does not.
Incidental learning

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