Synaptic integration in dendritic trees

Most neurons have elaborate dendritic trees that receive tens of thousands of synaptic inputs. Because postsynaptic responses to individual synaptic events are usually small and transient, the integration of many synaptic responses is needed to depolarize most neurons to action potential threshold. Over the past decade, advances in electrical and optical recording techniques have led to new insights into how synaptic responses propagate and interact within dendritic trees. In addition to their passive electrical and morphological properties, dendrites express active conductances that shape individual synaptic responses and influence synaptic integration locally within dendrites. Dendritic voltage-gated Na(+) and Ca(2+) channels support action potential backpropagation into the dendritic tree and local initiation of dendritic spikes, whereas K(+) conductances act to dampen dendritic excitability. While all dendrites investigated to date express active conductances, different neuronal types show specific patterns of dendritic channel expression leading to cell-specific differences in the way synaptic responses are integrated within dendritic trees. This review explores the way active and passive dendritic properties shape synaptic responses in the dendrites of central neurons, and emphasizes their role in synaptic integration.     

Oscillatory burst discharge generated through conditional backpropagation of dendritic spikes.

Gamma frequencies of burst discharge (>40 Hz) have become recognized in select cortical and non-cortical regions as being important in feature extraction, neural synchrony and oscillatory discharge. Pyramidal cells of the electrosensory lateral line lobe (ELL) of Apteronotus leptorhynchus generate burst discharge in relation to specific features of sensory input in vivo that resemble those recognized as gamma frequency discharge when examined in vitro. We have shown that these bursts are generated by an entirely novel mechanism termed conditional backpropagation that involves an intermittent failure of dendritic Na+ spike conduction. Conditional backpropagation arises from a frequency-dependent broadening of dendritic spikes during repetitive discharge, and a mismatch between the refractory periods of somatic and dendritic spikes. A high threshold class of K+ channel, AptKv3.3, is expressed at high levels and distributed over the entire soma-dendritic axis of pyramidal cells. AptKv3.3 channels are shown to contribute to the repolarization of both somatic and dendritic spikes, with pharmacological blockade of dendritic Kv3 channels revealing an important role in controlling the threshold for burst discharge. The entire process of conditional back-propagation and burst output is successfully simulated using a new compartmental model of pyramidal cells that incorporates a cumulative inactivation of dendritic K+ channels during repetitive discharge. This work is important in demonstrating how the success of spike backpropagation can control the output of a principle sensory neuron, and how this process is regulated by the distribution and properties of voltage-dependent K+ channels.     

Dendritic spines and long-term plasticity

A recent flurry of time-lapse imaging studies of live neurons have tried to address the century-old question: what morphological changes in dendritic spines can be related to long-term memory? Changes that have been proposed to relate to memory include the formation of new spines, the enlargement of spine heads and the pruning of spines. These observations also relate to a more general question of how stable dendritic spines are. The objective of this review is to critically assess the new data and to propose much needed criteria that relate spines to memory, thereby allowing progress in understanding the morphological basis of memory.     

The back and forth of dendritic plasticity

Synapses are located throughout the often-elaborate dendritic tree of central neurons. Hebbian models of plasticity require temporal association between synaptic input and neuronal output to produce long-term potentiation of excitatory transmission. Recent studies have highlighted how active dendritic spiking mechanisms control this association. Here, we review new work showing that associative synaptic plasticity can be generated without neuronal output and that the interplay between neuronal architecture and the active electrical properties of the dendritic tree regulates synaptic plasticity.     

Projections of Drosophila multidendritic neurons in the central nervous system: links with peripheral dendrite morphology

Neurons establish diverse dendritic morphologies during development, and a major challenge is to understand how these distinct developmental programs might relate to, and influence, neuronal function. Drosophila dendritic arborization (da) sensory neurons display class-specific dendritic morphology with extensive coverage of the body wall. To begin to build a basis for linking dendrite structure and function in this genetic system, we analyzed da neuron axon projections in embryonic and larval stages. We found that multiple parameters of axon morphology, including dorsoventral position, midline crossing and collateral branching, correlate with dendritic morphological class. We have identified a class-specific medial-lateral layering of axons in the central nervous system formed during embryonic development, which could allow different classes of da neurons to develop differential connectivity to second-order neurons. We have examined the effect of Robo family members on class-specific axon lamination, and have also taken a forward genetic approach to identify new genes involved in axon and dendrite development. For the latter, we screened the third chromosome at high resolution in vivo for mutations that affect class IV da neuron morphology. Several known loci, as well as putative novel mutations, were identified that contribute to sensory dendrite and/or axon patterning. This collection of mutants, together with anatomical data on dendrites and axons, should begin to permit studies of dendrite diversity in a combined developmental and functional context, and also provide a foundation for understanding shared and distinct mechanisms that control axon and dendrite morphology.     

