Morphologic evidence for spatially clustered spines in apical dendrites of monkey neocortical pyramidal cells

Information Thermodynamics: From Physics to Neuroscience

This paper provides a perspective on applying the concepts of information thermodynamics, developed recently in non-equilibrium statistical physics, to problems in theoretical neuroscience. Historically, information and energy in neuroscience have been treated separately, in contrast to physics approaches, where the relationship of entropy production with heat is a central idea. It is argued here that also in neural systems, information and energy can be considered within the same theoretical framework. Starting from basic ideas of thermodynamics and information theory on a classic Brownian particle, it is shown how noisy neural networks can infer its probabilistic motion. The decoding of the particle motion by neurons is performed with some accuracy, and it has some energy cost, and both can be determined using information thermodynamics. In a similar fashion, we also discuss how neural networks in the brain can learn the particle velocity and maintain that information in the weights of plastic synapses from a physical point of view. Generally, it is shown how the framework of stochastic and information thermodynamics can be used practically to study neural inference, learning, and information storing.

Cooperativity, Information Gain, and Energy Cost During Early LTP in Dendritic Spines

We investigate a mutual relationship between information and energy during the early phase of LTP induction and maintenance in a large-scale system of mutually coupled dendritic spines, with discrete internal states and probabilistic dynamics, within the framework of nonequilibrium stochastic thermodynamics. In order to analyze this computationally intractable stochastic multidimensional system, we introduce a pair approximation, which allows us to reduce the spine dynamics into a lower-dimensional manageable system of closed equations. We found that the rates of information gain and energy attain their maximal values during an initial period of LTP (i.e., during stimulation), and after that, they recover to their baseline low values, as opposed to a memory trace that lasts much longer. This suggests that the learning phase is much more energy demanding than the memory phase. We show that positive correlations between neighboring spines increase both a duration of memory trace and energy cost during LTP, but the memory time per invested energy increases dramatically for very strong, positive synaptic cooperativity, suggesting a beneficial role of synaptic clustering on memory duration. In contrast, information gain after LTP is the largest for negative correlations, and energy efficiency of that information generally declines with increasing synaptic cooperativity. We also find that dendritic spines can use sparse representations for encoding long-term information, as both energetic and structural efficiencies of retained information and its lifetime exhibit maxima for low fractions of stimulated synapses during LTP. Moreover, we find that such efficiencies drop significantly with increasing the number of spines. In general, our stochastic thermodynamics approach provides a unifying framework for studying, from first principles, information encoding, and its energy cost during learning and memory in stochastic systems of interacting synapses.

Cortical origin of theta error signals

A multi-scale approach elucidated the origin of the error-related-negativity (ERN), with its associated theta-rhythm, and the post-error-positivity (Pe) in macaque supplementary eye field (SEF). Using biophysical modeling, synaptic inputs to a subpopulation of layer-3 (L3) and layer-5 (L5) pyramidal cells (PCs) were optimized to reproduce error-related spiking modulation and inter-spike intervals. The intrinsic dynamics of dendrites in L5 but not L3 error PCs generate theta rhythmicity with random phases. Saccades synchronized the phases of the theta-rhythm, which was magnified on errors. Contributions from error PCs to the laminar current source density (CSD) observed in SEF were negligible and could not explain the observed association between error-related spiking modulation in L3 PCs and scalp-EEG. CSD from recorded laminar field potentials in SEF was comprised of multipolar components, with monopoles indicating strong electro-diffusion, dendritic/axonal electrotonic current leakage outside SEF, or violations of the model assumptions. Our results also demonstrate the involvement of secondary cortical regions, in addition to SEF, particularly for the later Pe component. The dipolar component from the observed CSD paralleled the ERN dynamics, while the quadrupolar component paralleled the Pe. These results provide the most advanced explanation to date of the cellular mechanisms generating the ERN.

The dendritic engram

Accumulating evidence from a wide range of studies, including behavioral, cellular, molecular and computational findings, support a key role of dendrites in the encoding and recall of new memories. Dendrites can integrate synaptic inputs in non-linear ways, provide the substrate for local protein synthesis and facilitate the orchestration of signaling pathways that regulate local synaptic plasticity. These capabilities allow them to act as a second layer of computation within the neuron and serve as the fundamental unit of plasticity. As such, dendrites are integral parts of the memory engram, namely the physical representation of memories in the brain and are increasingly studied during learning tasks. Here, we review experimental and computational studies that support a novel, dendritic view of the memory engram that is centered on non-linear dendritic branches as elementary memory units. We highlight the potential implications of dendritic engrams for the learning and memory field and discuss future research directions.

Hippocampal engram networks for fear memory recruit new synapses and modify pre-existing synapses in vivo

As basic units of neural networks, ensembles of synapses underlie cognitive functions such as learning and memory. These synaptic engrams show elevated synaptic density among engram cells following contextual fear memory formation. Subsequent analysis of the CA3-CA1 engram synapse revealed larger spine sizes, as the synaptic connectivity correlated with the memory strength. Here, we elucidate the synapse dynamics between CA3 and CA1 by tracking identical synapses at multiple time points by adapting two-photon microscopy and dual-eGRASP technique in vivo. After memory formation, synaptic connections between engram populations are enhanced in conjunction with synaptogenesis within the hippocampal network. However, extinction learning specifically correlated with the disappearance of CA3 engram to CA1 engram (E-E) synapses. We observed "newly formed" synapses near pre-existing synapses, which clustered CA3-CA1 engram synapses after fear memory formation. Overall, we conclude that dynamics at CA3 to CA1 E-E synapses are key sites for modification during fear memory states.

