浦久保 秀俊

This study developed a three-dimensional ultrastructural analysis application using serial block-face scanning electron microscopy (SBF-SEM) to investigate surgically acquired human skin tissues containing the arrector pili muscle. We utilized the en bloc staining, including reduced osmium, thiocarbohydrazide, and lead aspartate, as well as the embedding using a carbon-based conductive resin. Next, we obtained serial images with SBF-SEM. The results revealed dense nerve fiber networks branching from nearby nerve fiber bundles outside the muscle and running among muscle fibers. Additionally, the dense nerve network running through and along arrector pili muscle fibers rarely penetrates the connective tissues between smooth muscle fibers and epithelial cells. Furthermore, in the observation area, no individual smooth muscle fibers formed adhesion structures with the epithelial cells of the hair follicle, ending in the dermal extracellular matrix near the epithelial cells. These results indicate the usefulness of this approach for three-dimensional ultrastructural analyses of human skin tissues comprising follicular units and revealing structural changes in skin tissues, especially the arrector pili muscle and nerve fibers with hair follicular epithelium, in aging and diseased conditions.

Precise information on synapse organization in a dendrite is crucial to understanding the mechanisms underlying voltage integration and the variability in the strength of synaptic inputs across dendrites of different complex morphologies. Here, we used focused ion beam/scanning electron microscope (FIB/SEM) to image the dendritic spines of mice in the hippocampal CA1 region, CA3 region, somatosensory cortex, striatum, and cerebellum (CB). Our results show that the spine geometry and dimensions differ across neuronal cell types. Despite this difference, dendritic spines were organized in an orchestrated manner such that the postsynaptic density (PSD) area per unit length of dendrite scaled positively with the dendritic diameter in CA1 proximal stratum radiatum (PSR), cortex and CB. The ratio of the PSD area to neck length was kept relatively uniform across dendrites of different diameters in CA1 PSR. Computer simulation suggests that a similar level of synaptic strength across different dendrites in CA1 PSR enables the effective transfer of synaptic inputs from the dendrites towards soma. Excitatory postsynaptic potentials (EPSPs), evoked at single spines by glutamate uncaging and recorded at the soma, show that the neck length is more influential than head width in regulating the EPSP magnitude at the soma. Our study describes thorough morphological features and the organizational principles of dendritic spines in different brain regions.Significance statement Little is known about the characteristic anatomical features underlying the organization of spine synapses in a dendrite. This study used volume electron microscopy to make an extensive characterization of dendritic spine synapses in multiple regions of the mouse brain to uncover the principles underlying their placement along a dendritic shaft. By using a combination of approaches such as two-photon imaging, glutamate uncaging, electrophysiology, and computer simulation, we reveal the functional importance of regulated spine placement along a dendritic trunk. Our research presents a crucial step in understanding the synaptic computational principle in dendrites by highlighting the generalizable features of dendritic spine organization in a neuron.

Animals remember temporal links between their actions and subsequent rewards. We previously discovered a synaptic mechanism underlying such reward learning in D1 receptor (D1R)-expressing spiny projection neurons (D1 SPN) of the striatum. Dopamine (DA) bursts promote dendritic spine enlargement in a time window of only a few seconds after paired pre- and post-synaptic spiking (pre-post pairing), which is termed as reinforcement plasticity (RP). The previous study has also identified underlying signaling pathways; however, it still remains unclear how the signaling dynamics results in RP. In the present study, we first developed a computational model of signaling dynamics of D1 SPNs. The D1 RP model successfully reproduced experimentally observed protein kinase A (PKA) activity, including its critical time window. In this model, adenylate cyclase type 1 (AC1) in the spines/thin dendrites played a pivotal role as a coincidence detector against pre-post pairing and DA burst. In particular, pre-post pairing (Ca2+ signal) stimulated AC1 with a delay, and the Ca2+-stimulated AC1 was activated by the DA burst for the asymmetric time window. Moreover, the smallness of the spines/thin dendrites is crucial to the short time window for the PKA activity. We then developed a RP model for D2 SPNs, which also predicted the critical time window for RP that depended on the timing of pre-post pairing and phasic DA dip. AC1 worked for the coincidence detector in the D2 RP model as well. We further simulated the signaling pathway leading to Ca2+/calmodulin-dependent protein kinase II (CaMKII) activation and clarified the role of the downstream molecules of AC1 as the integrators that turn transient input signals into persistent spine enlargement. Finally, we discuss how such timing windows guide animals' reward learning.

