Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex

Saturated Reconstruction of a Volume of Neocortex

We describe automated technologies to probe the structure of neural tissue at nanometer resolution and use them to generate a saturated reconstruction of a sub-volume of mouse neocortex in which all cellular objects (axons, dendrites, and glia) and many sub-cellular components (synapses, synaptic vesicles, spines, spine apparati, postsynaptic densities, and mitochondria) are rendered and itemized in a database. We explore these data to study physical properties of brain tissue. For example, by tracing the trajectories of all excitatory axons and noting their juxtapositions, both synaptic and non-synaptic, with every dendritic spine we refute the idea that physical proximity is sufficient to predict synaptic connectivity (the so-called Peters' rule). This online minable database provides general access to the intrinsic complexity of the neocortex and enables further data-driven inquiries.

An algorithm to predict the connectome of neural microcircuits

Experimentally mapping synaptic connections, in terms of the numbers and locations of their synapses and estimating connection probabilities, is still not a tractable task, even for small volumes of tissue. In fact, the six layers of the neocortex contain thousands of unique types of synaptic connections between the many different types of neurons, of which only a handful have been characterized experimentally. Here we present a theoretical framework and a data-driven algorithmic strategy to digitally reconstruct the complete synaptic connectivity between the different types of neurons in a small well-defined volume of tissue—the micro-scale connectome of a neural microcircuit. By enforcing a set of established principles of synaptic connectivity, and leveraging interdependencies between fundamental properties of neural microcircuits to constrain the reconstructed connectivity, the algorithm yields three parameters per connection type that predict the anatomy of all types of biologically viable synaptic connections. The predictions reproduce a spectrum of experimental data on synaptic connectivity not used by the algorithm. We conclude that an algorithmic approach to the connectome can serve as a tool to accelerate experimental mapping, indicating the minimal dataset required to make useful predictions, identifying the datasets required to improve their accuracy, testing the feasibility of experimental measurements, and making it possible to test hypotheses of synaptic connectivity.

Delayed and Temporally Imprecise Neurotransmission in Reorganizing Cortical Microcircuits

Synaptic neurotransmission is modified at cortical connections throughout life. Varying the amplitude of the postsynaptic response is one mechanism that generates flexible signaling in neural circuits. The timing of the synaptic response may also play a role. Here, we investigated whether weakening and loss of an entire connection between excitatory cortical neurons was foreshadowed in the timing of the postsynaptic response. We made electrophysiological recordings in rat primary somatosensory cortex that was undergoing experience-dependent loss of complete local excitatory connections. The synaptic latency of pyramid-pyramid connections, which typically comprise multiple synapses, was longer and more variable. Connection strength and latency were not correlated. Instead, prolonged latency was more closely related to progression of connection loss. The action potential waveform and axonal conduction velocity were unaffected, suggesting that the altered timing of neurotransmission was attributable to a synaptic mechanism. Modeling studies indicated that increasing the latency and jitter at a subset of synapses reduced the number of action potentials fired by a postsynaptic neuron. We propose that prolonged synaptic latency and diminished temporal precision of neurotransmission are hallmarks of impending loss of a cortical connection.

Purinergic signalling during development and ageing

Extracellular purines and pyrimidines play major roles during embryogenesis, organogenesis, postnatal development and ageing in vertebrates, including humans. Pluripotent stem cells can differentiate into three primary germ layers of the embryo but may also be involved in plasticity and repair of the adult brain. These cells express the molecular components necessary for purinergic signalling, and their developmental fates can be manipulated via this signalling pathway. Functional P1, P2Y and P2X receptor subtypes and ectonucleotidases are involved in the development of different organ systems, including heart, blood vessels, skeletal muscle, urinary bladder, central and peripheral neurons, retina, inner ear, gut, lung and vas deferens. The importance of purinergic signalling in the ageing process is suggested by changes in expression of A1 and A2 receptors in old rat brains and reduction of P2X receptor expression in ageing mouse brain. By contrast, in the periphery, increases in expression of P2X3 and P2X4 receptors are seen in bladder and pancreas.

Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning

Although it is well known that long-term synaptic plasticity can be expressed both pre- and postsynaptically, the functional consequences of this arrangement have remained elusive. We show that spike-timing-dependent plasticity with both pre- and postsynaptic expression develops receptive fields with reduced variability and improved discriminability compared to postsynaptic plasticity alone. These long-term modifications in receptive field statistics match recent sensory perception experiments. Moreover, learning with this form of plasticity leaves a hidden postsynaptic memory trace that enables fast relearning of previously stored information, providing a cellular substrate for memory savings. Our results reveal essential roles for presynaptic plasticity that are missed when only postsynaptic expression of long-term plasticity is considered, and suggest an experience-dependent distribution of pre- and postsynaptic strength changes.

Anatomy and physiology of the thick-tufted layer 5 pyramidal neuron

The thick-tufted layer 5 (TTL5) pyramidal neuron is one of the most extensively studied neuron types in the mammalian neocortex and has become a benchmark for understanding information processing in excitatory neurons. By virtue of having the widest local axonal and dendritic arborization, the TTL5 neuron encompasses various local neocortical neurons and thereby defines the dimensions of neocortical microcircuitry. The TTL5 neuron integrates input across all neocortical layers and is the principal output pathway funneling information flow to subcortical structures. Several studies over the past decades have investigated the anatomy, physiology, synaptology, and pathophysiology of the TTL5 neuron. This review summarizes key discoveries and identifies potential avenues of research to facilitate an integrated and unifying understanding on the role of a central neuron in the neocortex.

Effects of homeostatic constraints on associative memory storage and synaptic connectivity of cortical circuits

The impact of learning and long-term memory storage on synaptic connectivity is not completely understood. In this study, we examine the effects of associative learning on synaptic connectivity in adult cortical circuits by hypothesizing that these circuits function in a steady-state, in which the memory capacity of a circuit is maximal and learning must be accompanied by forgetting. Steady-state circuits should be characterized by unique connectivity features. To uncover such features we developed a biologically constrained, exactly solvable model of associative memory storage. The model is applicable to networks of multiple excitatory and inhibitory neuron classes and can account for homeostatic constraints on the number and the overall weight of functional connections received by each neuron. The results show that in spite of a large number of neuron classes, functional connections between potentially connected cells are realized with less than 50% probability if the presynaptic cell is excitatory and generally a much greater probability if it is inhibitory. We also find that constraining the overall weight of presynaptic connections leads to Gaussian connection weight distributions that are truncated at zero. In contrast, constraining the total number of functional presynaptic connections leads to non-Gaussian distributions, in which weak connections are absent. These theoretical predictions are compared with a large dataset of published experimental studies reporting amplitudes of unitary postsynaptic potentials and probabilities of connections between various classes of excitatory and inhibitory neurons in the cerebellum, neocortex, and hippocampus.