Morphological Features of Human Dendritic Spines

Dendritic spine features in human neurons follow the up-to-date knowledge presented in the previous chapters of this book. Human dendrites are notable for their heterogeneity in branching patterns and spatial distribution. These data relate to circuits and specialized functions. Spines enhance neuronal connectivity, modulate and integrate synaptic inputs, and provide additional plastic functions to microcircuits and large-scale networks. Spines present a continuum of shapes and sizes, whose number and distribution along the dendritic length are diverse in neurons and different areas. Indeed, human neurons vary from aspiny or "relatively aspiny" cells to neurons covered with a high density of intermingled pleomorphic spines on very long dendrites. In this chapter, we discuss the phylogenetic and ontogenetic development of human spines and describe the heterogeneous features of human spiny neurons along the spinal cord, brainstem, cerebellum, thalamus, basal ganglia, amygdala, hippocampal regions, and neocortical areas. Three-dimensional reconstructions of Golgi-impregnated dendritic spines and data from fluorescence microscopy are reviewed with ultrastructural findings to address the complex possibilities for synaptic processing and integration in humans. Pathological changes are also presented, for example, in Alzheimer's disease and schizophrenia. Basic morphological data can be linked to current techniques, and perspectives in this research field include the characterization of spines in human neurons with specific transcriptome features, molecular classification of cellular diversity, and electrophysiological identification of coexisting subpopulations of cells. These data would enlighten how cellular attributes determine neuron type-specific connectivity and brain wiring for our diverse aptitudes and behavior.

Spindle-Shaped Neurons in the Human Posteromedial (Precuneus) Cortex

The human posteromedial cortex (PMC), which includes the precuneus (PC), represents a multimodal brain area implicated in emotion, conscious awareness, spatial cognition, and social behavior. Here, we describe the presence of Nissl-stained elongated spindle-shaped neurons (suggestive of von Economo neurons, VENs) in the cortical layer V of the anterior and central PC of adult humans. The adapted "single-section" Golgi method for postmortem tissue was used to study these neurons close to pyramidal ones in layer V until merging with layer VI polymorphic cells. From three-dimensional (3D) reconstructed images, we describe the cell body, two main longitudinally oriented ascending and descending dendrites as well as the occurrence of spines from proximal to distal segments. The primary dendritic shafts give rise to thin collateral branches with a radial orientation, and pleomorphic spines were observed with a sparse to moderate density along the dendritic length. Other spindle-shaped cells were observed with straight dendritic shafts and rare branches or with an axon emerging from the soma. We discuss the morphology of these cells and those considered VENs in cortical areas forming integrated brain networks for higher-order activities. The presence of spindle-shaped neurons and the current discussion on the morphology of putative VENs address the need for an in-depth neurochemical and transcriptomic characterization of the PC cytoarchitecture. These findings would include these spindle-shaped cells in the synaptic and information processing by the default mode network and for general intelligence in healthy individuals and in neuropsychiatric disorders involving the PC in the context of the PMC functioning.

Synapses from the Motor Cortex and a High-Order Thalamic Nucleus are Spatially Clustered in Proximity to Each Other in the Distal Tuft Dendrites of Mouse Somatosensory Cortex

The posterior medial nucleus of the thalamus (POm) and vibrissal primary motor cortex (vM1) convey essential information to the barrel cortex (S1BF) regarding whisker position and movement. Therefore, understanding the relative spatial relationship of these two inputs is a critical prerequisite for acquiring insights into how S1BF synthesizes information to interpret the location of an object. Using array tomography, we identified the locations of synapses from vM1 and POm on distal tuft dendrites of L5 pyramidal neurons where the two inputs are combined. Synapses from vM1 and POm did not show a significant branchlet preference and impinged on the same set of dendritic branchlets. Within dendritic branches, on the other hand, the two inputs formed robust spatial clusters of their own type. Furthermore, we also observed POm clusters in proximity to vM1 clusters. This work constitutes the first detailed description of the relative distribution of synapses from POm and vM1, which is crucial to elucidate the synaptic integration of whisker-based sensory information.

Dendritic Spine Density and Dynamics of Layer 5 Pyramidal Neurons of the Primary Motor Cortex Are Elevated With Aging

It is well established that motor impairment often occurs alongside healthy aging, leading to problems with fine motor skills and coordination. Although previously thought to be caused by neuronal death accumulating across the lifespan, it is now believed that the source of this impairment instead stems from more subtle changes in neural connectivity. The dendritic spine is a prime target for exploration of this problem because it is the postsynaptic partner of most excitatory synapses received by the pyramidal neuron, a cortical cell that carries much of the information processing load in the cerebral cortex. We repeatedly imaged the same dendrites in young adult and aged mouse motor cortex over the course of 1 month to look for differences in the baseline state of the dendritic spine population. These experiments reveal increased dendritic spine density, without obvious changes in spine clustering, occurring at the aged dendrite. Additionally, aged dendrites exhibit elevated spine turnover and stabilization alongside decreased long-term spine survival. These results suggest that at baseline the aged motor cortex may exist in a perpetual state of relative instability and attempts at compensation. This phenotype of aging may provide clues for future targets of aging-related motor impairment remediation.

