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Specific types of inhibitory neurons exhibit prolonged, high-frequency barrages of action potentials. Here, the authors show that astrocytes might mediate such barrage firing.

Bloss et al. show single axons form clustered inputs onto the dendrites of hippocampal pyramidal cells in a projection-specific manner. The spatial and temporal features inherent in these connections efficiently drive dendritic depolarization.

The authors identify a CA3 new pyramidal cell type with unique morphofunctional characteristics and distinct synaptic inputs and describe how these cells contribute to sharp-wave synchronization events, which are vital to hippocampal memory function.

Insight into the algorithmic form and learning principles underlying cognitive maps in the hippocampus is provided.

The unique dendritic morphology of pyramidal neurons is likely to have an impact on their function. Spruston discusses how the properties of these neurons' distinct dendritic domains might contribute to their integration of synaptic inputs.

The authors report that, in a subset of rodent hippocampal and neocortical interneurons, evoking hundreds of spikes at normal rates caused persistent firing that outlasted the stimulus by about a minute. Persistent firing was generated in the distal axon, did not require somatic depolarization and could be shared between interneurons via an axo-axonal interaction.

To store information, the brain modulates synapses, which mediate communication between neurons. A closer look hints that subcellular changes in response to groups of synapses facilitate this process.

Place cell responses are strongly modulated by context, allowing the hippocampus to effectively encode different environments. Electrical recordings show that the context-dependent representation of environmental features is present in the CA3 subregion and is inherited by CA1.

Although voltage-clamp recordings remain a favorite method for studying synaptic transmission, the space-clamp problems that are associated with somatic voltage-clamp recordings have never been directly measured. A study by Williams and Mitchell in this issue now measures the experimental errors associated with this technique.

Researchers developed DELTA, a method for brain-wide measurement of synaptic protein turnover with single-synapse resolution, providing a powerful tool to localize and study mechanisms underlying synaptic plasticity and learning.

It is now clear that most classical ‘cell types’ are composed of collections of cells with heterogeneous features. Cembrowski and Spruston describe the heterogeneity of hippocampal pyramidal cells and argue that these cells can act as a model of within-cell-type heterogeneity in the brain.

The authors derive a neural network theory of systems consolidation to assess why some memories consolidate more than others. They propose that brains regulate consolidation to optimize generalization, so only predictable memory components consolidate.

By showing that synaptic strength increases as a function of distance from the soma, Magee and Cook have solved the long-standing puzzle of how synapses on distal dendrites can influence action potential initiation.

Synaptotagmin-3 is identified as the presynaptic high-affinity calcium sensor to rapidly replenish synaptic vesicles to maintain steady synaptic transmission.

Pyramidal neurons integrate synaptic inputs arriving on a structurally and functionally complex dendritic tree that has nonlinear responses. A study in this issue shows that nonlinear computation occurs in individual dendritic branches, and suggests a possible approach to building neural network models directly connected to the behavior of real neurons and synapses.

Synaptic long-term potentiation and depression are determined by the frequency and timing of coactivated synapses. A new model explains many experimental plasticity observations and allows new predictions about neural circuit function.

For decades, researchers have wondered whether algorithms used by artificial neural networks might be implemented by biological networks. Payeur et al. have strengthened the connection between neuroscience and artificial intelligence by showing that biologically plausible mechanisms can approximate key features of an essential artificial intelligence learning algorithm.

Genetically encoded voltage indicators (GEVIs) allow visualisation of fast action potentials in neurons but most are bright at rest and dimmer during an action potential. Here, the authors engineer electrochromic FRET GEVIs with fast, bright and positive-going fluorescence signals for in vivo imaging.

Long-term potentiation and long-term depression require postsynaptic depolarization, which many current models attribute to backpropagating action potentials. New experimental work suggests, however, that other mechanisms can lead to dendritic depolarization, and that backpropagating action potentials may be neither necessary nor sufficient for synaptic plasticity in vivo.

Dendritic spines operate as high-impedance input structures that amplify local synaptic depolarization to enhance electrical interaction among coactive inputs.

Neurons receive synaptic input primarily onto their dendrites. This review focuses on how synaptic inputs are integrated by dendrites, with an emphasis on recent work in the intact brain. It describes the range of computations dendrites perform on their inputs, highlighting their critical role in information processing in the brain.