The effects of neuron morphology on graph theoretic measures of network connectivity: the analysis of a two-level statistical model

We developed a two-level statistical model that addresses the question of how properties of neurite morphology shape the large-scale network connectivity. We adopted a low-dimensional statistical description of neurites. From the neurite model description we derived the expected number of synapses, node degree, and the effective radius, the maximal distance between two neurons expected to form at least one synapse. We related these quantities to the network connectivity described using standard measures from graph theory, such as motif counts, clustering coefficient, minimal path length, and small-world coefficient. These measures are used in a neuroscience context to study phenomena from synaptic connectivity in the small neuronal networks to large scale functional connectivity in the cortex. For these measures we provide analytical solutions that clearly relate different model properties. Neurites that sparsely cover space lead to a small effective radius. If the effective radius is small compared to the overall neuron size the obtained networks share similarities with the uniform random networks as each neuron connects to a small number of distant neurons. Large neurites with densely packed branches lead to a large effective radius. If this effective radius is large compared to the neuron size, the obtained networks have many local connections. In between these extremes, the networks maximize the variability of connection repertoires. The presented approach connects the properties of neuron morphology with large scale network properties without requiring heavy simulations with many model parameters. The two-steps procedure provides an easier interpretation of the role of each modeled parameter. The model is flexible and each of its components can be further expanded. We identified a range of model parameters that maximizes variability in network connectivity, the property that might affect network capacity to exhibit different dynamical regimes.

Distribution and density of contacts from noradrenergic and serotonergic boutons on the dendrites of neck flexor motoneurons in the adult cat

Serotonergic (5-HT) and noradrenergic (NA) input to spinal motoneurons is essential for generating plateau potentials and self-sustained discharges. Extensor motoneurons are densely innervated by 5-HT and NA synapses and have robust plateau potentials and self-sustained discharges. Conversely, plateau potentials and self-sustained discharges are very rare in flexor motoneurons. The most likely reasons for this difference are that flexor motoneurons have few 5-HT and NA synapses and/or they are distributed distant to the channels responsible for plateau potentials and self-sustained discharges. However, the distribution of 5-HT and NA synapses on flexor motoneurons is unknown. Here we describe the distribution and density of 5-HT and NA synapses on motoneurons that innervate the flexor neck muscle, rectus capitis anterior (RCA), in the adult cat. Using a combination of intracellular staining, fluorescent immunohistochemistry, and 3D reconstruction techniques, we found that 5-HT and NA synapses are widely distributed throughout the dendritic trees of RCA motoneurons, albeit with a strong bias to small-diameter dendrites and to medial dendrites in the case of NA contacts. The number of 5-HT and NA contacts per motoneuron ranged, respectively, from 381 to 1,430 and from 642 to 1,382, which is 2.3- and 1.4-fold less than neck extensor motoneurons (Montague et al., J Comp Neurol 2013;521:638-656). These results suggest that 5-HT and NA synapses on flexor motoneurons may provide a powerful means of amplifying synaptic currents without incurring plateau potentials or self-sustained discharges. This feature is well suited to meet the biomechanical demands imposed on flexor muscles during different motor tasks.

Developmental time windows for axon growth influence neuronal network topology

Early brain connectivity development consists of multiple stages: birth of neurons, their migration and the subsequent growth of axons and dendrites. Each stage occurs within a certain period of time depending on types of neurons and cortical layers. Forming synapses between neurons either by growing axons starting at similar times for all neurons (much-overlapped time windows) or at different time points (less-overlapped) may affect the topological and spatial properties of neuronal networks. Here, we explore the extreme cases of axon formation during early development, either starting at the same time for all neurons (parallel, i.e., maximally overlapped time windows) or occurring for each neuron separately one neuron after another (serial, i.e., no overlaps in time windows). For both cases, the number of potential and established synapses remained comparable. Topological and spatial properties, however, differed: Neurons that started axon growth early on in serial growth achieved higher out-degrees, higher local efficiency and longer axon lengths while neurons demonstrated more homogeneous connectivity patterns for parallel growth. Second, connection probability decreased more rapidly with distance between neurons for parallel growth than for serial growth. Third, bidirectional connections were more numerous for parallel growth. Finally, we tested our predictions with C. elegans data. Together, this indicates that time windows for axon growth influence the topological and spatial properties of neuronal networks opening up the possibility to a posteriori estimate developmental mechanisms based on network properties of a developed network.

All-optical mapping of barrel cortex circuits based on simultaneous voltage-sensitive dye imaging and channelrhodopsin-mediated photostimulation

We describe an experimental approach that uses light to both control and detect neuronal activity in mouse barrel cortex slices: blue light patterned by a digital micromirror array system allowed us to photostimulate specific layers and columns, while a red-shifted voltage-sensitive dye was used to map out large-scale circuit activity. We demonstrate that such all-optical mapping can interrogate various circuits in somatosensory cortex by sequentially activating different layers and columns. Further, mapping in slices from whisker-deprived mice demonstrated that chronic sensory deprivation did not significantly alter feedforward inhibition driven by layer 5 pyramidal neurons. Further development of voltage-sensitive optical probes should allow this all-optical mapping approach to become an important and high-throughput tool for mapping circuit interactions in the brain.

Distribution and function of HCN channels in the apical dendritic tuft of neocortical pyramidal neurons

The apical tuft is the most remote area of the dendritic tree of neocortical pyramidal neurons. Despite its distal location, the apical dendritic tuft of layer 5 pyramidal neurons receives substantial excitatory synaptic drive and actively processes corticocortical input during behavior. The properties of the voltage-activated ion channels that regulate synaptic integration in tuft dendrites have, however, not been thoroughly investigated. Here, we use electrophysiological and optical approaches to examine the subcellular distribution and function of hyperpolarization-activated cyclic nucleotide-gated nonselective cation (HCN) channels in rat layer 5B pyramidal neurons. Outside-out patch recordings demonstrated that the amplitude and properties of ensemble HCN channel activity were uniform in patches excised from distal apical dendritic trunk and tuft sites. Simultaneous apical dendritic tuft and trunk whole-cell current-clamp recordings revealed that the pharmacological blockade of HCN channels decreased voltage compartmentalization and enhanced the generation and spread of apical dendritic tuft and trunk regenerative activity. Furthermore, multisite two-photon glutamate uncaging demonstrated that HCN channels control the amplitude and duration of synaptically evoked regenerative activity in the distal apical dendritic tuft. In contrast, at proximal apical dendritic trunk and somatic recording sites, the blockade of HCN channels decreased excitability. Dynamic-clamp experiments revealed that these compartment-specific actions of HCN channels were heavily influenced by the local and distributed impact of the high density of HCN channels in the distal apical dendritic arbor. The properties and subcellular distribution pattern of HCN channels are therefore tuned to regulate the interaction between integration compartments in layer 5B pyramidal neurons.