The Subcortical-Allocortical- Neocortical continuum for the Emergence and Morphological Heterogeneity of Pyramidal Neurons in the Human Brain

Human cortical and subcortical areas integrate emotion, memory, and cognition when interpreting various environmental stimuli for the elaboration of complex, evolved social behaviors. Pyramidal neurons occur in developed phylogenetic areas advancing along with the allocortex to represent 70-85% of the neocortical gray matter. Here, we illustrate and discuss morphological features of heterogeneous spiny pyramidal neurons emerging from specific amygdaloid nuclei, in CA3 and CA1 hippocampal regions, and in neocortical layers II/III and V of the anterolateral temporal lobe in humans. Three-dimensional images of Golgi-impregnated neurons were obtained using an algorithm for the visualization of the cell body, dendritic length, branching pattern, and pleomorphic dendritic spines, which are specialized plastic postsynaptic units for most excitatory inputs. We demonstrate the emergence and development of human pyramidal neurons in the cortical and basomedial (but not the medial, MeA) nuclei of the amygdala with cells showing a triangular cell body shape, basal branched dendrites, and a short apical shaft with proximal ramifications as "pyramidal-like" neurons. Basomedial neurons also have a long and distally ramified apical dendrite not oriented to the pial surface. These neurons are at the beginning of the allocortex and the limbic lobe. "Pyramidal-like" to "classic" pyramidal neurons with laminar organization advance from the CA3 to the CA1 hippocampal regions. These cells have basal and apical dendrites with specific receptive synaptic domains and several spines. Neocortical pyramidal neurons in layers II/III and V display heterogeneous dendritic branching patterns adapted to the space available and the afferent inputs of each brain area. Dendritic spines vary in their distribution, density, shapes, and sizes (classified as stubby/wide, thin, mushroom-like, ramified, transitional forms, "atypical" or complex forms, such as thorny excrescences in the MeA and CA3 hippocampal region). Spines were found isolated or intermingled, with evident particularities (e.g., an extraordinary density in long, deep CA1 pyramidal neurons), and some showing a spinule. We describe spiny pyramidal neurons considerably improving the connectional and processing complexity of the brain circuits. On the other hand, these cells have some vulnerabilities, as found in neurodegenerative Alzheimer's disease and in temporal lobe epilepsy.

Dendritic and Spine Heterogeneity of von Economo Neurons in the Human Cingulate Cortex

The human cingulate cortex (CC), included in the paralimbic cortex, participates in emotion, visceral responses, attention, cognition, and social behaviors. The CC has spindle-shaped/fusiform cell body neurons in its layer V, the von Economo neurons (VENs). VENs have further developed in primates, and the characterization of human VENs can benefit from the detailed descriptions of the shape of dendrites and spines. Here, we advance this issue and studied VENs in the anterior and midcingulate cortex from four neurologically normal adult subjects. We used the thionin technique and the adapted “single-section” Golgi method for light microscopy. Three-dimensional (3D) reconstructions were carried out for the visualization of Golgi-impregnated VENs’ cell body, ascending and descending dendrites, and collateral branches. We also looked for the presence, density, and shape of spines from proximal to distal dendrites. These neurons have a similar aspect for the soma, but features of spiny dendrites evidenced a morphological heterogeneity of CC VENs. Only for the description of this continuum of shapes, we labeled the most common feature as VEN 1, which has main dendritic shafts but few branches and sparse spines. VEN 2 shows an intermediate aspect, whereas VEN 3 displays the most profuse dendritic ramification and more spines with varied shapes from proximal to distal branches. Morphometric data exemplify the dendritic features of these cells. The heterogeneity of the dendritic architecture and spines suggests additional functional implications for the synaptic and information processing in VENs in integrated networks of normal and, possibly, neurological/psychiatric conditions involving the human CC.

Synaptic Clustering and Memory Formation

In the study of memory engrams, synaptic memory allocation is a newly emerged theme that focuses on how specific synapses are engaged in the storage of a given memory. Cumulating evidence from imaging and molecular experiments indicates that the recruitment of synapses that participate in the encoding and expression of memory is neither random nor uniform. A hallmark observation is the emergence of groups of synapses that share similar response properties and/or similar input properties and are located within a stretch of a dendritic branch. This grouping of synapses has been termed "synapse clustering" and has been shown to emerge in many different memory-related paradigms, as well as in in vitro studies. The clustering of synapses may emerge from synapses receiving similar input, or via many processes which allow for cross-talk between nearby synapses within a dendritic branch, leading to cooperative plasticity. Clustered synapses can act in concert to maximally exploit the nonlinear integration potential of the dendritic branches in which they reside. Their main contribution is to facilitate the induction of dendritic spikes and dendritic plateau potentials, which provide advanced computational and memory-related capabilities to dendrites and single neurons. This review focuses on recent evidence which investigates the role of synapse clustering in dendritic integration, sensory perception, learning, and memory as well as brain dysfunction. We also discuss recent theoretical work which explores the computational advantages provided by synapse clustering, leading to novel and revised theories of memory. As an eminent phenomenon during memory allocation, synapse clustering both shapes memory engrams and is also shaped by the parallel plasticity mechanisms upon which it relies.

