Synaptic clustering by dendritic signalling mechanisms

Compartmentalized versus global synaptic plasticity on dendrites controlled by experience

Synapses in the brain are continuously modified by experience, but the mechanisms are poorly understood. In vitro and theoretical studies suggest threshold-lowering interactions between nearby synapses that favor clustering of synaptic plasticity within a dendritic branch. Here, a fluorescently tagged AMPA receptor-based optical approach was developed permitting detection of single-synapse plasticity in mouse cortex. Sensory experience preferentially produced synaptic potentiation onto nearby dendritic synapses. Such clustering was significantly reduced by expression of a phospho-mutant AMPA receptor that is insensitive to threshold-lowering modulation for plasticity-driven synaptic incorporation. In contrast to experience, sensory deprivation caused homeostatic synaptic enhancement globally on dendrites. Clustered synaptic potentiation produced by experience could bind behaviorally relevant information onto dendritic subcompartments; global synaptic upscaling by deprivation could equally sensitize all dendritic regions for future synaptic input.

Synaptic plasticity of NMDA receptors: mechanisms and functional implications

Beyond their well-established role as triggers for LTP and LTD of fast synaptic transmission mediated by AMPA receptors, an expanding body of evidence indicates that NMDA receptors (NMDARs) themselves are also dynamically regulated and subject to activity-dependent long-term plasticity. NMDARs can significantly contribute to information transfer at synapses particularly during periods of repetitive activity. It is also increasingly recognized that NMDARs participate in dendritic synaptic integration and are critical for generating persistent activity of neural assemblies. Here we review recent advances on the mechanisms and functional consequences of NMDAR plasticity. Given the unique biophysical properties of NMDARs, synaptic plasticity of NMDAR-mediated transmission emerges as a particularly powerful mechanism for the fine tuning of information encoding and storage throughout the brain.

Branch-specific plasticity enables self-organization of nonlinear computation in single neurons

It has been conjectured that nonlinear processing in dendritic branches endows individual neurons with the capability to perform complex computational operations that are needed to solve for example the binding problem. However, it is not clear how single neurons could acquire such functionality in a self-organized manner, because most theoretical studies of synaptic plasticity and learning concentrate on neuron models without nonlinear dendritic properties. In the meantime, a complex picture of information processing with dendritic spikes and a variety of plasticity mechanisms in single neurons has emerged from experiments. In particular, new experimental data on dendritic branch strength potentiation in rat hippocampus have not yet been incorporated into such models. In this article, we investigate how experimentally observed plasticity mechanisms, such as depolarization-dependent spike-timing-dependent plasticity and branch-strength potentiation, could be integrated to self-organize nonlinear neural computations with dendritic spikes. We provide a mathematical proof that, in a simplified setup, these plasticity mechanisms induce a competition between dendritic branches, a novel concept in the analysis of single neuron adaptivity. We show via computer simulations that such dendritic competition enables a single neuron to become member of several neuronal ensembles and to acquire nonlinear computational capabilities, such as the capability to bind multiple input features. Hence, our results suggest that nonlinear neural computation may self-organize in single neurons through the interaction of local synaptic and dendritic plasticity mechanisms.

Development of cell type-specific connectivity patterns of converging excitatory axons in the retina

To integrate information from different presynaptic cell types, dendrites receive distinct patterns of synapses from converging axons. How different afferents in vivo establish specific connectivity patterns with the same dendrite is poorly understood. Here, we examine the synaptic development of three glutamatergic bipolar cell types converging onto a common postsynaptic retinal ganglion cell. We find that after axons and dendrites target appropriate synaptic layers, patterns of connections among these neurons diverge through selective changes in the conversion of axo-dendritic appositions to synapses. This process is differentially regulated by neurotransmission, which is required for the shift from single to multisynaptic appositions of one bipolar cell type but not for maintenance and elimination, respectively, of connections from the other two types. Thus, synaptic specificity among converging excitatory inputs in the retina emerges via differential synaptic maturation of axo-dendritic appositions and is shaped by neurotransmission in a cell type-dependent manner.

