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Experience-dependent plasticity is a key feature of brain synapses for which neuronal N-Methyl-D-Aspartate receptors (NMDARs) play a major role, from developmental circuit refinement to learning and memory. Astrocytes also express NMDARs, although their exact function has remained controversial. Here, we identify in mouse hippocampus, a circuit function for GluN2C NMDAR, a subtype highly expressed in astrocytes, in layer-specific tuning of synaptic strengths in CA1 pyramidal neurons. Interfering with astrocyte NMDAR or GluN2C NMDAR activity reduces the range of presynaptic strength distribution specifically in the stratum radiatum inputs without an appreciable change in the mean presynaptic strength. Mathematical modeling shows that narrowing of the width of presynaptic release probability distribution compromises the expression of long-term synaptic plasticity. Our findings suggest a novel feedback signaling system that uses astrocyte GluN2C NMDARs to adjust basal synaptic weight distribution of Schaffer collateral inputs, which in turn impacts computations performed by the CA1 pyramidal neuron.

Mounting evidence suggests that synaptic plasticity provides the cellular biological basis of learning and memory, and plasticity deficits play a key role in dementia caused by Alzheimer's disease. However, the mechanisms by which synaptic dysfunction contributes to the pathogenesis of Alzheimer's disease remain unclear. In the present study, Alzheimer's disease transgenic mice were used to determine the relationship between decreased hippocampal synaptic plasticity and pathological changes and cognitive-behavioral deterioration, as well as possible mechanisms underlying decreased synaptic plasticity in the early stages of Alzheimer's disease-like diseases. APP/PS1 double transgenic (5XFAD; Jackson Laboratory) mice and their littermates (wild-type, controls) were used in this study. Additional 6-week-old and 10-week-old 5XFAD mice and wild-type mice were used for electrophysiological recording of hippocampal dentate gyrus. For 10-week-old 5XFAD mice and wild-type mice, the left hippocampus was used for electrophysiological recording, and the right hippocampus was used for biochemical experiments or immunohistochemical staining to observe synaptophysin levels and amyloid beta deposition levels. The results revealed that, compared with wild-type mice, 6-week-old 5XFAD mice exhibited unaltered long-term potentiation in the hippocampal dentate gyrus. Another set of 5XFAD mice began to show attenuation at the age of 10 weeks, and a large quantity of amyloid beta protein was accumulated in hippocampal cells. The location of α-amino-3-hydroxy-5-methylisoxazole-4-propionic acid receptor and N-methyl-D-aspartic acid receptor subunits in synaptosomes was decreased. These findings indicate that the delocalization of postsynaptic glutamate receptors and an associated decline in synaptic plasticity may be key mechanisms in the early onset of Alzheimer's disease. The use and care of animals were in strict accordance with the ethical standards of the Animal Ethics Committee of Capital Medical University, China on December 17, 2015 (approval No. AEEI-2015-182).

Synaptic activity imposes large energy demands that are met by local adenosine triphosphate (ATP) synthesis through glycolysis and mitochondrial oxidative phosphorylation. ATP drives action potentials, supports synapse assembly and remodelling, and fuels synaptic vesicle filling and recycling, thus sustaining synaptic transmission. Given their polarized morphological features — including long axons and extensive branching in their terminal regions — neurons face exceptional challenges in maintaining presynaptic energy homeostasis, particularly during intensive synaptic activity. Recent studies have started to uncover the mechanisms and signalling pathways involved in activity-dependent and energy-sensitive regulation of presynaptic energetics, or ‘synaptoenergetics’. These conceptual advances have established the energetic regulation of synaptic efficacy and plasticity as an exciting research field that is relevant to a range of neurological disorders associated with bioenergetic failure and synaptic dysfunction.Numerous energy-demanding cellular processes contribute to synaptic activity and function. Li and Sheng describe the mechanisms that regulate presynaptic energy supply to ensure that neurons can meet these demands and maintain their functions during periods of intensive synaptic activity.

