Brain-derived neurotrophic factor (BDNF) plays an important role in hippocampus-dependent learning and memory. Canonically, this has been ascribed to an enhancing effect on neuronal excitability and synaptic plasticity in the CA1 region. However, it is the pyramidal neurons in the subiculum that form the primary efferent pathways conveying hippocampal information to other areas of the brain, and yet the effect of BDNF on these neurons has remained unexplored. We present new data that BDNF regulates neuronal excitability and cellular plasticity in a much more complex manner than previously suggested. Subicular pyramidal neurons can be divided into two major classes, which have different electrophysiological and morphological properties, different requirements for the induction of plasticity and different extra-hippocampal projections. We found that BDNF increases excitability in one class of subicular pyramidal neurons, yet decreases excitability of the other class. Further, while endogenous BDNF was necessary for the induction of synaptic plasticity in both cell types, BDNF enhanced intrinsic plasticity in one class of pyramidal neurons, yet suppressed intrinsic plasticity in the other. Taken together, these data suggest a novel role for BDNF signaling, as it appears to dynamically and bidirectionally regulate the output of hippocampal information to different regions of the brain.

Clarifying gene expression in narrowly defined neuronal populations can provide insight into cellular identity, computation, and functionality. Here, we used next-generation RNA sequencing (RNA-seq) to produce a quantitative, whole genome characterization of gene expression for the major excitatory neuronal classes of the hippocampus; namely, granule cells and mossy cells of the dentate gyrus, and pyramidal cells of areas CA3, CA2, and CA1. Moreover, for the canonical cell classes of the trisynaptic loop, we profiled transcriptomes at both dorsal and ventral poles, producing a cell-class- and region-specific transcriptional description for these populations. This dataset clarifies the transcriptional properties and identities of lesser-known cell classes, and moreover reveals unexpected variation in the trisynaptic loop across the dorsal-ventral axis. We have created a public resource, Hipposeq (http://hipposeq.janelia.org), which provides analysis and visualization of these data and will act as a roadmap relating molecules to cells, circuits, and computation in the hippocampus.

Neuronal circuit function is governed by precise patterns of connectivity between specialized groups of neurons. The diversity of GABAergic interneurons is a hallmark of cortical circuits, yet little is known about their targeting to individual postsynaptic dendrites. We examined synaptic connectivity between molecularly defined inhibitory interneurons and CA1 pyramidal cell dendrites using correlative light-electron microscopy and large-volume array tomography. We show that interneurons can be highly selective in their connectivity to specific dendritic branch types and, furthermore, exhibit precisely targeted connectivity to the origin or end of individual branches. Computational simulations indicate that the observed subcellular targeting enables control over the nonlinear integration of synaptic input or the initiation and backpropagation of action potentials in a branch-selective manner. Our results demonstrate that connectivity between interneurons and pyramidal cell dendrites is more precise and spatially segregated than previously appreciated, which may be a critical determinant of how inhibition shapes dendritic computation.

Tissue and organ function has been conventionally understood in terms of the interactions among discrete and homogeneous cell types. This approach has proven difficult in neuroscience due to the marked diversity across different neuron classes, but it may be further hampered by prominent within-class variability. Here, we considered a well-defined canonical neuronal population-hippocampal CA1 pyramidal cells (CA1 PCs)-and systematically examined the extent and spatial rules of transcriptional heterogeneity. Using next-generation RNA sequencing, we identified striking variability in CA1 PCs, such that the differences within CA1 along the dorsal-ventral axis rivaled differences across distinct pyramidal neuron classes. This variability emerged from a spectrum of continuous gene-expression gradients, producing a transcriptional profile consistent with a multifarious continuum of cells. This work reveals an unexpected amount of variability within a canonical and narrowly defined neuronal population and suggests that continuous, within-class heterogeneity may be an important feature of neural circuits.