Recently, there has been rapid expansion in the field of micro-connectomics, which targets the three-dimensional (3D) reconstruction of neuronal networks from stacks of two-dimensional (2D) electron microscopy (EM) images. The spatial scale of the 3D reconstruction increases rapidly owing to deep convolutional neural networks (CNNs) that enable automated image segmentation. Several research teams have developed their own software pipelines for CNN-based segmentation. However, the complexity of such pipelines makes their use difficult even for computer experts and impossible for non-experts. In this study, we developed a new software program, called UNI-EM, for 2D and 3D CNN-based segmentation. UNI-EM is a software collection for CNN-based EM image segmentation, including ground truth generation, training, inference, postprocessing, proofreading, and visualization. UNI-EM incorporates a set of 2D CNNs, i.e., U-Net, ResNet, HighwayNet, and DenseNet. We further wrapped flood-filling networks (FFNs) as a representative 3D CNN-based neuron segmentation algorithm. The 2D- and 3D-CNNs are known to demonstrate state-of-the-art level segmentation performance. We then provided two example workflows: mitochondria segmentation using a 2D CNN and neuron segmentation using FFNs. By following these example workflows, users can benefit from CNN-based segmentation without possessing knowledge of Python programming or CNN frameworks.

See also: https://youtu.be/GWVtrtwNovw

Human observers perceive illusory rotations after the disappearance of circularly repeating patches containing dark-to-light luminance. This afterimage rotation is a very powerful phenomenon, but little is known about the mechanisms underlying it. Here, we use a computational model to show that the afterimage rotation can be explained by a combination of fast light adaptation and the physiological architecture of the early visual system, consisting of ON-and OFF-type visual pathways. In this retinal ON/OFF model, the afterimage rotation appeared as a rotation of focus lines of retinal ON/OFF responses. Focus lines rotated clockwise on a light background, but counterclockwise on a dark background. These findings were consistent with the results of psychophysical experiments, which were also performed by us. Additionally, the velocity of the afterimage rotation was comparable with that observed in our psychophysical experiments. These results suggest that the early visual system (including the retina) is responsible for the generation of the afterimage rotation, and that this illusory rotation may be systematically misinterpreted by our high-level visual system.

Crosstalk between neurons and glia may constitute a significant part of information processing in the brain. We present a novel method of statistically identifying interactions in a neuron-glia network. We attempted to identify neuron-glia interactions from neuronal and glial activities via maximum-a-posteriori (MAP)-based parameter estimation by developing a generalized linear model (GLM) of a neuron-glia network. The interactions in our interest included functional connectivity and response functions. We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections. We only accepted addition or removal when the modification improved the cross-validated likelihood. We applied the method to a high-throughput, multicellular in vitro Ca2+ imaging dataset obtained from the CA3 region of a rat hippocampus, and then evaluated the reliability of connectivity estimates using a statistical test based on a surrogate method. Our findings based on the estimated connectivity were in good agreement with currently available physiological knowledge, suggesting our method can elucidate undiscovered functions of neuron-glia systems.

Animal behaviors are reinforced by subsequent rewards following within a narrow time window. Such reward signals are primarily coded by dopamine, which modulates the synaptic connections of medium spiny neurons in the striatum. The mechanisms of the narrow timing detection, however, remain unknown. Here, we optically stimulated dopaminergic and glutamatergic inputs separately and found that dopamine promoted spine enlargement only during a narrow time window (0.3 to 2 seconds) after the glutamatergic inputs. The temporal contingency was detected by rapid regulation of adenosine 3',5'-cyclic monophosphate in thin distal dendrites, in which protein-kinase A was activated only within the time window because of a high phosphodiesterase activity. Thus, we describe a molecular basis of reinforcement plasticity at the level of single dendritic spines.