Circadian clock regulates the shape and content of dendritic spines in mouse barrel cortex

Circadian rhythmicity affects neuronal activity induced changes in the density of synaptic contacts and dendritic spines, the most common location of synapses, in mouse somatosensory cortex. In the present study we analyzed morphology of single- and double-synapse spines under light/dark (12:12) and constant darkness conditions. Using serial electron micrographs we examined the shape of spines (stubby, thin, mushroom) and their content (smooth endoplasmic reticulum, spine apparatus), because these features are related to the maturation and stabilization of spines. We observed significant diurnal and circadian changes in the shape of spines that are differentially regulated: single-synapse spines remain under circadian clock regulation, while changes of double-synapse spines are driven by light. The thin and mushroom single-synapse spines, regardless of their content, are more stable comparing with the stubby single-synapse spines that show the greatest diversity. All types of double-synapse spines demonstrate a similar level of stability. In light/dark regime, formation of new mushroom single-synapse spines occurs, while under constant darkness new stubby single-synapse spines are formed. There are no shape preferences for new double-synapse spines. Diurnal and circadian alterations also concern spine content: both light exposure and the clock influence translocation of smooth endoplasmic reticulum from dendritic shaft to the spine. The increasing number of mushroom single-synapse spines and the presence of only those mushroom double-synapse spines that contain spine apparatus in the light phase indicates that the exposure to light, a stress factor for nocturnal animals, promotes enlargement and maturation of spines to increase synaptic strength and to enhance the effectiveness of neurotransmission.

Reduced sensory-evoked structural plasticity in the aging barrel cortex

Impairments in synaptic connectivity have been linked to cognitive deficits in age-related neurodegenerative disorders and healthy aging. However, the anatomical and structural bases of these impairments have not been identified yet. A hallmark of neural plasticity in young adults is short-term synaptic rearrangement, yet aged animals already display higher synaptic turnover rates at the baseline. Using two-photon excitation (2PE) microscopy, we explored if this elevated turnover alters the aged brain's response to plasticity. Following a sensory-evoked plasticity protocol involving whisker stimulation, aged mice display reduced spine dynamics (gain, loss, and turnover), decreased spine clustering, and lower spine stability when compared to young adult mice. These results suggest a deficiency of the cortical neurons of aged mice to structurally incorporate new sensory experiences, in the form of clustered, long-lasting synapses, into already existing cortical circuits. This research provides the first evidence linking experience-dependent plasticity with in vivo spine dynamics in the aged brain and supports a model of both reduced synaptic plasticity and reduced synaptic tenacity in the aged somatosensory system.

Homeostatic Plasticity Scales Dendritic Spine Volumes and Changes the Threshold and Specificity of Hebbian Plasticity

Information is encoded in neural networks through changes in synaptic weights. Synaptic learning rules involve a combination of rapid Hebbian plasticity and slower homeostatic synaptic plasticity that regulates neuronal activity through global synaptic scaling. Hebbian and homeostatic plasticity have been extensively investigated, whereas much less is known about their interaction. Here we investigated structural and functional consequences of homeostatic plasticity at dendritic spines of mouse hippocampal neurons. We found that prolonged activity blockade induced spine growth, paralleling synaptic strength increases. Following activity blockade, glutamate uncaging-mediated stimulation at single spines led to size-dependent structural potentiation: smaller spines underwent robust growth, whereas larger spines remained unchanged. Moreover, spines near the stimulated spine exhibited volume changes following homeostatic plasticity, indicating that there was a breakdown of input specificity following homeostatic plasticity. Overall, these findings demonstrate that Hebbian and homeostatic plasticity interact to shape neural connectivity through non-uniform structural plasticity at inputs.

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Chronic activity blockade leads to enlarged hippocampal spines and structural scaling

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Homeostatic plasticity affects subsequent Hebbian plasticity according to size of spines

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Neighbors also grow after potentiation of single spines, compromising input specificity

Quantitative Analysis of the Spatial Organization of Synaptic Inputs on the Postsynaptic Dendrite

The spatial organization of synaptic inputs on the dendritic tree of cortical neurons is considered to play an important role in the dendritic integration of synaptic activity. Active electrical properties of dendrites and mechanisms of dendritic integration have been studied for a long time. New technological developments are now enabling the characterization of the spatial organization of synaptic inputs on dendrites. However, quantitative methods for the analysis of such data are lacking. In order to place cluster parameters into the framework of dendritic integration and synaptic summation, these parameters need to be assessed rigorously in a quantitative manner. Here I present an approach for the analysis of synaptic input clusters on the dendritic tree that is based on combinatorial analysis of the likelihoods to observe specific input arrangements. This approach is superior to the commonly applied analysis of nearest neighbor distances between synaptic inputs comparing their distribution to simulations with random reshuffling or bootstrapping. First, the new approach yields exact likelihood values rather than approximate numbers obtained from simulations. Second and more importantly, the new approach identifies individual clusters and thereby allows to quantify and characterize individual cluster properties.