Multiple clusters of release sites formed by individual thalamic afferents onto cortical interneurons ensure reliable transmission

Thalamic afferents supply the cortex with sensory information by contacting both excitatory neurons and inhibitory interneurons. Interestingly, thalamic contacts with interneurons constitute such a powerful synapse that even one afferent can fire interneurons, thereby driving feedforward inhibition. However, the spatial representation of this potent synapse on interneuron dendrites is poorly understood. Using Ca imaging and electron microscopy we show that an individual thalamic afferent forms multiple contacts with the interneuronal proximal dendritic arbor, preferentially near branch points. More contacts are correlated with larger amplitude synaptic responses. Each contact, consisting of a single bouton, can release up to seven vesicles simultaneously, resulting in graded and reliable Ca transients. Computational modeling indicates that the release of multiple vesicles at each contact minimally reduces the efficiency of the thalamic afferent in exciting the interneuron. This strategy preserves the spatial representation of thalamocortical inputs across the dendritic arbor over a wide range of release conditions.

Dendritic slow dynamics enables localized cortical activity to switch between mobile and immobile modes with noisy background input

Mounting lines of evidence suggest the significant computational ability of a single neuron empowered by active dendritic dynamics. This motivates us to study what functionality can be acquired by a network of such neurons. The present paper studies how such rich single-neuron dendritic dynamics affects the network dynamics, a question which has scarcely been specifically studied to date. We simulate neurons with active dendrites networked locally like cortical pyramidal neurons, and find that naturally arising localized activity--called a bump--can be in two distinct modes, mobile or immobile. The mode can be switched back and forth by transient input to the cortical network. Interestingly, this functionality arises only if each neuron is equipped with the observed slow dendritic dynamics and with in vivo-like noisy background input. If the bump activity is considered to indicate a point of attention in the sensory areas or to indicate a representation of memory in the storage areas of the cortex, this would imply that the flexible mode switching would be of great potential use for the brain as an information processing device. We derive these conclusions using a natural extension of the conventional field model, which is defined by combining two distinct fields, one representing the somatic population and the other representing the dendritic population. With this tool, we analyze the spatial distribution of the degree of after-spike adaptation and explain how we can understand the presence of the two distinct modes and switching between the modes. We also discuss the possible functional impact of this mode-switching ability.

The decade of the dendritic NMDA spike

In the field of cortical cellular physiology, much effort has been invested in understanding thick apical dendrites of pyramidal neurons and the regenerative sodium and calcium spikes that take place in the apical trunk. Here we focus on thin dendrites of pyramidal cells (basal, oblique, and tuft dendrites), and we discuss one relatively novel form of an electrical signal ("NMDA spike") that is specific for these branches. Basal, oblique, and apical tuft dendrites receive a high density of glutamatergic synaptic contacts. Synchronous activation of 10-50 neighboring glutamatergic synapses triggers a local dendritic regenerative potential, NMDA spike/plateau, which is characterized by significant local amplitude (40-50 mV) and an extraordinary duration (up to several hundred milliseconds). The NMDA plateau potential, when it is initiated in an apical tuft dendrite, is able to maintain a good portion of that tuft in a sustained depolarized state. However, if NMDA-dominated plateau potentials originate in proximal segments of basal dendrites, they regularly bring the neuronal cell body into a sustained depolarized state, which resembles a cortical Up state. At each dendritic initiation site (basal, oblique, and tuft) an NMDA spike creates favorable conditions for causal interactions of active synaptic inputs, including the spatial or temporal binding of information, as well as processes of short-term and long-term synaptic modifications (e.g., long-term potentiation or long-term depression). Because of their strong amplitudes and durations, local dendritic NMDA spikes make up the cellular substrate for multisite independent subunit computations that enrich the computational power and repertoire of cortical pyramidal cells. We propose that NMDA spikes are likely to play significant roles in cortical information processing in awake animals (spatiotemporal binding, working memory) and during slow-wave sleep (neuronal Up states, consolidation of memories).

Synaptic integration gradients in single cortical pyramidal cell dendrites

Cortical pyramidal neurons receive thousands of synaptic inputs arriving at different dendritic locations with varying degrees of temporal synchrony. It is not known if different locations along single cortical dendrites integrate excitatory inputs in different ways. Here we have used two-photon glutamate uncaging and compartmental modeling to reveal a gradient of nonlinear synaptic integration in basal and apical oblique dendrites of cortical pyramidal neurons. Excitatory inputs to the proximal dendrite sum linearly and require precise temporal coincidence for effective summation, whereas distal inputs are amplified with high gain and integrated over broader time windows. This allows distal inputs to overcome their electrotonic disadvantage, and become surprisingly more effective than proximal inputs at influencing action potential output. Thus, single dendritic branches can already exhibit nonuniform synaptic integration, with the computational strategy shifting from temporal coding to rate coding along the dendrite.