A dendritic spine is a very small structure (similar to 0.1 mu m(3)) of a neuron that processes input timing information. Why are spines so small? Here, we provide functional reasons; the size of spines is optimal for information coding. Spines code input timing information by the probability of Ca2+ increases, which makes robust and sensitive information coding possible. We created a stochastic simulation model of input timing-dependent Ca2+ increases in a cerebellar Purkinje cell's spine. Spines used probability coding of Ca2+ increases rather than amplitude coding for input timing detection via stochastic facilitation by utilizing the small number of molecules in a spine volume, where information per volume appeared optimal. Probability coding of Ca2+ increases in a spine volume was more robust against input fluctuation and more sensitive to input numbers than amplitude coding of Ca2+ increases in a cell volume. Thus, stochasticity is a strategy by which neurons robustly and sensitively code information.

Ca2+/Calrnodulin-dependent protein kinase II (CaMKII) has been shown to play a major role in establishing memories through complex molecular interactions including phosphorylation of multiple synaptic targets. However, it is still controversial whether CaMKII itself serves as a molecular memory because of a lack of direct evidence. Here, we show that a single holoenzyme of CaMKII per se serves as an erasable molecular memory switch. We reconstituted Ca2+/Calmodulin-dependent CaMKII autophosphorylation in the presence of protein phosphatase 1 in vitro, and found that CaMKII phosphorylation shows a switch-like response with history dependence (hysteresis) only in the presence of an N-methyl-D-aspartate receptor-derived peptide. This hysteresis is Ca2+ and protein phosphatase 1 concentration-dependent, indicating that the CaMKII memory switch is not simply caused by an N-methyl-D-aspartate receptor-derived peptide lock of CaMKII in an active conformation. Mutation of a phosphorylation site of the peptide shifted the Ca2+ range of hysteresis. These functions may be crucial for induction and maintenance of long-term synaptic plasticity at hippocampal synapses.

Cerebellar long-term depression (LTD) and cortical spike-timing-dependent synaptic plasticity (STDP) are two well-known and well-characterized types of synaptic plasticity. Induction of both types of synaptic plasticity depends on the spike timing, pairing frequency, and pairing numbers of two different sources of spiking. This implies that the induction of synaptic plasticity may share common frameworks in terms of signal processing regardless of the different signaling pathways involved in the two types of synaptic plasticity. Here we propose that both types share common frameworks of signal processing for spike-timing, pairing-frequency, and pairing-numbers detection. We developed system models of both types of synaptic plasticity and analyzed signal processing in the induction of synaptic plasticity. We found that both systems have upstream subsystems for spike-timing detection and downstream subsystems for pairing-frequency and pairing-numbers detection. The upstream systems used multiplication of signals from the feedback filters and nonlinear functions for spike-timing detection. The downstream subsystems used temporal filters with longer time constants for pairing-frequency detection and nonlinear switch-like functions for pairing-numbers detection, indicating that the downstream subsystems serve as a leaky integrate-and-fire system. Thus, our findings suggest that a common conceptual framework for the induction of synaptic plasticity exists despite the differences in molecular species and pathways. © 2013 Elsevier Ltd.