The ability to form memory is an essential trait that allows learning and the accumulation of knowledge. But what is a memory? There has been a long history of searching for the neuronal substrate that forms memory in the brain, and the emerging view is that ensembles of engram cells explain how memories are formed and retrieved. In a Review, Josselyn and Tonegawa discuss the evidence for engram cells as a substrate of memory, particularly in rodents; what we have learned so far about the features of memory, including memory formation, retrieval over time, and loss; and future directions to understand how memory becomes knowledge. Science , this issue p. eaaw4325 In 1904, Richard Semon introduced the term “engram” to describe the neural substrate for storing memories. An experience, Semon proposed, activates a subset of cells that undergo off-line, persistent chemical and/or physical changes to become an engram. Subsequent reactivation of this engram induces memory retrieval. Although Semon’s contributions were largely ignored in his lifetime, new technologies that allow researchers to image and manipulate the brain at the level of individual neurons has reinvigorated engram research. We review recent progress in studying engrams, including an evaluation of evidence for the existence of engrams, the importance of intrinsic excitability and synaptic plasticity in engrams, and the lifetime of an engram. Together, these findings are beginning to define an engram as the basic unit of memory.

Glutamatergic synapses display variable strength and diverse short-term plasticity (STP), even for a given type of connection. Using nonnegative tensor factorization and conventional state modeling, we demonstrate that a kinetic scheme consisting of two sequential and reversible steps of release–machinery assembly and a final step of synaptic vesicle (SV) fusion reproduces STP and its diversity among synapses. Analyzing transmission at the calyx of Held synapses reveals that differences in synaptic strength and STP are not primarily caused by variable fusion probability (pfusion ) but are determined by the fraction of docked synaptic vesicles equipped with a mature release machinery. Our simulations show that traditional quantal analysis methods do not necessarily report pfusion of SVs with a mature release machinery but reflect both pfusion and the distribution between mature and immature priming states at rest. Thus, the approach holds promise for a better mechanistic dissection of the roles of presynaptic proteins in the sequence of SV docking, two-step priming, and fusion. It suggests a mechanism for activity-induced redistribution of synaptic efficacy.

Synapses are often strengthened during wake periods and thus need to be homeostatically readjusted during sleep. During slow-wave sleep, synaptic depression is dominant. Sharp wave and ripple events are transient high-frequency field oscillations that occur spontaneously during slow-wave sleep in the brain. Norimoto et al. found that these events induced long-term depression of hippocampal synapses and may thus help to refine recently acquired memories (see the Perspective by Draguhn). Science , this issue p. 1524 ; see also p. 1461 Sharp-wave ripple events in slow-wave sleep induce long-term depression at hippocampal synapses in sleeping mice. The specific effects of sleep on synaptic plasticity remain unclear. We report that mouse hippocampal sharp-wave ripple oscillations serve as intrinsic events that trigger long-lasting synaptic depression. Silencing of sharp-wave ripples during slow-wave states prevented the spontaneous down-regulation of net synaptic weights and impaired the learning of new memories. The synaptic down-regulation was dependent on the N -methyl- d -aspartate receptor and selective for a specific input pathway. Thus, our findings are consistent with the role of slow-wave states in refining memory engrams by reducing recent memory-irrelevant neuronal activity and suggest a previously unrecognized function for sharp-wave ripples.

Activity-dependent synaptic plasticity is a ubiquitous property of the nervous system that allows neurons to communicate and change their connections as a function of past experiences. Through reweighting of synaptic strengths, the nervous system can remodel itself, giving rise to durable memories that create the biological basis for mental function. In healthy individuals, synaptic plasticity undergoes characteristic developmental and aging trajectories. Dysfunctional plasticity, in turn, underlies a wide spectrum of neuropsychiatric disorders including depression, schizophrenia, addiction, and posttraumatic stress disorder. From a mechanistic standpoint, synaptic plasticity spans the gamut of spatial and temporal scales, from microseconds to the lifespan, from microns to the entire nervous system. With the numbers and strengths of synapses changing on such wide scales, there is an important need to develop measurement techniques with complimentary sensitivities and a growing number of approaches are now being harnessed for this purpose. Through hemodynamic measures, structural and tracer imaging, and noninvasive neuromodulation, it is possible to image structural and functional changes that underlie synaptic plasticity and associated behavioral learning. Here we review the mechanisms of neural plasticity and the historical and future trends in techniques that allow imaging of synaptic changes that accompany psychiatric disorders, highlighting emerging therapeutics and the challenges and opportunities accompanying this burgeoning area of study.