An optogenetics- and imaging-assisted simultaneous multiple patch-clamp recording system for decoding complex neural circuits

Deciphering neuronal circuitry is central to understanding brain function and dysfunction, yet it remains a daunting task. To facilitate the dissection of neuronal circuits, a process requiring functional analysis of synaptic connections and morphological identification of interconnected neurons, we present here a method for stable simultaneous octuple patch-clamp recordings. This method allows physiological analysis of synaptic interconnections among 4-8 simultaneously recorded neurons and/or 10-30 sequentially recorded neurons, and it allows anatomical identification of >85% of recorded interneurons and >99% of recorded principal neurons. We describe how to apply the method to rodent tissue slices; however, it can be used on other model organisms. We also describe the latest refinements and optimizations of mechanics, electronics, optics and software programs that are central to the realization of a combined single- and two-photon microscopy-based, optogenetics- and imaging-assisted, stable, simultaneous quadruple-viguple patch-clamp recording system. Setting up the system, from the beginning of instrument assembly and software installation to full operation, can be completed in 3-4 d.

A barrel-related interneuron in layer 4 of rat somatosensory cortex with a high intrabarrel connectivity

Synaptic connections between identified fast-spiking (FS), parvalbumin (PV)-positive interneurons, and excitatory spiny neurons in layer 4 (L4) of the barrel cortex were investigated using patch-clamp recordings and simultaneous biocytin fillings. Three distinct clusters of FS L4 interneurons were identified based on their axonal morphology relative to the barrel column suggesting that these neurons do not constitute a homogeneous interneuron population. One L4 FS interneuron type had an axonal domain strictly confined to a L4 barrel and was therefore named "barrel-confined inhibitory interneuron" (BIn). BIns established reliable inhibitory synaptic connections with L4 spiny neurons at a high connectivity rate of 67%, of which 69% were reciprocal. Unitary IPSPs at these connections had a mean amplitude of 0.9 ± 0.8 mV with little amplitude variation and weak short-term synaptic depression. We found on average 3.7 ± 1.3 putative inhibitory synaptic contacts that were not restricted to perisomatic areas. In conclusion, we characterized a novel type of barrel cortex interneuron in the major thalamo-recipient layer 4 forming dense synaptic networks with L4 spiny neurons. These networks constitute an efficient and powerful inhibitory feedback system, which may serve to rapidly reset the barrel microcircuitry following sensory activation.

Functional organization of excitatory synaptic strength in primary visual cortex

The strength of synaptic connections fundamentally determines how neurons influence each other's firing. Excitatory connection amplitudes between pairs of cortical neurons vary over two orders of magnitude, comprising only very few strong connections among many weaker ones. Although this highly skewed distribution of connection strengths is observed in diverse cortical areas, its functional significance remains unknown: it is not clear how connection strength relates to neuronal response properties, nor how strong and weak inputs contribute to information processing in local microcircuits. Here we reveal that the strength of connections between layer 2/3 (L2/3) pyramidal neurons in mouse primary visual cortex (V1) obeys a simple rule--the few strong connections occur between neurons with most correlated responses, while only weak connections link neurons with uncorrelated responses. Moreover, we show that strong and reciprocal connections occur between cells with similar spatial receptive field structure. Although weak connections far outnumber strong connections, each neuron receives the majority of its local excitation from a small number of strong inputs provided by the few neurons with similar responses to visual features. By dominating recurrent excitation, these infrequent yet powerful inputs disproportionately contribute to feature preference and selectivity. Therefore, our results show that the apparently complex organization of excitatory connection strength reflects the similarity of neuronal responses, and suggest that rare, strong connections mediate stimulus-specific response amplification in cortical microcircuits.

Qualitatively different effect of repeated stress during adolescence on principal neuron morphology across lateral and basal nuclei of the rat amygdala

Repeated stress can elicit symptoms of depression and anxiety. The amygdala is a significant contributor to the expression of emotion and the basolateral amygdala (BLA) is a major target for the effects of stress on emotion. The adolescent time period may be particularly susceptible to the effects of stress on emotion. While repeated stress has been demonstrated to modify the morphology of BLA neurons in adult rats, little is known about its effects on BLA neurons during adolescence. This study tests the effects of repeated stress during adolescence on BLA neuronal morphology, and whether these are similar to the effects of stress during adulthood. The BLA includes the basal (BA) and lateral (LAT) nuclei, which are differentially responsive to stress in adults. Therefore, effects of stress during adolescence were compared between the BA and LAT nuclei. Morphological features of reconstructed BLA neurons were examined using Golgi-Cox-stained tissue from control or repeated restraint stress-exposed rats. We found subtle dendritic growth coupled with loss of spines after repeated stress during adolescence. The magnitude and dendritic location of these differences varied between the BA and LAT nuclei in strong contrast to the stress-induced increases in spine number seen in adults. These results demonstrate that repeated stress during adolescence has markedly different effects on BLA neuronal morphology, and the extent of these changes is BLA nucleus-dependent. Moreover, altered neuroanatomy was associated with age-dependent effects of repeated stress on generalization of fear, and may point to the necessity for different approaches to target stress-induced changes in adolescents.

Structured synaptic inhibition has a critical role in multiple-choice motion-discrimination tasks

Neural network models have been constructed to explore the underlying neural mechanisms for decision-making in multiple-choice motion-discrimination tasks. Despite great progress made, several key experimental observations have not been interpreted. In contrast to homogeneous connectivity between pyramidal cells and interneurons in previous models, here their connectivity is totally structured in a continuous recurrent network model. Specifically, we assume two types of inhibitory connectivity: opposite-feature and similar-feature inhibition, representing that the connectivity strength has a maximum between neural pairs with opposite and identical preferred directions, respectively. With a common parameter set, the model accounted for a wide variety of physiological and behavioral data from monkey experiments, including those that previous models failed to reproduce. We found that the opposite-feature inhibition endows the decision-making circuit with an elimination strategy, which effectively reduces the number of choice alternatives for inspection to speed up the decision process at the cost of decision accuracy. Conversely, the similar-feature inhibition markedly enhances the ability of the network to make a choice among multiple options and improves the accuracy of decisions, while slowing down the decision process. A simplified mean-field model was also presented to analytically characterize the effect of structured inhibition on fine discrimination. We made a testable prediction: only the combination of cross-feature and similar-feature inhibition enables the circuit to make a categorical choice among 12 alternatives. Together, the current work highlights the importance of structured synaptic inhibition in multiple-choice decision-making processes and sheds light on the neural mechanisms for visual motion perception.