The development of direction selectivity in the visual system depends on visual experience. In the developing Xenopus retinotectal system, tectal neurons (TNs) become direction selective through spike timing-dependent plasticity (STDP) after repetitive retinal exposure to a moving bar in a specific direction. We investigated the mechanism responsible for the development of direction selectivity in the Xenopus retinotectal system using a neural circuit model with STDP. In this retinotectal circuit model, a moving bar stimulated the retinal ganglion cells (RGCs), which provided feedforward excitation to the TNs and interneurons (INs). The INs provided delayed feedforward inhibition to the TNs. The TNs also received feedback excitation from neighboring TNs. As a synaptic learning rule, a molecular STDP model was used for synapses between the RGCs and TNs. The retinotectal circuit model reproduced experimentally observed features of the development of direction selectivity, such as increase in input to the TN. The peak of feedforward excitation from RGCs to TNs shifted earlier as a result of STDP. Together with the delayed feedforward inhibition, a stronger earlier transient feedforward signal was generated, which exceeded the threshold of the feedback excitation from the neighboring TNs and resulted in amplification of input to the TN. The suppression of the delayed feedforward inhibition resulted in the development of orientation selectivity rather than direction selectivity, indicating the pivotal role of the delayed feedforward inhibition in direction selectivity. We propose a mechanism for the development of direction selectivity involving a delayed feedforward inhibition with STDP and the amplification of feedback excitation.

STDP (spike-timing-dependent synaptic plasticity) is thought to be a synaptic learning rule that embeds spike-timing information into a specific pattern of synaptic strengths in neuronal circuits, resulting in a memory. STDP consists of bidirectional long-term changes in synaptic strengths. This process includes long-term potentiation and long-term depression, which are dependent on the timing of presynaptic and postsynaptic spikings. In this review, we focus on computational aspects of signaling mechanisms that induce and maintain STDP as a key step toward the definition of a general synaptic learning rule. In addition, we discuss the temporal and spatial aspects of STDP, and the requirement of a homeostatic mechanism of STDP in vivo. [DOI: 10.2976/1.3137602]

Spike timing-dependent synaptic plasticity (STDP) plays an important role in neural development and information processing in the brain; however, the mechanism by which spike timing information is encoded into STDP remains unclear. Here, we show that a novel allosteric kinetics of NMDA receptors (NMDARs) is required for STDP. We developed a detailed biophysical model of STDP and found that the model required spike timing-dependent distinct suppression of NMDARs by Ca2(+)-calmodulin. This led us to predict an allosteric kinetics of NMDARs: a slow and rapid suppression of NMDARs by Ca2(+) -calmodulin with prespiking -> postspiking and postspiking -> prespiking, respectively. We found that the allosteric kinetics, but not the conventional kinetics, is consistent with specific features of amplitudes and peak time of NMDAR-mediated EPSPs in experiments. We found that the allosteric kinetics of NMDARs was also valid for synaptic plasticity induced by more complex spike trains in layer II/III of visual cortex. We extracted an essential synaptic learning rule by reduction of the allosteric STDP model and found that spike timing-dependent bidirectional role of postspiking in synaptic modification, which depends on the allosteric kinetics, is the essential principle in STDP. Thus, we propose a simple hypothesis of the allosteric kinetics of NMDARs that can coherently explain critical features of spike timing-dependent NMDAR-mediated EPSPs and synaptic plasticity.

The propagation of highly synchronous firings across neuronal networks, called the synfire chain, has been actively studied both theoretically and experimentally. The temporal accuracy and remarkable stability of the propagation have been repeatedly examined in previous studies. However, for such a mode of signal transduction to play a major role in processing information in the brain, the propagation should also be controlled dynamically and flexibly. Here, we show that inhibitory but not excitatory input can bidirectionally modulate the propagation, i.e., enhance or suppress the synchronous firings depending on the timing of the input. Our simulations based on the Hodgkin-Huxley neuron model demonstrate this bidirectional modulation and suggest that it should be achieved with any biologically inspired modeling. Our finding may help describe a concrete scenario of how multiple synfire chains lying in a neuronal network are appropriately controlled to perform significant information processing.