Effects of location and extent of spine clustering on synaptic integration in striatal medium spiny neurons-a computational study

The nucleus accumbens (NAc) is known widely for its role in the reward circuit, which is dysregulated in a number of psychological disorders. Recent evidence also suggests the contribution of this structure in spatial and gustatory memories. Because of its role in different types of memories, similar to the hippocampus, we assumed the formation of spine clusters, which are engrams of memory, to be present on dendrites of medium spiny neurons (MSNs). We found that the activation of clustered inputs resulted in sublinear summation when clusters were present on the same branch and also when inputs were distributed on different branches. The size, as well as the location of clusters, was found to affect the summation. With an increase in cluster size and distance from soma, the summation was increasingly sublinear. When the temporal integration window was measured for clustered spines, it was found to be narrower as compared to that for a single spine. Also, distally located clusters resulted in a wider temporal window, as compared to proximal clusters. Our results suggest that depending on the location of clusters, the modes of integration will differ in MSNs possessing clustered spines.

Clustered structural and functional plasticity of dendritic spines

The configuration of synaptic circuits underlies their ability to process and store information. Research on dendritic spines has revealed that their structural and functional alterations are clustered along the parent dendrite. Here we review the evidence supporting such notion of clustered synaptic plasticity, discuss its functional implications and possible contributing factors, and suggest potential strategies to deal with open challenges.

Is realistic neuronal modeling realistic?

Scientific models are abstractions that aim to explain natural phenomena. A successful model shows how a complex phenomenon arises from relatively simple principles while preserving major physical or biological rules and predicting novel experiments. A model should not be a facsimile of reality; it is an aid for understanding it. Contrary to this basic premise, with the 21st century has come a surge in computational efforts to model biological processes in great detail. Here we discuss the oxymoronic, realistic modeling of single neurons. This rapidly advancing field is driven by the discovery that some neurons don't merely sum their inputs and fire if the sum exceeds some threshold. Thus researchers have asked what are the computational abilities of single neurons and attempted to give answers using realistic models. We briefly review the state of the art of compartmental modeling highlighting recent progress and intrinsic flaws. We then attempt to address two fundamental questions. Practically, can we realistically model single neurons? Philosophically, should we realistically model single neurons? We use layer 5 neocortical pyramidal neurons as a test case to examine these issues. We subject three publically available models of layer 5 pyramidal neurons to three simple computational challenges. Based on their performance and a partial survey of published models, we conclude that current compartmental models are ad hoc, unrealistic models functioning poorly once they are stretched beyond the specific problems for which they were designed. We then attempt to plot possible paths for generating realistic single neuron models.

Insights into age-old questions of new dendritic spines: From form to function

Principal neurons in multiple brain regions receive a vast majority of excitatory synaptic contacts on the tiny dendritic appendages called dendritic spines. These structures are believed to be the locus of memory storage in the brain. Indeed, neurological diseases leading to impairment in memory and cognitive capabilities are often associated with structural alteration of dendritic spines. While several landmark studies in the past have provided a great deal of information on the structure, function and molecular composition of prototypical mature dendritic spines, we still have a limited knowledge of nascent spines. In recent years there has been a surge of interest to understand the nascent spines and the increasing technical advances in the genetic, molecular and imaging methods have opened avenues for systematic and thorough investigation. In this review, by discussing studies from several labs including ours, we provide a systematic summary of the development, structure, molecular expression and function of nascent spines and highlight some of the potentially important and interesting research questions that remain to be answered.

Clusters of synaptic inputs on dendrites of layer 5 pyramidal cells in mouse visual cortex

The spatial organization of synaptic inputs on the dendritic tree of cortical neurons plays a major role for dendritic integration and neural computations, yet, remarkably little is known about it. We mapped the spatial organization of glutamatergic synapses between layer 5 pyramidal cells by combining optogenetics and 2-photon calcium imaging in mouse neocortical slices. To mathematically characterize the organization of inputs we developed an approach based on combinatorial analysis of the likelihoods of specific synapse arrangements. We found that the synapses of intralaminar inputs form clusters on the basal dendrites of layer 5 pyramidal cells. These clusters contain 4 to 14 synapses within ≤30 µm of dendrite. According to the spatiotemporal characteristics of synaptic summation, these numbers suggest that there will be non-linear dendritic integration of synaptic inputs during synchronous activation.

DOI:http://dx.doi.org/10.7554/eLife.09222.001

Neurons in the brain exchange information through points of contact called synapses. If electrical activity arriving at a number of synapses exceeds a certain threshold, it can trigger an electrical impulse, which is transmitted to the next neuron. Synapses generally connect with branch-like structures called dendrites on the receiving neuron. However, little is known about how synapses are arranged on dendrites.

Gökçe et al. have now used a technique called optogenetics to work out the exact arrangement of a type of synapse on neurons in a part of the mouse brain that is devoted to vision. Optogenetics takes advantage of light-activated proteins that can trigger electrical activity. Gökçe et al. used mice that had been genetically engineered to produce these proteins in specific neurons, and then deliberately triggered electrical activity simply by shining light on these neurons. The experiments also used another technique called two-photon calcium imaging to monitor the activity of single synapses in response to the electrical activity triggered by optogenetics.

Gökçe et al. found that these neurons have clusters of four to fourteen synapses within a space of 30 micrometers along a dendrite. Synapses in clusters that are active at the same time can interact and thereby generate electrical signals more effectively than synapses spread across the dendrites. Further experiments are now needed to map the synapses between other kinds of neurons, and to map synapses from two different inputs at the same time.