Simultaneous two-photon activation of presynaptic cells and calcium imaging in postsynaptic dendritic spines

Background

Dendritic spines of pyramidal neurons are distributed along the complicated structure of the dendritic branches and possess a variety of morphologies associated with synaptic strength. The location and structure of dendritic spines determine the extent of synaptic input integration in the postsynaptic neuron. However, how spine location or size relates to the position of innervating presynaptic cells is not yet known. This report describes a new method that represents a first step toward addressing this issue.

Results

The technique combines two-photon uncaging of glutamate over a broad area (~500 × 250 × 100 μm) with two-photon calcium imaging in a narrow region (~50 × 10 × 1 μm). The former was used for systematic activation of layer 2/3 pyramidal cells in the rat motor cortex, while the latter was used to detect the dendritic spines of layer 5 pyramidal cells that were innervated by some of the photoactivated cells. This technique allowed identification of various sizes of innervated spine located <140 μm laterally from the postsynaptic soma. Spines distal to their parent soma were preferentially innervated by cells on the ipsilateral side. No cluster of neurons innervating the same dendritic branch was detected.

Conclusions

This new method will be a powerful tool for clarifying the microarchitecture of synaptic connections, including the positional and structural characteristics of dendritic spines along the dendrites.

Roller Coaster Scanning reveals spontaneous triggering of dendritic spikes in CA1 interneurons

Inhibitory interneurons are considered to be the controlling units of neural networks, despite their sparse number and unique morphological characteristics compared with excitatory pyramidal cells. Although pyramidal cell dendrites have been shown to display local regenerative events--dendritic spikes (dSpikes)--evoked by artificially patterned stimulation of synaptic inputs, no such studies exist for interneurons or for spontaneous events. In addition, imaging techniques have yet to attain the required spatial and temporal resolution for the detection of spontaneously occurring events that trigger dSpikes. Here we describe a high-resolution 3D two-photon laser scanning method (Roller Coaster Scanning) capable of imaging long dendritic segments resolving individual spines and inputs with a temporal resolution of a few milliseconds. By using this technique, we found that local, NMDA receptor-dependent dSpikes can be observed in hippocampal CA1 stratum radiatum interneurons during spontaneous network activities in vitro. These NMDA spikes appear when approximately 10 spatially clustered inputs arrive synchronously and trigger supralinear integration in dynamic interaction zones. In contrast to the one-to-one relationship between computational subunits and dendritic branches described in pyramidal cells, here we show that interneurons have relatively small (∼14 μm) sliding interaction zones. Our data suggest a unique principle as to how interneurons integrate synaptic information by local dSpikes.

Synaptic plasticity in medial vestibular nucleus neurons: comparison with computational requirements of VOR adaptation

BACKGROUND

Vestibulo-ocular reflex (VOR) gain adaptation, a longstanding experimental model of cerebellar learning, utilizes sites of plasticity in both cerebellar cortex and brainstem. However, the mechanisms by which the activity of cortical Purkinje cells may guide synaptic plasticity in brainstem vestibular neurons are unclear. Theoretical analyses indicate that vestibular plasticity should depend upon the correlation between Purkinje cell and vestibular afferent inputs, so that, in gain-down learning for example, increased cortical activity should induce long-term depression (LTD) at vestibular synapses.

METHODOLOGY/PRINCIPAL FINDINGS

Here we expressed this correlational learning rule in its simplest form, as an anti-Hebbian, heterosynaptic spike-timing dependent plasticity interaction between excitatory (vestibular) and inhibitory (floccular) inputs converging on medial vestibular nucleus (MVN) neurons (input-spike-timing dependent plasticity, iSTDP). To test this rule, we stimulated vestibular afferents to evoke EPSCs in rat MVN neurons in vitro. Control EPSC recordings were followed by an induction protocol where membrane hyperpolarizing pulses, mimicking IPSPs evoked by flocculus inputs, were paired with single vestibular nerve stimuli. A robust LTD developed at vestibular synapses when the afferent EPSPs coincided with membrane hyperpolarization, while EPSPs occurring before or after the simulated IPSPs induced no lasting change. Furthermore, the iSTDP rule also successfully predicted the effects of a complex protocol using EPSP trains designed to mimic classical conditioning.