Spike timing-dependent synaptic plasticity (STDP) develops neural circuits through close interaction with the firing function of single neurons. Here, we investigate the neuronal firing function dependency of STDP about a single-compartment Hodgkin-Huxley (HH) model neuron and a leaky integrate-and-fire (LIF) model neuron. Computer simulation shows that the development of synaptic strength by synaptic input pairs with constant intervals depends on the firing function of the models. The more organized packet inputs also develop synaptic strength in a firing function-dependent manner, to realize specific information processing. These results suggest that in real biological systems too, STDP with the same time window will develop different neural circuits, depending on the neuronal firing functions. © 2006 Wiley Periodicals. Inc.

Sustained contraction of cells depends on sustained Rho-associated kinase (Rho-kinase) activation. We developed a computational model of the Rho-kinase pathway to understand the systems characteristics. Thrombin-dependent in vivo transient responses of Rho activation and Ca2+ increase could be reproduced in silico. Low and high thrombin stimulation induced transient and sustained phosphorylation, respectively, of myosin light chain (MLC) and myosin phosphatase targeting subunit 1 (MYPT1) in vivo. The transient phosphorylation of MLC and MYPT1 could be reproduced in silico, but their sustained phosphorylation could not. This discrepancy between in vivo and in silico in the sustained responses downstream of Rho-kinase indicates that a missing pathway(s) may be responsible for the sustained Rho-kinase activation. We found, experimentally, that the sustained phosphorylation of MLC and MYPT1 exhibit all-or-none responses. Bromoenol lactone, a specific inhibitor of Ca2+-independent phospholipase A2 (iPLA2), inhibited sustained phosphorylation of MLC and MYPT1, which indicates that sustained Rho-kinase activation requires iPLA2 activity. Thus, the systems analysis of the Rho-kinase pathway identified a novel iPLA2-dependent mechanism of the sustained Rho-kinase activation, which exhibits an all-or-none response.

Although the supralinear summation of synchronizing excitatory postsynaptic potentials (EPSPs) and backpropagating action potentials (APs) is important for spike-timing-dependent synaptic plasticity (STDP), the spatial conditions of the amplification in the divergent dendritic structure have yet to be analyzed. In the present study, we simulated the coincidence of APs with EPSPs at randomly determined synaptic sites of a morphologically reconstructed hippocampal CA1 pyramidal model neuron and clarified the spatial condition of the amplifying synapses. In the case of uniform conductance inputs, the amplifying synapses were localized in the middle apical dendrites and distal basal dendrites with small diameters, and the ratio of synapses was unexpectedly small: 8-16% in both apical and basal dendrites. This was because the appearance of strong amplification requires the coincidence of both APs of 3-30 mV and EPSPs of over 6 mV, both of which depend on the dendritic location of synaptic sites. We found that the localization of amplifying synapses depends on A-type K+ channel distribution because backpropagating APs depend on the A-type K+ channel distribution, and that the localizations of amplifying synapses were similar within a range of physiological synaptic conductances. We also quantified the spread of membrane amplification in dendrites, indicating that the neighboring synapses can also show the amplification. These findings allowed us to computationally illustrate the spatial localization of synapses for supralinear summation of APs and EPSPs within thin dendritic branches where patch clamp experiments cannot be easily conducted.

In this study, we simulate pairing backpropagating action potentials (APs) with excitatory postsynaptic potentials (EPSPs) in a hippocampal CA1 model neuron, and make clear the synaptic-site dependence of the supralinear amplification, which is considered as the origin of LTP in spike-timing dependent plasticity (STDP). Pairing APs with EPSPs at randomly determined synaptic sites reveals that the supralinear amplification needs modest APs with strong EPSPs, and the sites are restricted in apical middle dendrites and basal distal dendrites with small diameters. We also discuss the importance of this restriction in hippocampal information processing.

In this study, we inquire the generation mechanism of the spike-timing dependent synaptic plasticity by computer simulation. Postsynaptic Ca2+ concentration in a spine determines synaptic plasticity. We examine the spike timing dependence of Ca2+ concentration with a multi-compartment model of a hippocampal CA I pyramidal cell. Consequently, only the Ca2+ concentration does not account for STDP, but the Ca2+ concentration filtered by mGluR-activation reproduces STDP timing window.