DOI:http://dx.doi.org/10.7554/eLife.09222.002

Quantifying the Number of Discriminable Coincident Dendritic Input Patterns through Dendritic Tree Morphology

Current developments in neuronal physiology are unveiling novel roles for dendrites. Experiments have shown mechanisms of non-linear synaptic NMDA dependent activations, able to discriminate input patterns through the waveforms of the excitatory postsynaptic potentials. Contextually, the synaptic clustering of inputs is the principal cellular strategy to separate groups of common correlated inputs. Dendritic branches appear to work as independent discriminating units of inputs potentially reflecting an extraordinary repertoire of pattern memories. However, it is unclear how these observations could impact our comprehension of the structural correlates of memory at the cellular level. This work investigates the discrimination capabilities of neurons through computational biophysical models to extract a predicting law for the dendritic input discrimination capability (M). By this rule we compared neurons from a neuron reconstruction repository (neuromorpho.org). Comparisons showed that primate neurons were not supported by an equivalent M preeminence and that M is not uniformly distributed among neuron types. Remarkably, neocortical neurons had substantially less memory capacity in comparison to those from non-cortical regions. In conclusion, the proposed rule predicts the inherent neuronal spatial memory gathering potentially relevant anatomical and evolutionary considerations about the brain cytoarchitecture.

Modeling maintenance of long-term potentiation in clustered synapses: long-term memory without bistability

Memories are stored, at least partly, as patterns of strong synapses. Given molecular turnover, how can synapses maintain strong for the years that memories can persist? Some models postulate that biochemical bistability maintains strong synapses. However, bistability should give a bimodal distribution of synaptic strength or weight, whereas current data show unimodal distributions for weights and for a correlated variable, dendritic spine volume. Thus it is important for models to simulate both unimodal distributions and long-term memory persistence. Here a model is developed that connects ongoing, competing processes of synaptic growth and weakening to stochastic processes of receptor insertion and removal in dendritic spines. The model simulates long-term (>1 yr) persistence of groups of strong synapses. A unimodal weight distribution results. For stability of this distribution it proved essential to incorporate resource competition between synapses organized into small clusters. With competition, these clusters are stable for years. These simulations concur with recent data to support the “clustered plasticity hypothesis” which suggests clusters, rather than single synaptic contacts, may be a fundamental unit for storage of long-term memory. The model makes empirical predictions and may provide a framework to investigate mechanisms maintaining the balance between synaptic plasticity and stability of memory.

Synaptic clustering within dendrites: an emerging theory of memory formation

It is generally accepted that complex memories are stored in distributed representations throughout the brain, however the mechanisms underlying these representations are not understood. Here, we review recent findings regarding the subcellular mechanisms implicated in memory formation, which provide evidence for a dendrite-centered theory of memory. Plasticity-related phenomena which affect synaptic properties, such as synaptic tagging and capture, synaptic clustering, branch strength potentiation and spinogenesis provide the foundation for a model of memory storage that relies heavily on processes operating at the dendrite level. The emerging picture suggests that clusters of functionally related synapses may serve as key computational and memory storage units in the brain. We discuss both experimental evidence and theoretical models that support this hypothesis and explore its advantages for neuronal function.

Reduced density of dendritic spines in pyramidal neurons of rats exposed to alcohol during early postnatal life

Dendritic spines are the main postsynaptic sites of excitatory connections of neocortical pyramidal neurons. Alterations of spine shape, number, and density can be observed in different mental diseases, including those caused by developmental alcohol exposure. Pyramidal neurons of layer 2/3 are the most abundant cells of the neocortex and represent the main source of associative cortico-cortical connections. These neurons are essential for higher functions mediated by the cortex such as feature selection and perceptual grouping. Furthermore, their connections have been shown to be altered in experimental models of fetal alcohol spectrum disorders. Here, we used a Golgi-like tracing method to study the spine density of layer 2/3 associative pyramidal neurons in the somatosensory cortex of adult rats exposed to alcohol during the first postnatal week. The main result of the present study is represented by the decreased spine density in the apical dendrite of alcohol-treated rats, as compared to controls. As to the basal dendritic tree, there were no significant differences between the experimental and the control group. A decreased density of dendritic spines in the apical dendrite may impair the excitatory input onto pyramidal neurons, thus resulting in a widespread alteration of the cortical information flow.

Early fear memory defects are associated with altered synaptic plasticity and molecular architecture in the TgCRND8 Alzheimer's disease mouse model

Alzheimer's disease (AD) is a complex and slowly progressing dementing disorder that results in neuronal and synaptic loss, deposition in brain of aberrantly folded proteins, and impairment of spatial and episodic memory. Most studies of mouse models of AD have employed analyses of cognitive status and assessment of amyloid burden, gliosis, and molecular pathology during disease progression. Here we sought to understand the behavioral, cellular, ultrastructural, and molecular changes that occur at a pathological stage equivalent to the early stages of human AD. We studied the TgCRND8 mouse, a model of aggressive AD amyloidosis, at an early stage of plaque pathology (3 months of age) in comparison to their wildtype littermates and assessed changes in cognition, neuron and spine structure, and expression of synaptic glutamate receptor proteins. We found that, at this age, TgCRND8 mice display substantial plaque deposition in the neocortex and hippocampus and impairment on cued and contextual memory tasks. Of particular interest, we also observed a significant decrease in the number of neurons in the hippocampus. Furthermore, analysis of CA1 neurons revealed significant changes in apical and basal dendritic spine types, as well as altered expression of GluN1 and GluA2 receptors. This change in molecular architecture within the hippocampus may reflect a rising representation of inherently less stable thin spine populations, which can cause cognitive decline. These changes, taken together with toxic insults from amyloid-β protein, may underlie the observed neuronal loss.