The Role of Ongoing Dendritic Oscillations in Single-Neuron Dynamics

The dendritic tree contributes significantly to the elementary computations a neuron performs while converting its synaptic inputs into action potential output. Traditionally, these computations have been characterized as both temporally and spatially localized. Under this localist account, neurons compute near-instantaneous mappings from their current input to their current output, brought about by somatic summation of dendritic contributions that are generated in functionally segregated compartments. However, recent evidence about the presence of oscillations in dendrites suggests a qualitatively different mode of operation: the instantaneous phase of such oscillations can depend on a long history of inputs, and under appropriate conditions, even dendritic oscillators that are remote may interact through synchronization. Here, we develop a mathematical framework to analyze the interactions of local dendritic oscillations and the way these interactions influence single cell computations. Combining weakly coupled oscillator methods with cable theoretic arguments, we derive phase-locking states for multiple oscillating dendritic compartments. We characterize how the phase-locking properties depend on key parameters of the oscillating dendrite: the electrotonic properties of the (active) dendritic segment, and the intrinsic properties of the dendritic oscillators. As a direct consequence, we show how input to the dendrites can modulate phase-locking behavior and hence global dendritic coherence. In turn, dendritic coherence is able to gate the integration and propagation of synaptic signals to the soma, ultimately leading to an effective control of somatic spike generation. Our results suggest that dendritic oscillations enable the dendritic tree to operate on more global temporal and spatial scales than previously thought; notably that local dendritic activity may be a mechanism for generating on-going whole-cell voltage oscillations.     

Arithmetic of subthreshold synaptic summation in a model CA1 pyramidal cell

The rules of synaptic integration in pyramidal cells remain obscure, in part due to conflicting interpretations of existing experimental data. To clarify issues, we developed a CA1 pyramidal cell model calibrated with a broad spectrum of in vitro data. Using simultaneous dendritic and somatic recordings and combining results for two different response measures (peak versus mean EPSP), two different stimulus formats (single shock versus 50 Hz trains), and two different spatial integration conditions (within versus between-branch summation), we found that the cell's subthreshold responses to paired inputs are best described as a sum of nonlinear subunit responses, where the subunits correspond to different dendritic branches. In addition to suggesting a new type of experiment and providing testable predictions, our model shows how conclusions regarding synaptic arithmetic can be influenced by an array of seemingly innocuous experimental design choices.     

Role of active dendritic conductances in subthreshold input integration

Dendrites of many types of neurons contain voltage-dependent conductances that are active at subthreshold membrane potentials. To understand the computations neurons perform it is key to understand the role of active dendrites in the subthreshold processing of synaptic inputs. We examine systematically how active dendritic conductances affect the time course of postsynaptic potentials propagating along dendrites, and how they affect the interaction between such signals. Voltage-dependent currents can be classified into two types that have qualitatively different effects on subthreshold input responses: regenerative dendritic currents boost and broaden EPSPs, while restorative currents attenuate and narrow EPSPs. Importantly, the effects of active dendritic currents on EPSP shape increase as the EPSP travels along the dendrite. The effectiveness of active currents in modulating the EPSP shape is determined by their activation time constant: the faster it is, the stronger the effect on EPSP amplitude, while the largest effects on EPSP width occur when it is comparable to the membrane time constant. We finally demonstrate that the two current types can differentially improve precision and robustness of neural computations: restorative currents enhance coincidence detection of dendritic inputs, whereas direction selectivity to sequences of dendritic inputs is enhanced by regenerative dendritic currents.     

Developing velocity sensitivity in a model neuron by local synaptic plasticity

Sensor neurons, like those in the visual cortex, display specific functional properties, e.g., tuning for the orientation, direction and velocity of a moving stimulus. It is still unclear how these properties arise from the processing of the inputs which converge at a given cell. Specifically, little is known how such properties can develop by ways of synaptic plasticity. In this study we investigate the hypothesis that velocity sensitivity can develop at a neuron from different types of synaptic plasticity at different dendritic sub-structures. Specifically we are implementing spike-timing dependent plasticity at one dendritic branch and conventional long-term potentiation at another branch, both driven by dendritic spikes triggered by moving inputs. In the first part of the study, we show how velocity sensitivity can arise from such a spatially localized difference in the plasticity. In the second part we show how this scenario is augmented by the interaction between dendritic spikes and back-propagating spikes also at different dendritic branches. Recent theoretical (Saudargiene et al. in Neural Comput 16:595–626, 2004) and experimental (Froemke et al. in Nature 434:221–225, 2005) results on spatially localized plasticity suggest that such processes may play a major role in determining how synapses will change depending on their site. The current study suggests that such mechanisms could be used to develop the functional specificities of a neuron.     