CONCLUSIONS

These results, in strong support of theoretical predictions, suggest that the cerebellum alters the strength of vestibular synapses on MVN neurons through hetero-synaptic, anti-Hebbian iSTDP. Since the iSTDP rule does not depend on post-synaptic firing, it suggests a possible mechanism for VOR adaptation without compromising gaze-holding and VOR performance in vivo.

Cellular and subcellular localization of the neuron-specific plasma membrane calcium ATPase PMCA1a in the rat brain

Regulation of intracellular calcium is crucial both for proper neuronal function and survival. By coupling ATP hydrolysis with Ca(2+) extrusion from the cell, the plasma membrane calcium-dependent ATPases (PMCAs) play an essential role in controlling intracellular calcium levels in neurons. In contrast to PMCA2 and PMCA3, which are expressed in significant levels only in the brain and a few other tissues, PMCA1 is ubiquitously distributed, and is thus widely believed to play a "housekeeping" function in mammalian cells. Whereas the PMCA1b splice variant is predominant in most tissues, an alternative variant, PMCA1a, is the major form of PMCA1 in the adult brain. Here, we use immunohistochemistry to analyze the cellular and subcellular distribution of PMCA1a in the brain. We show that PMCA1a is not ubiquitously expressed, but rather is confined to neurons, where it concentrates in the plasma membrane of somata, dendrites, and spines. Thus, rather than serving a general housekeeping function, our data suggest that PMCA1a is a calcium pump specialized for neurons, where it may contribute to the modulation of somatic and dendritic Ca(2+) transients.

Dendritic synapse location and neocortical spike-timing-dependent plasticity

While it has been appreciated for decades that synapse location in the dendritic tree has a powerful influence on signal processing in neurons, the role of dendritic synapse location on the induction of long-term synaptic plasticity has only recently been explored. Here, we review recent work revealing how learning rules for spike-timing-dependent plasticity (STDP) in cortical neurons vary with the spatial location of synaptic input. A common principle appears to be that proximal synapses show conventional STDP, whereas distal inputs undergo plasticity according to novel learning rules. One crucial factor determining location-dependent STDP is the backpropagating action potential, which tends to decrease in amplitude and increase in width as it propagates into the dendritic tree of cortical neurons. We discuss additional location-dependent mechanisms as well as the functional implications of heterogeneous learning rules at different dendritic locations for the organization of synaptic inputs.

Spike timing-dependent plasticity as the origin of the formation of clustered synaptic efficacy engrams

Synapse location, dendritic active properties and synaptic plasticity are all known to play some role in shaping the different input streams impinging onto a neuron. It remains unclear however, how the magnitude and spatial distribution of synaptic efficacies emerge from this interplay. Here, we investigate this interplay using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and spike timing-dependent plasticity (STDP). Specifically, we focus on the issue of how the efficacy of synapses contributed by different input streams are spatially represented in dendrites after STDP learning. We construct a simple feed forward network where a detailed model neuron receives synaptic inputs independently from multiple yet equally sized groups of afferent fibers with correlated activity, mimicking the spike activity from different neuronal populations encoding, for example, different sensory modalities. Interestingly, ensuing STDP learning, we observe that for all afferent groups, STDP leads to synaptic efficacies arranged into spatially segregated clusters effectively partitioning the dendritic tree. These segregated clusters possess a characteristic global organization in space, where they form a tessellation in which each group dominates mutually exclusive regions of the dendrite. Put simply, the dendritic imprint from different input streams left after STDP learning effectively forms what we term a “dendritic efficacy mosaic.” Furthermore, we show how variations of the inputs and STDP rule affect such an organization. Our model suggests that STDP may be an important mechanism for creating a clustered plasticity engram, which shapes how different input streams are spatially represented in dendrite.