The formation of multi-synaptic connections by the interaction of synaptic and structural plasticity and their functional consequences

Cortical connectivity emerges from the permanent interaction between neuronal activity and synaptic as well as structural plasticity. An important experimentally observed feature of this connectivity is the distribution of the number of synapses from one neuron to another, which has been measured in several cortical layers. All of these distributions are bimodal with one peak at zero and a second one at a small number (3–8) of synapses.

In this study, using a probabilistic model of structural plasticity, which depends on the synaptic weights, we explore how these distributions can emerge and which functional consequences they have.

We find that bimodal distributions arise generically from the interaction of structural plasticity with synaptic plasticity rules that fulfill the following biological realistic constraints: First, the synaptic weights have to grow with the postsynaptic activity. Second, this growth curve and/or the input-output relation of the postsynaptic neuron have to change sub-linearly (negative curvature). As most neurons show such input-output-relations, these constraints can be fulfilled by many biological reasonable systems.

Given such a system, we show that the different activities, which can explain the layer-specific distributions, correspond to experimentally observed activities.

Considering these activities as working point of the system and varying the pre- or postsynaptic stimulation reveals a hysteresis in the number of synapses. As a consequence of this, the connectivity between two neurons can be controlled by activity but is also safeguarded against overly fast changes.

These results indicate that the complex dynamics between activity and plasticity will, already between a pair of neurons, induce a variety of possible stable synaptic distributions, which could support memory mechanisms.

The connectivity between neurons is modified by different mechanisms. On a time scale of minutes to hours one finds synaptic plasticity, whereas mechanisms for structural changes at axons or dendrites may take days. One main factor determining structural changes is the weight of a connection, which, in turn, is adapted by synaptic plasticity. Both mechanisms, synaptic and structural plasticity, are influenced and determined by the activity pattern in the network. Hence, it is important to understand how activity and the different plasticity mechanisms influence each other. Especially how activity influences rewiring in adult networks is still an open question.

We present a model, which captures these complex interactions by abstracting structural plasticity with weight-dependent probabilities. This allows for calculating the distribution of the number of synapses between two neurons analytically. We report that biologically realistic connection patterns for different cortical layers generically arise with synaptic plasticity rules in which the synaptic weights grow with postsynaptic activity. The connectivity patterns also lead to different activity levels resembling those found in the different cortical layers. Interestingly such a system exhibits a hysteresis by which connections remain stable longer than expected, which may add to the stability of information storage in the network.

Electrophysiological and morphological characterization of neuronal microcircuits in acute brain slices using paired patch-clamp recordings

The combination of patch clamp recordings from two (or more) synaptically coupled neurons (paired recordings) in acute brain slice preparations with simultaneous intracellular biocytin filling allows a correlated analysis of their structural and functional properties. With this method it is possible to identify and characterize both pre- and postsynaptic neurons by their morphology and electrophysiological response pattern. Paired recordings allow studying the connectivity patterns between these neurons as well as the properties of both chemical and electrical synaptic transmission. Here, we give a step-by-step description of the procedures required to obtain reliable paired recordings together with an optimal recovery of the neuron morphology. We will describe how pairs of neurons connected via chemical synapses or gap junctions are identified in brain slice preparations. We will outline how neurons are reconstructed to obtain their 3D morphology of the dendritic and axonal domain and how synaptic contacts are identified and localized. We will also discuss the caveats and limitations of the paired recording technique, in particular those associated with dendritic and axonal truncations during the preparation of brain slices because these strongly affect connectivity estimates. However, because of the versatility of the paired recording approach it will remain a valuable tool in characterizing different aspects of synaptic transmission at identified neuronal microcircuits in the brain.

Electrophysiological properties of genetically identified subtypes of layer 5 neocortical pyramidal neurons: Ca²⁺ dependence and differential modulation by norepinephrine

We studied neocortical pyramidal neurons from two lines of bacterial artificial chromosome mice (etv1 and glt; Gene Expression Nervous System Atlas: GENSAT project), each of which expresses enhanced green fluorescent protein (EGFP) in a different subpopulation of layer 5 pyramidal neurons. In barrel cortex, etv1 and glt pyramidal cells were previously reported to differ in terms of their laminar distribution, morphology, thalamic inputs, cellular targets, and receptive field size. In this study, we measured the laminar distribution of etv1 and glt cells. On average, glt cells were located more deeply; however, the distributions of etv1 and glt cells extensively overlap in layer 5. To test whether these two cell types differed in electrophysiological properties that influence firing behavior, we prepared acute brain slices from 2-4-wk-old mice, where EGFP-positive cells in somatosensory cortex were identified under epifluorescence and then studied using whole cell current- or voltage-clamp recordings. We studied the details of action potential parameters and repetitive firing, characterized by the larger slow afterhyperpolarizations (AHPs) in etv1 neurons and larger medium AHPs (mAHPS) in glt cells, and compared currents underlying the mAHP and slow AHP (sAHP) in etv1 and glt neurons. Etv1 cells exhibited lower dV/dt for spike polarization and repolarization and reduced direct current (DC) gain (lower f-I slope) for repetitive firing than glt cells. Most importantly, we found that 1) differences in the expression of Ca(2+)-dependent K(+) conductances (small-conductance calcium-activated potassium channels and sAHP channels) determine major functional differences between etv1 and glt cells, and 2) there is differential modulation of etv1 and glt neurons by norepinephrine.

Stimulus dependence of local field potential spectra: experiment versus theory

The local field potential (LFP) captures different neural processes, including integrative synaptic dynamics that cannot be observed by measuring only the spiking activity of small populations. Therefore, investigating how LFP power is modulated by external stimuli can offer important insights into sensory neural representations. However, gaining such insight requires developing data-driven computational models that can identify and disambiguate the neural contributions to the LFP. Here, we investigated how networks of excitatory and inhibitory integrate-and-fire neurons responding to time-dependent inputs can be used to interpret sensory modulations of LFP spectra. We computed analytically from such models the LFP spectra and the information that they convey about input and used these analytical expressions to fit the model to LFPs recorded in V1 of anesthetized macaques (Macaca mulatta) during the presentation of color movies. Our expressions explain 60%-98% of the variance of the LFP spectrum shape and its dependency upon movie scenes and we achieved this with realistic values for the best-fit parameters. In particular, synaptic best-fit parameters were compatible with experimental measurements and the predictions of firing rates, based only on the fit of LFP data, correlated with the multiunit spike rate recorded from the same location. Moreover, the parameters characterizing the input to the network across different movie scenes correlated with cross-scene changes of several image features. Our findings suggest that analytical descriptions of spiking neuron networks may become a crucial tool for the interpretation of field recordings.