Dendritic integration of synaptic inputs mediates rapid neural computation as well as longer-lasting plasticity. Several channel types can mediate dendritically initiated spikes (dSpikes), which may impact information processing and storage across multiple timescales; however, the roles of different channels in the rapid vs long-term effects of dSpikes are unknown. We show here that dSpikes mediated by Nav channels (blocked by a low concentration of TTX) are required for long-term potentiation (LTP) in the distal apical dendrites of hippocampal pyramidal neurons. Furthermore, imaging, simulations, and buffering experiments all support a model whereby fast Nav channel-mediated dSpikes (Na-dSpikes) contribute to LTP induction by promoting large, transient, localized increases in intracellular calcium concentration near the calcium-conducting pores of NMDAR and L-type Cav channels. Thus, in addition to contributing to rapid neural processing, Na-dSpikes are likely to contribute to memory formation via their role in long-lasting synaptic plasticity.

Understanding how individual neurons integrate the thousands of synaptic inputs they receive is critical to understanding how the brain works. Modeling studies in silico and experimental work in vitro, dating back more than half a century, have revealed that neurons can perform a variety of different passive and active forms of synaptic integration on their inputs. But how are synaptic inputs integrated in the intact brain? With the development of new techniques, this question has recently received substantial attention, with new findings suggesting that many of the forms of synaptic integration observed in vitro also occur in vivo, including in awake animals. Here we review six decades of progress, which collectively highlights the complex ways that single neurons integrate their inputs, emphasizing the critical role of dendrites in information processing in the brain.

Spatial and temporal features of synaptic inputs engage integration mechanisms on multiple scales, including presynaptic release sites, postsynaptic dendrites, and networks of inhibitory interneurons. Here we investigate how these mechanisms cooperate to filter synaptic input in hippocampal area CA1. Dendritic recordings from CA1 pyramidal neurons reveal that proximal inputs from CA3 as well as distal inputs from entorhinal cortex layer III (ECIII) sum sublinearly or linearly at low firing rates due to feedforward inhibition, but sum supralinearly at high firing rates due to synaptic facilitation, producing a high-pass filter. However, during ECIII and CA3 input comparison, supralinear dendritic integration is dynamically balanced by feedforward and feedback inhibition, resulting in suppression of dendritic complex spiking. We find that a particular subpopulation of CA1 interneurons expressing neuropeptide Y (NPY) contributes prominently to this dynamic filter by integrating both ECIII and CA3 input pathways and potently inhibiting CA1 pyramidal neuron dendrites.

Understanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and standardization to provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons.

Understanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and standardization to provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons.

Proper brain function requires the precise localization of proteins and signaling molecules on a nanometer scale. The examination of molecular organization at this scale has been difficult in part because it is beyond the reach of conventional, diffraction-limited light microscopy. The recently developed method of superresolution, localization-based fluorescent microscopy (LBM), such as photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM), has demonstrated a resolving power at a 10 nm scale and is poised to become a vital tool in modern neuroscience research. Indeed, LBM has revealed previously unknown cellular architectures and organizational principles in neurons. Here, we discuss the principles of LBM, its current applications in neuroscience, and the challenges that must be met before its full potential is achieved. We also present the unpublished results of our own experiments to establish a sample preparation procedure for applying LBM to study brain tissue. Synapse, 69:283-294, 2015. © 2015 Wiley Periodicals, Inc.

We describe an engineered family of highly antigenic molecules based on GFP-like fluorescent proteins. These molecules contain numerous copies of peptide epitopes and simultaneously bind IgG antibodies at each location. These 'spaghetti monster' fluorescent proteins (smFPs) distributed well in neurons, notably into small dendrites, spines and axons. smFP immunolabeling localized weakly expressed proteins not well resolved with traditional epitope tags. By varying epitope and scaffold, we generated a diverse family of mutually orthogonal antigens. In cultured neurons and mouse and fly brains, smFP probes allowed robust, orthogonal multicolor visualization of proteins, cell populations and neuropil. smFP variants complement existing tracers and greatly increase the number of simultaneous imaging channels, and they performed well in advanced preparations such as array tomography, super-resolution fluorescence imaging and electron microscopy. In living cells, the probes improved single-molecule image tracking and increased yield for RNA-seq. These probes facilitate new experiments in connectomics, transcriptomics and protein localization.