Spike timing dependent plasticity: a consequence of more fundamental learning rules

Spike timing dependent plasticity (STDP) is a phenomenon in which the precise timing of spikes affects the sign and magnitude of changes in synaptic strength. STDP is often interpreted as the comprehensive learning rule for a synapse - the "first law" of synaptic plasticity. This interpretation is made explicit in theoretical models in which the total plasticity produced by complex spike patterns results from a superposition of the effects of all spike pairs. Although such models are appealing for their simplicity, they can fail dramatically. For example, the measured single-spike learning rule between hippocampal CA3 and CA1 pyramidal neurons does not predict the existence of long-term potentiation one of the best-known forms of synaptic plasticity. Layers of complexity have been added to the basic STDP model to repair predictive failures, but they have been outstripped by experimental data. We propose an alternate first law: neural activity triggers changes in key biochemical intermediates, which act as a more direct trigger of plasticity mechanisms. One particularly successful model uses intracellular calcium as the intermediate and can account for many observed properties of bidirectional plasticity. In this formulation, STDP is not itself the basis for explaining other forms of plasticity, but is instead a consequence of changes in the biochemical intermediate, calcium. Eventually a mechanism-based framework for learning rules should include other messengers, discrete change at individual synapses, spread of plasticity among neighboring synapses, and priming of hidden processes that change a synapse's susceptibility to future change. Mechanism-based models provide a rich framework for the computational representation of synaptic plasticity.

Neuronal arithmetic

The vast computational power of the brain has traditionally been viewed as arising from the complex connectivity of neural networks, in which an individual neuron acts as a simple linear summation and thresholding device. However, recent studies show that individual neurons utilize a wealth of nonlinear mechanisms to transform synaptic input into output firing. These mechanisms can arise from synaptic plasticity, synaptic noise, and somatic and dendritic conductances. This tool kit of nonlinear mechanisms confers considerable computational power on both morphologically simple and more complex neurons, enabling them to perform a range of arithmetic operations on signals encoded ina variety of different ways.

Dendritic organization of sensory input to cortical neurons in vivo

In sensory cortex regions, neurons are tuned to specific stimulus features. For example, in the visual cortex, many neurons fire predominantly in response to moving objects of a preferred orientation. However, the characteristics of the synaptic input that cortical neurons receive to generate their output firing pattern remain unclear. Here we report a novel approach for the visualization and functional mapping of sensory inputs to the dendrites of cortical neurons in vivo. By combining high-speed two-photon imaging with electrophysiological recordings, we identify local subthreshold calcium signals that correspond to orientation-specific synaptic inputs. We find that even inputs that share the same orientation preference are widely distributed throughout the dendritic tree. At the same time, inputs of different orientation preference are interspersed, so that adjacent dendritic segments are tuned to distinct orientations. Thus, orientation-tuned neurons can compute their characteristic firing pattern by integrating spatially distributed synaptic inputs coding for multiple stimulus orientations.

Chasing the cell assembly

Although we know enormous amounts of detailed information about the neurons that make up the cortex, placing this information back into the context of the behaving animal is a serious challenge. The functional cell assembly hypothesis first described by Hebb (The Organization of Behavior; a Neuropsychological Theory. New York: Wiley; 1949) aimed to provide a mechanistic explanation of how groups of neurons, acting together, form a percept. The vast number of neurons potentially involved make testing this hypothesis exceedingly difficult as neither the number nor locations of assembly members are known. Although increasing the number of neurons from which simultaneous recordings are made is of benefit, providing evidence for or against a hypothesis like Hebb's requires more than this. In this review, we aim to outline some recent technical advances, which may light the way in the chase for the functional cell assembly.