Integrate-and-fire neurons driven by asymmetric dichotomous noise

We consider a general integrate-and-fire (IF) neuron driven by asymmetric dichotomous noise. In contrast to the Gaussian white noise usually used in the so-called diffusion approximation, this noise is colored, i.e., it exhibits temporal correlations. We give an analytical expression for the stationary voltage distribution of a neuron receiving such noise and derive recursive relations for the moments of the first passage time density, which allow us to calculate the firing rate and the coefficient of variation of interspike intervals. We study how correlations in the input affect the rate and regularity of firing under variation of the model's parameters for leaky and quadratic IF neurons. Further, we consider the limit of small correlation times and find lowest order corrections to the first passage time moments to be proportional to the square root of the correlation time. We show analytically that to this lowest order, correlations always lead to a decrease in firing rate for a leaky IF neuron. All theoretical expressions are compared to simulations of leaky and quadratic IF neurons.

Single dendrite-targeting interneurons generate branch-specific inhibition

Microcircuits composed of dendrite-targeting inhibitory interneurons and pyramidal cells (PCs) are fundamental elements of cortical networks, however, the impact of individual interneurons on pyramidal dendrites is unclear. Here, we combine paired recordings and calcium imaging to determine the spatial domain over which single dendrite-targeting interneurons influence PCs in olfactory cortex. We show that a major action of individual interneurons is to inhibit dendrites in a branch-specific fashion.

Postsynaptic mGluR5 promotes evoked AMPAR-mediated synaptic transmission onto neocortical layer 2/3 pyramidal neurons during development

Both short- and long-term roles for the group I metabotropic glutamate receptor number 5 (mGluR5) have been examined for the regulation of cortical glutamatergic synapses. However, how mGluR5 sculpts neocortical networks during development still remains unclear. Using a single cell deletion strategy, we examined how mGluR5 regulates glutamatergic synaptic pathways in neocortical layer 2/3 (L2/3) during development. Electrophysiological measurements were made in acutely prepared slices to obtain a functional understanding of the effects stemming from loss of mGluR5 in vivo. Loss of postsynaptic mGluR5 results in an increase in the frequency of action potential-independent synaptic events but, paradoxically, results in a decrease in evoked transmission in two separate synaptic pathways providing input to the same pyramidal neurons. Synaptic transmission through α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, but not N-methyl-d-aspartate (NMDA) receptors, is specifically decreased. In the local L2/3 pathway, the decrease in evoked transmission appears to be largely due to a decrease in cell-to-cell connectivity and not in the strength of individual cell-to-cell connections. This decrease in evoked transmission correlates with a decrease in the total dendritic length in a region of the dendritic arbor that likely receives substantial input from these two pathways, thereby suggesting a morphological correlate to functional alterations. These changes are accompanied by an increase in intrinsic membrane excitability. Our data indicate that total mGluR5 function, incorporating both short- and long-term processes, promotes the strengthening of AMPA receptor-mediated transmission in multiple neocortical pathways.

Optical imaging of the propagation patterns of neural responses in the rat sensory cortex: comparison under two different anesthetic conditions

Although many studies have reported the influence of anesthetics on the shape of somatic evoked potential, none has evaluated the influence on the spatio-temporal pattern of neural activity in detail. It is practically impossible to analyze neural activities spatially, using conventional electrophysiological methods. Applying our multiple-site optical recording technique for measuring membrane potential from multiple-sites with a high time resolution, we compared the spatio-temporal pattern of the evoked activity under two different anesthetic conditions induced by urethane or α-chloralose. The somatic cortical response was evoked by electrical stimulation of the hindlimb, and the optical signals were recorded from the rat sensorimotor cortex stained with a voltage-sensitive dye (RH414). The evoked activity emerged in a restricted area and propagated in a concentric manner. The spatio-temporal pattern of the evoked activity was analyzed using isochrone maps. There were significant differences in the latency and propagation velocity of the evoked activity, as well as the full width at half maximum of optical signal between the two anesthetic conditions. Differences in the amplitude and the slope of the rising phase were not significant.

Memory maintenance in synapses with calcium-based plasticity in the presence of background activity

Most models of learning and memory assume that memories are maintained in neuronal circuits by persistent synaptic modifications induced by specific patterns of pre- and postsynaptic activity. For this scenario to be viable, synaptic modifications must survive the ubiquitous ongoing activity present in neural circuits in vivo. In this paper, we investigate the time scales of memory maintenance in a calcium-based synaptic plasticity model that has been shown recently to be able to fit different experimental data-sets from hippocampal and neocortical preparations. We find that in the presence of background activity on the order of 1 Hz parameters that fit pyramidal layer 5 neocortical data lead to a very fast decay of synaptic efficacy, with time scales of minutes. We then identify two ways in which this memory time scale can be extended: (i) the extracellular calcium concentration in the experiments used to fit the model are larger than estimated concentrations in vivo. Lowering extracellular calcium concentration to in vivo levels leads to an increase in memory time scales of several orders of magnitude; (ii) adding a bistability mechanism so that each synapse has two stable states at sufficiently low background activity leads to a further boost in memory time scale, since memory decay is no longer described by an exponential decay from an initial state, but by an escape from a potential well. We argue that both features are expected to be present in synapses in vivo. These results are obtained first in a single synapse connecting two independent Poisson neurons, and then in simulations of a large network of excitatory and inhibitory integrate-and-fire neurons. Our results emphasise the need for studying plasticity at physiological extracellular calcium concentration, and highlight the role of synaptic bi- or multistability in the stability of learned synaptic structures.

Synaptic plasticity is widely believed to be the main mechanism underlying learning and memory. In recent years, several mathematical plasticity rules have been shown to fit satisfactorily a wide range of experimental data in hippocampal and neocortical in vitro preparations. In particular, a model in which plasticity is driven by the postsynaptic calcium concentration was shown to reproduce successfully how synaptic changes depend on spike timing, specific spike patterns, and firing rate. The advantage of calcium-based rules is the possibility of predicting how changes in extracellular concentrations will affect plasticity. This is particularly significant in the view that in vitro studies are typically done at higher concentrations than the ones measured in vivo. Using such a rule, with parameters fitting in vitro data, we explore how long the memory of a particular synaptic change can be maintained in the presence of background neuronal activity, ubiquitously observed in cortex. We find that the memory time scales increase by several orders of magnitude when calcium concentrations are lowered from typical in vitro experiments to in vivo. Furthermore, we find that synaptic bistability further extends the memory time scale, and estimate that synaptic changes in vivo could be stable on the scale of weeks to months.