A role for kinesin heavy chain in controlling vesicle transport into dendrites in Drosophila

The unique architecture of neurons requires the establishment and maintenance of polarity, which relies in part on microtubule-based transport to deliver essential cargo into dendrites. To test different models of differential motor protein regulation and to understand how different compartments in neurons are supplied with necessary functional proteins, we studied mechanisms of dendritic transport, using Drosophila as a model system. Our data suggest that dendritic targeting systems in Drosophila and mammals are evolutionarily conserved, since mammalian cargoes are moved into appropriate domains in Drosophila. In a genetic screen for mutants that mislocalize the dendritic marker human transferrin receptor (hTfR), we found that kinesin heavy chain (KHC) may function as a dendritic motor. Our analysis of dendritic and axonal phenotypes of KHC loss-of-function clones revealed a role for KHC in maintaining polarity of neurons, as well as ensuring proper axonal outgrowth. In addition we identified adenomatous polyposis coli 1 (APC1) as an interaction partner of KHC in controlling directed transport and modulating kinesin function in neurons.     

Dendritic cells: emerging pharmacological targets of immunosuppressive drugs

Immunosuppressive drugs have revolutionized organ transplantation and improved the therapeutic management of autoimmune diseases. The development of immunosuppressive drugs and understanding of their action traditionally has been focused on lymphocytes, but recent evidence indicates that these agents interfere with immune responses at the earliest stage, targeting key functions of dendritic cells (DCs). Here, we review our present understanding of how classical and new immunosuppressive agents interfere with DC development and function. This knowledge might provide a rational basis for the selection of immunosuppressive drugs in different clinical settings and for the generation of tolerogenic DCs in the laboratory.     

Building a dendritic actin filament network branch by branch: models of filament orientation pattern and force generation in lamellipodia

We review mathematical and computational models of the structure, dynamics, and force generation properties of dendritic actin networks. These models have been motivated by the dendritic nucleation model, which provided a mechanistic picture of how the actin cytoskeleton system powers cell motility. We describe how they aimed to explain the self-organization of the branched network into a bimodal distribution of filament orientations peaked at 35° and ??35° with respect to the direction of membrane protrusion, as well as other patterns. Concave and convex force–velocity relationships were derived, depending on network organization, filament, and membrane elasticity and accounting for actin polymerization at the barbed end as a Brownian ratchet. This review also describes models that considered the kinetics and transport of actin and diffuse regulators and mechanical coupling to a substrate, together with explicit modeling of dendritic networks.     

Computational modeling of dendrites

Computational methods have been part of neuroscience for many years. For example, models developed with these methods have provided a theory that helps explain the action potential. More recently, as experimental patch-electrode techniques have revealed new biophysics related to dendritic function and synaptic integration, computational models of dendrites have been developed to explain and further illuminate these results, and to predict possible additional behavior. Here, a collection of computational models of dendrites is reviewed. The goal is to help explain how such computational techniques work, some of their limitations, and what one can hope to learn about dendrites by modeling them.     

Sampling issues in quantitative analysis of dendritic spines morphology

ABSTRACT: BACKGROUND: Quantitative analysis of changes in dendritic spine morphology has become an interesting issue in contemporary neuroscience. However, the diversity in dendritic spines population might seriously influence the results of measurements in which their morphology is studied, the detection of differences in spine morphology between control and test group is often compromised by the number of dendritic spines taken for analysis. In order to estimate how severe is such an impact we have performed Monte Carlo simulations examining various experimental setups and statistical approaches. The confocal images of dendritic spines from hippocampal dissociated cultures have been used to create a set of variables exploited as the simulation resources. RESULTS: The tabulated results of simulations are given, providing the number of dendritic spines required for the detection of hidden morphological differences between control and test group, in spine head-width, length and area. It turns out that this is the head-width among these three variables, where the changes are most easily detected. Simulation of changes occurring in a subpopulation of spines reveal the strong dependence of detectability on the statistical approach applied. The analysis based on comparison of percentage of spines in subclasses is less sensitive than the direct comparison of relevant variables describing spines morphology. CONCLUSIONS: We evaluated the sampling aspect and effect of systematic morphological variation on detecting the differences in spine morphology. Provided results may serve as a guideline in selecting the number of samples to be studied in a planned experiment. Our simulations might be a step towards the development of a standardized method of quantitative comparison of dendritic spines morphology, in which different sources of errors are considered.     