Perisomatic voltage-gated sodium channels actively maintain linear synaptic integration in principal neurons of the medial superior olive

Principal neurons of the medial superior olive (MSO) compute azimuthal sound location by integrating phase-locked inputs from each ear. While previous experimental and modeling studies have proposed that voltage-gated sodium channels (VGSCs) play an important role in synaptic integration in the MSO, these studies appear at odds with the unusually weak active backpropagation of action potentials into the soma and dendrites. To understand the spatial localization and biophysical properties of VGSCs, we isolated sodium currents in MSO principal neurons in gerbil brainstem slices. Nucleated and cell-attached patches revealed that VGSC density at the soma is comparable to that of many other neuron types, but channel expression is largely absent from the dendrites. Further, while somatic VGSCs activated with conventional voltage dependence (V(1/2) = -30 mV), they exhibited an unusually negative range of steady-state inactivation (V(1/2) = -77 mV), leaving approximately 92% of VGSCs inactivated at the resting potential (approximately -58 mV). In current-clamp experiments, non-inactivated VGSCs were sufficient to amplify subthreshold EPSPs near action potential threshold, counterbalancing the suppression of EPSP peaks by low voltage-activated potassium channels. EPSP amplification was restricted to the perisomatic region of the neuron, and relatively insensitive to preceding inhibition. Finally, computational modeling showed that the exclusion of VGSCs from the dendrites equalizes somatic EPSP amplification across synaptic locations and lowered the threshold for bilateral versus unilateral excitatory synaptic inputs. Together, these findings suggest that the pattern of sodium channel expression in MSO neurons contributes to these neurons' selectivity for coincident binaural inputs.

Properties of piriform cortex pyramidal cell dendrites: implications for olfactory circuit design

Unlike the neocortex, sensory input to the piriform cortex is anatomically segregated in layer 1, making it ideal for studying the dendritic integration of synaptic inputs pivotal for sensory information processing. Here we investigated dendritic integration of olfactory bulb inputs in pyramidal cells using dual patch-clamp recordings along the soma-apical dendritic axis. We found that these dendrites are relatively compact with 50% maximal somatic current loss for synaptic inputs arriving at distal dendritic regions. Distal dendrites could generate small and fast local spikes, but they had little impact on the soma, indicating that they are only weakly active. In contrast to the neocortex, we found no evidence for dendritic Ca(2+) or NMDA spikes though these dendrites actively supported action potential backpropagation with concomitant entry of Ca(2+) ions. Based on experiments and simulations we suggest that regardless of dendritic location, olfactory bulb inputs have nearly uniform potency and are distributed diffusely over the distal apical tree (layer Ia), thereby minimizing sublinear summation effects. This indicates that any stimulus feature extraction performed by these cells will occur at the soma and is based on the nearly linear sum of olfactory bulb inputs, rather than on explicitly designed clusters of functionally related synapses in the dendritic tree.

Hebbian crosstalk prevents nonlinear unsupervised learning

Learning is thought to occur by localized, activity-induced changes in the strength of synaptic connections between neurons. Recent work has shown that induction of change at one connection can affect changes at others (“crosstalk”). We studied the role of such crosstalk in nonlinear Hebbian learning using a neural network implementation of independent components analysis. We find that there is a sudden qualitative change in the performance of the network at a threshold crosstalk level, and discuss the implications of this for nonlinear learning from higher-order correlations in the neocortex.

Locally balanced dendritic integration by short-term synaptic plasticity and active dendritic conductances

The high degree of variability observed in spike trains and membrane potentials of pyramidal neurons in vivo is thought to be a consequence of a balance between excitatory and inhibitory inputs, which depends on the dynamics of the network. We simulated synaptic currents and ion channels in a reconstructed hippocampal CA1 pyramidal cell and show here that a local balance can be achieved on a dendritic branch with a different mechanism, based on presynaptic depression of quantal release interacting with active dendritic conductances. This mechanism, which does not require synaptic inhibition, allows each dendritic branch to remain sensitive to correlated synaptic inputs, induces a high degree of variability in the output spike train, and can be combined with other balance mechanisms based on network dynamics. This hypothesis makes a testable prediction for the cause of the observed variability in the firing of hippocampal place cells.

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.

Neurons were originally divided into three morphologically distinct compartments: the dendrites receive the synaptic input, the soma integrates it and communicates the output of the cell to other neurons via the axon. Although several lines of evidence challenged this oversimplified view, neurons are still considered to be the basic information processing units of the nervous system as their output reflects the computations performed by the entire dendritic tree. In the present study, the authors build a simplified computational model and calculate that, in certain neurons, relatively small dendritic branches are able to independently trigger somatic firing. Therefore, in these cells, an action potential mirrors the activity of a small dendritic subunit rather than the input arriving to the whole dendritic tree. These neurons can be regarded as a network of a few independent integrator units connected to a common output unit. The authors demonstrate that a moderately branched dendritic tree of hippocampal granule cells may be optimized for these parallel computations. Finally the authors show that these parallel dendritic computations could explain some aspects of the location dependent activity of hippocampal granule cells.