Random positions of dendritic spines in human cerebral cortex

Dendritic spines establish most excitatory synapses in the brain and are located in Purkinje cell's dendrites along helical paths, perhaps maximizing the probability to contact different axons. To test whether spine helixes also occur in neocortex, we reconstructed >500 dendritic segments from adult human cortex obtained from autopsies. With Fourier analysis and spatial statistics, we analyzed spine position along apical and basal dendrites of layer 3 pyramidal neurons from frontal, temporal, and cingulate cortex. Although we occasionally detected helical positioning, for the great majority of dendrites we could not reject the null hypothesis of spatial randomness in spine locations, either in apical or basal dendrites, in neurons of different cortical areas or among spines of different volumes and lengths. We conclude that in adult human neocortex spine positions are mostly random. We discuss the relevance of these results for spine formation and plasticity and their functional impact for cortical circuits.

Inter-synaptic learning of combination rules in a cortical network model

Selecting responses in working memory while processing combinations of stimuli depends strongly on their relations stored in long-term memory. However, the learning of XOR-like combinations of stimuli and responses according to complex rules raises the issue of the non-linear separability of the responses within the space of stimuli. One proposed solution is to add neurons that perform a stage of non-linear processing between the stimuli and responses, at the cost of increasing the network size. Based on the non-linear integration of synaptic inputs within dendritic compartments, we propose here an inter-synaptic (IS) learning algorithm that determines the probability of potentiating/depressing each synapse as a function of the co-activity of the other synapses within the same dendrite. The IS learning is effective with random connectivity and without either a priori wiring or additional neurons. Our results show that IS learning generates efficacy values that are sufficient for the processing of XOR-like combinations, on the basis of the sole correlational structure of the stimuli and responses. We analyze the types of dendrites involved in terms of the number of synapses from pre-synaptic neurons coding for the stimuli and responses. The synaptic efficacy values obtained show that different dendrites specialize in the detection of different combinations of stimuli. The resulting behavior of the cortical network model is analyzed as a function of inter-synaptic vs. Hebbian learning. Combinatorial priming effects show that the retrospective activity of neurons coding for the stimuli trigger XOR-like combination-selective prospective activity of neurons coding for the expected response. The synergistic effects of inter-synaptic learning and of mixed-coding neurons are simulated. The results show that, although each mechanism is sufficient by itself, their combined effects improve the performance of the network.

Synaptic competition in structural plasticity and cognitive function

Connections between neurons can undergo long-lasting changes in synaptic strength correlating with changes in structure. These events require the synthesis of new proteins, the availability of which can lead to cooperative and competitive interactions between synapses for the expression of plasticity. These processes can occur over limited spatial distances and temporal periods, defining dendritic regions over which activity may be integrated and could lead to the physical rewiring of synapses into functional groups. Such clustering of inputs may increase the computational power of neurons by allowing information to be combined in a greater than additive manner. The availability of new proteins may be a key modulatory step towards activity-dependent, long-term growth or elimination of spines necessary for remodelling of connections. Thus, the aberrant growth or shrinkage of dendritic spines could occur if protein levels are misregulated. Indeed, such perturbations can be seen in several mental retardation disorders, wherein either too much or too little protein translation exists, matching an observed increase or decrease in spine density, respectively. Cellular events which alter protein availability could relieve a constraint on synaptic competition and disturb synaptic clustering mechanisms. These changes may be detrimental to modifications in neural circuitry following activity.

Statistical analysis of dendritic spine distributions in rat hippocampal cultures

BACKGROUND

Dendritic spines serve as key computational structures in brain plasticity. Much remains to be learned about their spatial and temporal distribution among neurons. Our aim in this study was to perform exploratory analyses based on the population distributions of dendritic spines with regard to their morphological characteristics and period of growth in dissociated hippocampal neurons. We fit a log-linear model to the contingency table of spine features such as spine type and distance from the soma to first determine which features were important in modeling the spines, as well as the relationships between such features. A multinomial logistic regression was then used to predict the spine types using the features suggested by the log-linear model, along with neighboring spine information. Finally, an important variant of Ripley's K-function applicable to linear networks was used to study the spatial distribution of spines along dendrites.

RESULTS

Our study indicated that in the culture system, (i) dendritic spine densities were "completely spatially random", (ii) spine type and distance from the soma were independent quantities, and most importantly, (iii) spines had a tendency to cluster with other spines of the same type.

CONCLUSIONS

Although these results may vary with other systems, our primary contribution is the set of statistical tools for morphological modeling of spines which can be used to assess neuronal cultures following gene manipulation such as RNAi, and to study induced pluripotent stem cells differentiated to neurons.