Deep and precise quantification of the mouse synaptosomal proteome reveals substantial remodeling during postnatal maturation

During postnatal murine maturation, behavioral patterns emerge and become shaped by experience-dependent adaptations. During the same period, the morphology of dendritic spines, the morphological correlates of excitatory synapses, is known to change, and there is evidence of concurrent alterations of the synaptosomal protein machinery. To obtain comprehensive and quantitative insights in the developmental regulation of the proteome of synapses, we prepared cortical synaptosomal fractions from a total of 16 individual juvenile and adult mouse brains (age 3 or 8 weeks, respectively). We then applied peptide-based iTRAQ labeling (four pools of 4 animals) and high-resolution two-dimensional peptide fractionation (99 SCX fractions and 3 h reversed-phase gradients) using a hybrid CID-HCD acquisition method on a Velos Orbitrap mass spectrometer to identify a comprehensive set of synaptic proteins and to quantify changes in protein expression. We obtained a data set tracking expression levels of 3500 proteins mapping to 3427 NCBI GeneIDs during development with complete quantification data available for 3422 GeneIDs, which, to the best of our knowledge, constitutes the deepest coverage of the synaptosome proteome to date. The inclusion of biological replicates in a single mass spectrometry analysis demonstrated both high reproducibility of our synaptosome preparation method as well as high precision of our quantitative data (correlation coefficient R = 0.87 for the biological replicates). To evaluate the validity of our data, the developmental regulation of eight proteins identified in our analysis was confirmed independently using western blotting. A gene ontology analysis confirmed the synaptosomal nature of a large fraction of identified proteins. Of note, the set of the most strongly regulated proteins revealed candidates involved in neurological processes in health and disease states. This highlights the fact that developmentally regulated proteins can play additional roles in neurological disease processes. All data have been deposited to the ProteomeXchange with identifier PXD000552.

Analytical approximations of the firing rate of an adaptive exponential integrate-and-fire neuron in the presence of synaptic noise

Computational models offer a unique tool for understanding the network-dynamical mechanisms which mediate between physiological and biophysical properties, and behavioral function. A traditional challenge in computational neuroscience is, however, that simple neuronal models which can be studied analytically fail to reproduce the diversity of electrophysiological behaviors seen in real neurons, while detailed neuronal models which do reproduce such diversity are intractable analytically and computationally expensive. A number of intermediate models have been proposed whose aim is to capture the diversity of firing behaviors and spike times of real neurons while entailing the simplest possible mathematical description. One such model is the exponential integrate-and-fire neuron with spike rate adaptation (aEIF) which consists of two differential equations for the membrane potential (V) and an adaptation current (w). Despite its simplicity, it can reproduce a wide variety of physiologically observed spiking patterns, can be fit to physiological recordings quantitatively, and, once done so, is able to predict spike times on traces not used for model fitting. Here we compute the steady-state firing rate of aEIF in the presence of Gaussian synaptic noise, using two approaches. The first approach is based on the 2-dimensional Fokker-Planck equation that describes the (V,w)-probability distribution, which is solved using an expansion in the ratio between the time constants of the two variables. The second is based on the firing rate of the EIF model, which is averaged over the distribution of the w variable. These analytically derived closed-form expressions were tested on simulations from a large variety of model cells quantitatively fitted to in vitro electrophysiological recordings from pyramidal cells and interneurons. Theoretical predictions closely agreed with the firing rate of the simulated cells fed with in-vivo-like synaptic noise.

Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits

Layer 5 thick tufted pyramidal cells (TTCs) in the neocortex are particularly electrically complex, owing to their highly excitable dendrites. The interplay between dendritic nonlinearities and recurrent cortical microcircuit activity in shaping network response is largely unknown. We simulated detailed conductance-based models of TTCs forming recurrent microcircuits that were interconnected as found experimentally; the network was embedded in a realistic background synaptic activity. TTCs microcircuits significantly amplified brief thalamocortical inputs; this cortical gain was mediated by back-propagation activated N-methyl-d-aspartate depolarizations and dendritic back-propagation-activated Ca²⁺ spike firing, ignited by the coincidence of thalamic-activated somatic spike and local dendritic synaptic inputs, originating from the cortical microcircuit. Surprisingly, dendritic nonlinearities in TTCs microcircuits linearly multiplied thalamic inputs—amplifying them while maintaining input selectivity. Our findings indicate that dendritic nonlinearities are pivotal in controlling the gain and the computational functions of TTCs microcircuits, which serve as a dominant output source for the neocortex.

A new efficient method for synaptic vesicle quantification reveals differences between medial prefrontal cortex perforated and nonperforated synapses

Communication between neurons is mediated by the release of neurotransmitter-containing vesicles from presynaptic terminals. Quantitative characterization of synaptic vesicles can be highly valuable for understanding mechanisms underlying synaptic function and plasticity. We performed a quantitative ultrastructural analysis of cortical excitatory synapses by mean of a new, efficient method, as an alternative to three-dimensional (3D) reconstruction. Based on a hierarchical sampling strategy and unequivocal identification of the region of interest, serial sections from excitatory synapses of medial prefrontal cortex (mPFC) of six Sprague-Dawley rats were acquired with a transmission electron microscope. Unbiased estimates of total 3D volume of synaptic terminals were obtained through the Cavalieri estimator, and adequate correction factors for vesicle profile number estimation were applied for final vesicle quantification. Our analysis was based on 79 excitatory synapses, nonperforated (NPSs) and perforated (PSs) subtypes. We found that total number of docked and reserve-pool vesicles in PSs significantly exceeded that in NPSs (by, respectively, 77% and 78%). These differences were found to be related to changes in size between the two subtypes (active zone area by 86%; bouton volume by 105%) rather than to postsynaptic density shape. Positive significant correlations were found between number of docked and reserve-pool vesicles, active zone area and docked vesicles, and bouton volume and reserve pool vesicles. Our method confirmed the large size of mPFC PSs and a linear correlation between presynaptic features of typical hippocampal synapses. Moreover, a greater number of docked vesicles in PSs may promote a high synaptic strength of these synapses.