Neural networks based on memristive devices 1 – 3 have the ability to improve throughput and energy efficiency for machine learning 4 , 5 and artificial intelligence 6 , especially in edge applications 7 – 21 . Because training a neural network model from scratch is costly in terms of hardware resources, time and energy, it is impractical to do it individually on billions of memristive neural networks distributed at the edge. A practical approach would be to download the synaptic weights obtained from the cloud training and program them directly into memristors for the commercialization of edge applications. Some post-tuning in memristor conductance could be done afterwards or during applications to adapt to specific situations. Therefore, in neural network applications, memristors require high-precision programmability to guarantee uniform and accurate performance across a large number of memristive networks 22 – 28 . This requires many distinguishable conductance levels on each memristive device, not only laboratory-made devices but also devices fabricated in factories. Analog memristors with many conductance states also benefit other applications, such as neural network training, scientific computing and even ‘mortal computing’ 25 , 29 , 30 . Here we report 2,048 conductance levels achieved with memristors in fully integrated chips with 256 × 256 memristor arrays monolithically integrated on complementary metal–oxide–semiconductor (CMOS) circuits in a commercial foundry. We have identified the underlying physics that previously limited the number of conductance levels that could be achieved in memristors and developed electrical operation protocols to avoid such limitations. These results provide insights into the fundamental understanding of the microscopic picture of memristive switching as well as approaches to enable high-precision memristors for various applications. Chips with 256 × 256 memristor arrays that were monolithically integrated on complementary metal–oxide–semiconductor (CMOS) circuits in a commercial foundry achieved 2,048 conductance levels in individual memristors.

Dendritic spines, the postsynaptic compartments of excitatory neurotransmission, have different shapes classified from ‘stubby’ to ‘mushroom-like’. Whereas mushroom spines are essential for adult brain function, stubby spines disappear during brain maturation. It is still unclear whether and how they differ in protein composition. To address this, we combined electron microscopy and quantitative biochemistry with super-resolution microscopy to annotate more than 47,000 spines for more than 100 synaptic targets. Surprisingly, mushroom and stubby spines have similar average protein copy numbers and topologies. However, an analysis of the correlation of each protein to the postsynaptic density mass, used as a marker of synaptic strength, showed substantially more significant results for the mushroom spines. Secretion and trafficking proteins correlated particularly poorly to the strength of stubby spines. This suggests that stubby spines are less likely to adequately respond to dynamic changes in synaptic transmission than mushroom spines, which possibly explains their loss during brain maturation. This work provides a first molecular view of dendritic spines, for both the mushroom and stubby classes, obtained by integrating electron microscopy, quantitative biochemistry, super-resolution microscopy and 3D molecular visualizations.

Learning is primarily mediated by activity-dependent modifications of synaptic strength within neuronal circuits. We discovered that place fields in hippocampal area CA1 are produced by a synaptic potentiation notably different from Hebbian plasticity. Place fields could be produced in vivo in a single trial by potentiation of input that arrived seconds before and after complex spiking. The potentiated synaptic input was not initially coincident with action potentials or depolarization. This rule, named behavioral time scale synaptic plasticity, abruptly modifies inputs that were neither causal nor close in time to postsynaptic activation. In slices, five pairings of subthreshold presynaptic activity and calcium (Ca2+) plateau potentials produced a large potentiation with an asymmetric seconds-long time course. This plasticity efficiently stores entire behavioral sequences within synaptic weights to produce predictive place cell activity.