Dendrites of distinct classes of Drosophila sensory neurons show different capacities for homotypic repulsion

BACKGROUND: Understanding how dendrites establish their territory is central to elucidating how neuronal circuits are built. Signaling between dendrites is thought to be important for defining their territories; however, the strategies by which different types of dendrites communicate are poorly understood. We have shown previously that two classes of Drosophila peripheral da sensory neurons, the class III and class IV neurons, provide complete and independent tiling of the body wall. By contrast, dendrites of class I and class II neurons do not completely tile the body wall, but they nevertheless occupy nonoverlapping territories. RESULTS: By developing reagents to permit high-resolution studies of dendritic tiling in living animals, we demonstrate that isoneuronal and heteroneuronal class IV dendrites engage in persistent repulsive interactions. In contrast to the extensive dendritic exclusion shown by class IV neurons, duplicated class III neurons showed repulsion only at their dendritic terminals. Supernumerary class I and class II neurons innervated completely overlapping regions of the body wall, and this finding suggests a lack of like-repels-like behavior. CONCLUSIONS: These data suggest that repulsive interactions operate between morphologically alike dendritic arbors in Drosophila. Further, Drosophila da sensory neurons appear to exhibit at least three different types of class-specific dendrite-dendrite interactions: persistent repulsion by all branches, repulsion only by terminal dendrites, and no repulsion.     

Tiling of the Drosophila epidermis by multidendritic sensory neurons

Insect dendritic arborization (da) neurons provide an opportunity to examine how diverse dendrite morphologies and dendritic territories are established during development. We have examined the morphologies of Drosophila da neurons by using the MARCM (mosaic analysis with a repressible cell marker) system. We show that each of the 15 neurons per abdominal hemisegment spread dendrites to characteristic regions of the epidermis. We place these neurons into four distinct morphological classes distinguished primarily by their dendrite branching complexities. Some class assignments correlate with known proneural gene requirements as well as with central axonal projections. Our data indicate that cells within two morphological classes partition the body wall into distinct, non-overlapping territorial domains and thus are organized as separate tiled sensory systems. The dendritic domains of cells in different classes, by contrast, can overlap extensively. We have examined the cell-autonomous roles of starry night (stan) (also known as flamingo (fmi)) and sequoia (seq) in tiling. Neurons with these genes mutated generally terminate their dendritic fields at normal locations at the lateral margin and segment border, where they meet or approach the like dendrites of adjacent neurons. However, stan mutant neurons occasionally send sparsely branched processes beyond these territories that could potentially mix with adjacent like dendrites. Together, our data suggest that widespread tiling of the larval body wall involves interactions between growing dendritic processes and as yet unidentified signals that allow avoidance by like dendrites.     

IFN regulation and functions in myeloid dendritic cells

A central issue in dendritic cells (DC) biology is to understand how type I IFNs modulate the immuno-regulatory properties of DC. In this review I will address this issue in light of the recent experimental evidence on the expression and function of these cytokines in myeloid DC. This knowledge may have important therapeutic implications in infectious and neoplastic diseases and open new perspectives in the use of IFNs as vaccine adjuvants and in the development of DC-based vaccines.     

Parallel Computational Subunits in Dentate Granule Cells Generate Multiple Place Fields

A fundamental question in understanding neuronal computations is how dendritic events influence the output of the neuron. Different forms of integration of neighbouring and distributed synaptic inputs, isolated dendritic spikes and local regulation of synaptic efficacy suggest that individual dendritic branches may function as independent computational subunits. In the present paper, we study how these local computations influence the output of the neuron. Using a simple cascade model, we demonstrate that triggering somatic firing by a relatively small dendritic branch requires the amplification of local events by dendritic spiking and synaptic plasticity. The moderately branching dendritic tree of granule cells seems optimal for this computation since larger dendritic trees favor local plasticity by isolating dendritic compartments, while reliable detection of individual dendritic spikes in the soma requires a low branch number. Finally, we demonstrate that these parallel dendritic computations could contribute to the generation of multiple independent place fields of hippocampal granule cells.     

Memory retention in pyramidal neurons: a unified model of energy-based homo and heterosynaptic plasticity with homeostasis

The brain can learn new tasks without forgetting old ones. This memory retention is closely associated with the long-term stability of synaptic strength. To understand the capacity of pyramidal neurons to preserve memory under different tasks, we established a plasticity model based on the postsynaptic membrane energy state, in which the change in synaptic strength depends on the difference between the energy state after stimulation and the resting energy state. If the post-stimulation energy state is higher than the resting energy state, then synaptic depression occurs. On the contrary, the synapse is strengthened. Our model unifies homo- and heterosynaptic plasticity and can reproduce synaptic plasticity observed in multiple experiments, such as spike-timing-dependent plasticity, and cooperative plasticity with few and common parameters. Based on the proposed plasticity model, we conducted a simulation study on how the activation patterns of dendritic branches by different tasks affect the synaptic connection strength of pyramidal neurons. We further investigate the formation mechanism by which different tasks activate different dendritic branches. Simulation results show that compare to the classic plasticity model, the plasticity model we proposed can achieve a better spatial separation of different branches activated by different tasks in pyramidal neurons, which deepens our insight into the memory retention mechanism of brains.