Dendritic morphology of pyramidal neurons in the chimpanzee neocortex: regional specializations and comparison to humans

The primate cerebral cortex is characterized by regional variation in the structure of pyramidal neurons, with more complex dendritic arbors and greater spine density observed in prefrontal compared with sensory and motor cortices. Although there are several investigations in humans and other primates, virtually nothing is known about regional variation in the morphology of pyramidal neurons in the cerebral cortex of great apes, humans' closest living relatives. The current study uses the rapid Golgi stain to quantify the dendritic structure of layer III pyramidal neurons in 4 areas of the chimpanzee cerebral cortex: Primary somatosensory (area 3b), primary motor (area 4), prestriate visual (area 18), and prefrontal (area 10) cortex. Consistent with previous studies in humans and macaque monkeys, pyramidal neurons in the prefrontal cortex of chimpanzees exhibit greater dendritic complexity than those in other cortical regions, suggesting that prefrontal cortical evolution in primates is characterized by increased potential for integrative connectivity. Compared with chimpanzees, the pyramidal neurons of humans had significantly longer and more branched dendritic arbors in all cortical regions.

Synaptogenesis and development of pyramidal neuron dendritic morphology in the chimpanzee neocortex resembles humans

Neocortical development in humans is characterized by an extended period of synaptic proliferation that peaks in mid-childhood, with subsequent pruning through early adulthood, as well as relatively delayed maturation of neuronal arborization in the prefrontal cortex compared with sensorimotor areas. In macaque monkeys, cortical synaptogenesis peaks during early infancy and developmental changes in synapse density and dendritic spines occur synchronously across cortical regions. Thus, relatively prolonged synapse and neuronal maturation in humans might contribute to enhancement of social learning during development and transmission of cultural practices, including language. However, because macaques, which share a last common ancestor with humans ≈ 25 million years ago, have served as the predominant comparative primate model in neurodevelopmental research, the paucity of data from more closely related great apes leaves unresolved when these evolutionary changes in the timing of cortical development became established in the human lineage. To address this question, we used immunohistochemistry, electron microscopy, and Golgi staining to characterize synaptic density and dendritic morphology of pyramidal neurons in primary somatosensory (area 3b), primary motor (area 4), prestriate visual (area 18), and prefrontal (area 10) cortices of developing chimpanzees (Pan troglodytes). We found that synaptogenesis occurs synchronously across cortical areas, with a peak of synapse density during the juvenile period (3-5 y). Moreover, similar to findings in humans, dendrites of prefrontal pyramidal neurons developed later than sensorimotor areas. These results suggest that evolutionary changes to neocortical development promoting greater neuronal plasticity early in postnatal life preceded the divergence of the human and chimpanzee lineages.

Coding and decoding with dendrites

Since the discovery of complex, voltage dependent mechanisms in the dendrites of multiple neuron types, great effort has been devoted in search of a direct link between dendritic properties and specific neuronal functions. Over the last few years, new experimental techniques have allowed the visualization and probing of dendritic anatomy, plasticity and integrative schemes with unprecedented detail. This vast amount of information has caused a paradigm shift in the study of memory, one of the most important pursuits in Neuroscience, and calls for the development of novel theories and models that will unify the available data according to some basic principles. Traditional models of memory considered neural cells as the fundamental processing units in the brain. Recent studies however are proposing new theories in which memory is not only formed by modifying the synaptic connections between neurons, but also by modifications of intrinsic and anatomical dendritic properties as well as fine tuning of the wiring diagram. In this review paper we present previous studies along with recent findings from our group that support a key role of dendrites in information processing, including the encoding and decoding of new memories, both at the single cell and the network level.

Influence of highly distinctive structural properties on the excitability of pyramidal neurons in monkey visual and prefrontal cortices

Whole-cell patch-clamp recordings and high-resolution 3D morphometric analyses of layer 3 pyramidal neurons in in vitro slices of monkey primary visual cortex (V1) and dorsolateral granular prefrontal cortex (dlPFC) revealed that neurons in these two brain areas possess highly distinctive structural and functional properties. Area V1 pyramidal neurons are much smaller than dlPFC neurons, with significantly less extensive dendritic arbors and far fewer dendritic spines. Relative to dlPFC neurons, V1 neurons have a significantly higher input resistance, depolarized resting membrane potential, and higher action potential (AP) firing rates. Most V1 neurons exhibit both phasic and regular-spiking tonic AP firing patterns, while dlPFC neurons exhibit only tonic firing. Spontaneous postsynaptic currents are lower in amplitude and have faster kinetics in V1 than in dlPFC neurons, but are no different in frequency. Three-dimensional reconstructions of V1 and dlPFC neurons were incorporated into computational models containing Hodgkin-Huxley and AMPA receptor and GABA(A) receptor gated channels. Morphology alone largely accounted for observed passive physiological properties, but led to AP firing rates that differed more than observed empirically, and to synaptic responses that opposed empirical results. Accordingly, modeling predicts that active channel conductances differ between V1 and dlPFC neurons. The unique features of V1 and dlPFC neurons are likely fundamental determinants of area-specific network behavior. The compact electrotonic arbor and increased excitability of V1 neurons support the rapid signal integration required for early processing of visual information. The greater connectivity and dendritic complexity of dlPFC neurons likely support higher level cognitive functions including working memory and planning.