Possible dendritic contribution to unimodal numerosity tuning and weber-fechner law-dependent numerical cognition

Humans and animals are known to share an ability to estimate or compare the numerosity of visual stimuli, and this ability is considered to be supported by the cortical neurons that have unimodal tuning for numerosity, referred to as the numerosity detector neurons. How such unimodal numerosity tuning is shaped through plasticity mechanisms is unknown. Here, I propose a testable hypothetical mechanism based on recently revealed features of the neuronal dendrite, namely, cooperative plasticity induction and nonlinear input integration at nearby dendritic sites, on the basis of the existing proposal that individual visual stimuli are represented as similar localized activities regardless of the size or the shape in a cortical region in the dorsal visual pathway. Intriguingly, the proposed mechanism naturally explains a prominent feature of the numerosity detector neurons, namely, the broadening of the tuning curve in proportion to the preferred numerosity, which is considered to underlie the known Weber-Fechner law-dependent accuracy of numerosity estimation and comparison. The simulated tuning curves are less sharp than reality, however, and together with the evidence from human imaging studies that numerical representation is a distributed phenomenon, it may not be likely that the proposed mechanism operates by itself. Rather, the proposed mechanism might facilitate the formation of hierarchical circuitry proposed in the previous studies, which includes neurons with monotonic numerosity tuning as well as those with sharp unimodal tuning, by serving as an efficient initial condition.

Experimental and computational aspects of signaling mechanisms of spike-timing-dependent plasticity

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.

Subcellular topography of visually driven dendritic activity in the vertebrate visual system

Neural pathways projecting from sensory organs to higher brain centers form topographic maps in which neighbor relationships are preserved from a sending to a receiving neural population. Sensory input can generate compartmentalized electrical and biochemical activity in the dendrites of a receiving neuron. Here, we show that in the developing retinotectal projection of young Xenopus tadpoles, visually driven Ca2+ signals are topographically organized at the subcellular, dendritic scale. Functional in vivo two-photon Ca2+ imaging revealed that the sensitivity of dendritic Ca2+ signals to stimulus location in visual space is correlated with their anatomical position within the dendritic tree of individual neurons. This topographic distribution was dependent on NMDAR activation, whereas global Ca2+ signals were mediated by Ca2+ influx through dendritic, voltage-dependent Ca2+ channels. These findings suggest a framework for plasticity models that invoke local dendritic Ca2+ signaling in the elaboration of neural connectivity and dendrite-specific information storage.

The morphology of supragranular pyramidal neurons in the human insular cortex: a quantitative Golgi study

Although the primate insular cortex has been studied extensively, a comprehensive investigation of its neuronal morphology has yet to be completed. To that end, neurons from 20 human subjects (10 males and 10 females; N = 600) were selected from the secondary gyrus brevis, precentral gyrus, and postcentral gyrus of the left insula. The secondary gyrus brevis was generally more complex in terms of dendritic/spine extent than either the precentral or postcentral insular gyri, which is consistent with the posterior-anterior gradient of dendritic complexity observed in other cortical regions. The male insula had longer, spinier dendrites than the female insula, potentially reflecting sex differences in interoception. In comparing the current insular data with regional dendritic data quantified from other Brodmann's areas (BAs), insular total dendritic length (TDL) was less than the TDL of high integration cortices (BA6beta, 10, 11, 39), but greater than the TDL of low integration cortices (BA3-1-2, 4, 22, 44). Insular dendritic spine number was significantly greater than both low and high integration regions. Overall, the insula had spinier, but shorter neurons than did high integration cortices, and thus may represent a specialized type of heteromodal cortex, one that integrates crude multisensory information crucial to interoceptive processes.

Computational implications of cooperative plasticity induction at nearby dendritic sites

Recent studies have revealed that plasticity is not regulated independently at individual synapses but rather that there is cooperativity or associativity between nearby synapses in the dendritic tree of individual cortical pyramidal cells. Here, I summarize experimental results regarding such cooperative plasticity and its underlying mechanisms and consider their computational implications.