Simulating vertical and horizontal inhibition with short-term dynamics in a multi-column multi-layer model of neocortex

The paper introduces a multi-layer multi-column model of the cortex that uses four different neuron types and short-term plasticity dynamics. It was designed with details of neuronal connectivity available in the literature and meets these conditions: (1) biologically accurate laminar and columnar flows of activity, (2) normal function of low-threshold spiking and fast spiking neurons, and (3) ability to generate different stages of epileptiform activity. With these characteristics the model allows for modeling lesioned or malformed cortex, i.e. examine properties of developmentally malformed cortex in which the balance between inhibitory neuron subtypes is disturbed.

Spatially distributed dendritic resonance selectively filters synaptic input

An important task performed by a neuron is the selection of relevant inputs from among thousands of synapses impinging on the dendritic tree. Synaptic plasticity enables this by strenghtening a subset of synapses that are, presumably, functionally relevant to the neuron. A different selection mechanism exploits the resonance of the dendritic membranes to preferentially filter synaptic inputs based on their temporal rates. A widely held view is that a neuron has one resonant frequency and thus can pass through one rate. Here we demonstrate through mathematical analyses and numerical simulations that dendritic resonance is inevitably a spatially distributed property; and therefore the resonance frequency varies along the dendrites, and thus endows neurons with a powerful spatiotemporal selection mechanism that is sensitive both to the dendritic location and the temporal structure of the incoming synaptic inputs.

Reduced chemical and electrical connections of fast-spiking interneurons in experimental cortical dysplasia

Aberrant neural connections are regarded as a principal factor contributing to epileptogenesis. This study examined chemical and electrical connections between fast-spiking (FS), parvalbumin (PV)-immunoreactive (FS-PV) interneurons and regular-spiking (RS) neurons (pyramidal neurons or spiny stellate neurons) in a rat model of prenatal irradiation-induced cortical dysplasia. Presynaptic action potentials were evoked by current injection and the elicited unitary inhibitory or excitatory postsynaptic potentials (uIPSPs or uEPSPs) were recorded in the postsynaptic cell. In dysplastic cortex, connection rates between presynaptic FS-PV interneurons and postsynaptic RS neurons and FS-PV interneurons, and uIPSP amplitudes were significantly smaller than controls, but both failure rates and coefficient of variation of uIPSP amplitudes were larger than controls. In contrast, connection rates from RS neurons to FS-PV interneurons and uEPSPs amplitude were similar in the two groups. Assessment of the paired pulse ratio showed a significant decrease in synaptic release probability at FS-PV interneuronal terminals, and the density of terminal boutons on axons of biocytin-filled FS-PV interneurons was also decreased, suggesting presynaptic dysfunction in chemical synapses formed by FS-PV interneurons. Electrical connections were observed between FS-PV interneurons, and the connection rates and coupling coefficients were smaller in dysplastic cortex than controls. In dysplastic cortex, we found a reduced synaptic efficiency for uIPSPs originating from FS-PV interneurons regardless of the type of target cell, and impaired electrical connections between FS-PV interneurons. This expands our understanding of the fundamental impairment of inhibition in this model and may have relevance for certain types of human cortical dysplasia.

Connection-type-specific biases make uniform random network models consistent with cortical recordings

Uniform random sparse network architectures are ubiquitous in computational neuroscience, but the implicit hypothesis that they are a good representation of real neuronal networks has been met with skepticism. Here we used two experimental data sets, a study of triplet connectivity statistics and a data set measuring neuronal responses to channelrhodopsin stimuli, to evaluate the fidelity of thousands of model networks. Network architectures comprised three neuron types (excitatory, fast spiking, and nonfast spiking inhibitory) and were created from a set of rules that govern the statistics of the resulting connection types. In a high-dimensional parameter scan, we varied the degree distributions (i.e., how many cells each neuron connects with) and the synaptic weight correlations of synapses from or onto the same neuron. These variations converted initially uniform random and homogeneously connected networks, in which every neuron sent and received equal numbers of synapses with equal synaptic strength distributions, to highly heterogeneous networks in which the number of synapses per neuron, as well as average synaptic strength of synapses from or to a neuron were variable. By evaluating the impact of each variable on the network structure and dynamics, and their similarity to the experimental data, we could falsify the uniform random sparse connectivity hypothesis for 7 of 36 connectivity parameters, but we also confirmed the hypothesis in 8 cases. Twenty-one parameters had no substantial impact on the results of the test protocols we used.

Long-term recordings improve the detection of weak excitatory-excitatory connections in rat prefrontal cortex

Characterization of synaptic connectivity is essential to understanding neural circuit dynamics. For extracellularly recorded spike trains, indirect evidence for connectivity can be inferred from short-latency peaks in the correlogram between two neurons. Despite their predominance in cortex, however, significant interactions between excitatory neurons (E) have been hard to detect because of their intrinsic weakness. By taking advantage of long duration recordings, up to 25 h, from rat prefrontal cortex, we found that 7.6% of the recorded pyramidal neurons are connected. This corresponds to ∼70% of the local E-E connection probability that has been reported by paired intracellular recordings (11.6%). This value is significantly higher than previous reports from extracellular recordings, but still a substantial underestimate. Our analysis showed that long recording times and strict significance thresholds are necessary to detect weak connections while avoiding false-positive results, but will likely still leave many excitatory connections undetected. In addition, we found that hyper-reciprocity of connections in prefrontal cortex that was shown previously by paired intracellular recordings was only present in short-distance, but not in long distance (∼300 micrometers or more) interactions. As hyper-reciprocity is restricted to local clusters, it might be a minicolumnar effect. Given the current surge of interest in very high-density neural spike recording (e.g., NIH BRAIN Project) it is of paramount importance that we have statistically reliable methods for estimating connectivity from cross-correlation analysis available. We provide an important step in this direction.

The role of nicotinic receptors in shaping and functioning of the glutamatergic system: a window into cognitive pathology

The involvement of the cholinergic system in learning, memory and attention has long been recognized, although its neurobiological mechanisms are not fully understood. Recent evidence identifies the endogenous cholinergic signaling via nicotinic acetylcholine receptors (nAChRs) as key players in determining the morphological and functional maturation of the glutamatergic system. Here, we review the available experimental and clinical evidence of nAChRs contribution to the establishment of the glutamatergic system, and therefore to cognitive function. We provide some clues of the putative underlying molecular mechanisms and discuss recent human studies that associate genetic variability of the genes encoding nAChR subunits with cognitive disorders. Finally, we discuss the new avenues to therapeutically targeting nAChRs in persons with cognitive dysfunction for which the α7-nAChR subunit is an important etiological mechanism.

Structural synaptic plasticity has high memory capacity and can explain graded amnesia, catastrophic forgetting, and the spacing effect

Although already William James and, more explicitly, Donald Hebb's theory of cell assemblies have suggested that activity-dependent rewiring of neuronal networks is the substrate of learning and memory, over the last six decades most theoretical work on memory has focused on plasticity of existing synapses in prewired networks. Research in the last decade has emphasized that structural modification of synaptic connectivity is common in the adult brain and tightly correlated with learning and memory. Here we present a parsimonious computational model for learning by structural plasticity. The basic modeling units are "potential synapses" defined as locations in the network where synapses can potentially grow to connect two neurons. This model generalizes well-known previous models for associative learning based on weight plasticity. Therefore, existing theory can be applied to analyze how many memories and how much information structural plasticity can store in a synapse. Surprisingly, we find that structural plasticity largely outperforms weight plasticity and can achieve a much higher storage capacity per synapse. The effect of structural plasticity on the structure of sparsely connected networks is quite intuitive: Structural plasticity increases the "effectual network connectivity", that is, the network wiring that specifically supports storage and recall of the memories. Further, this model of structural plasticity produces gradients of effectual connectivity in the course of learning, thereby explaining various cognitive phenomena including graded amnesia, catastrophic forgetting, and the spacing effect.

Early hypersynchrony in juvenile PINK1(-)/(-) motor cortex is rescued by antidromic stimulation

In Parkinson’s disease (PD), cortical networks show enhanced synchronized activity but whether this precedes motor signs is unknown. We investigated this question in PINK1⁻/⁻ mice, a genetic rodent model of the PARK6 variant of familial PD which shows impaired spontaneous locomotion at 16 months. We used two-photon calcium imaging and whole-cell patch clamp in slices from juvenile (P14–P21) wild-type or PINK1⁻/⁻ mice. We designed a horizontal tilted cortico-subthalamic slice where the only connection between cortex and subthalamic nucleus (STN) is the hyperdirect cortico-subthalamic pathway. We report excessive correlation and synchronization in PINK1⁻/⁻ M1 cortical networks 15 months before motor impairment. The percentage of correlated pairs of neurons and their strength of correlation were higher in the PINK1⁻/⁻ M1 than in the wild type network and the synchronized network events involved a higher percentage of neurons. Both features were independent of thalamo-cortical pathways, insensitive to chronic levodopa treatment of pups, but totally reversed by antidromic invasion of M1 pyramidal neurons by axonal spikes evoked by high frequency stimulation (HFS) of the STN. Our study describes an early excess of synchronization in the PINK1⁻/⁻ cortex and suggests a potential role of antidromic activation of cortical interneurons in network desynchronization. Such backward effect on interneurons activity may be of importance for HFS-induced network desynchronization.

Glutamate-bound NMDARs arising from in vivo-like network activity extend spatio-temporal integration in a L5 cortical pyramidal cell model

In vivo, cortical pyramidal cells are bombarded by asynchronous synaptic input arising from ongoing network activity. However, little is known about how such 'background' synaptic input interacts with nonlinear dendritic mechanisms. We have modified an existing model of a layer 5 (L5) pyramidal cell to explore how dendritic integration in the apical dendritic tuft could be altered by the levels of network activity observed in vivo. Here we show that asynchronous background excitatory input increases neuronal gain and extends both temporal and spatial integration of stimulus-evoked synaptic input onto the dendritic tuft. Addition of fast and slow inhibitory synaptic conductances, with properties similar to those from dendritic targeting interneurons, that provided a 'balanced' background configuration, partially counteracted these effects, suggesting that inhibition can tune spatio-temporal integration in the tuft. Excitatory background input lowered the threshold for NMDA receptor-mediated dendritic spikes, extended their duration and increased the probability of additional regenerative events occurring in neighbouring branches. These effects were also observed in a passive model where all the non-synaptic voltage-gated conductances were removed. Our results show that glutamate-bound NMDA receptors arising from ongoing network activity can provide a powerful spatially distributed nonlinear dendritic conductance. This may enable L5 pyramidal cells to change their integrative properties as a function of local network activity, potentially allowing both clustered and spatially distributed synaptic inputs to be integrated over extended timescales.

Structured connectivity in cerebellar inhibitory networks

Defining the rules governing synaptic connectivity is key to formulating theories of neural circuit function. Interneurons can be connected by both electrical and chemical synapses, but the organization and interaction of these two complementary microcircuits is unknown. By recording from multiple molecular layer interneurons in the cerebellar cortex, we reveal specific, nonrandom connectivity patterns in both GABAergic chemical and electrical interneuron networks. Both networks contain clustered motifs and show specific overlap between them. Chemical connections exhibit a preference for transitive patterns, such as feedforward triplet motifs. This structured connectivity is supported by a characteristic spatial organization: transitivity of chemical connectivity is directed vertically in the sagittal plane, and electrical synapses appear strictly confined to the sagittal plane. The specific, highly structured connectivity rules suggest that these motifs are essential for the function of the cerebellar network.

Dense neuron clustering explains connectivity statistics in cortical microcircuits

Local cortical circuits appear highly non-random, but the underlying connectivity rule remains elusive. Here, we analyze experimental data observed in layer 5 of rat neocortex and suggest a model for connectivity from which emerge essential observed non-random features of both wiring and weighting. These features include lognormal distributions of synaptic connection strength, anatomical clustering, and strong correlations between clustering and connection strength. Our model predicts that cortical microcircuits contain large groups of densely connected neurons which we call clusters. We show that such a cluster contains about one fifth of all excitatory neurons of a circuit which are very densely connected with stronger than average synapses. We demonstrate that such clustering plays an important role in the network dynamics, namely, it creates bistable neural spiking in small cortical circuits. Furthermore, introducing local clustering in large-scale networks leads to the emergence of various patterns of persistent local activity in an ongoing network activity. Thus, our results may bridge a gap between anatomical structure and persistent activity observed during working memory and other cognitive processes.

The log-dynamic brain: how skewed distributions affect network operations

We often assume that the variables of functional and structural brain parameters - such as synaptic weights, the firing rates of individual neurons, the synchronous discharge of neural populations, the number of synaptic contacts between neurons and the size of dendritic boutons - have a bell-shaped distribution. However, at many physiological and anatomical levels in the brain, the distribution of numerous parameters is in fact strongly skewed with a heavy tail, suggesting that skewed (typically lognormal) distributions are fundamental to structural and functional brain organization. This insight not only has implications for how we should collect and analyse data, it may also help us to understand how the different levels of skewed distributions - from synapses to cognition - are related to each other.