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

On the Structure of Cortical Microcircuits Inferred from Small Sample Sizes

The structure in cortical microcircuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue, we sought to reproduce the nonrandom features of cortical circuits by considering several distinct classes of network topology, including clustered networks, networks with distance-dependent connectivity, and those with broad degree distributions. To our surprise, we found that all of these qualitatively distinct topologies could account equally well for all reported nonrandom features despite being easily distinguishable from one another at the network level. This apparent paradox was a consequence of estimating network properties given only small sample sizes. In other words, networks that differ markedly in their global structure can look quite similar locally. This makes inferring network structure from small sample sizes, a necessity given the technical difficulty inherent in simultaneous intracellular recordings, problematic. We found that a network statistic called the sample degree correlation (SDC) overcomes this difficulty. The SDC depends only on parameters that can be estimated reliably given small sample sizes and is an accurate fingerprint of every topological family. We applied the SDC criterion to data from rat visual and somatosensory cortex and discovered that the connectivity was not consistent with any of these main topological classes. However, we were able to fit the experimental data with a more general network class, of which all previous topologies were special cases. The resulting network topology could be interpreted as a combination of physical spatial dependence and nonspatial, hierarchical clustering.SIGNIFICANCE STATEMENT The connectivity of cortical microcircuits exhibits features that are inconsistent with a simple random network. Here, we show that several classes of network models can account for this nonrandom structure despite qualitative differences in their global properties. This apparent paradox is a consequence of the small numbers of simultaneously recorded neurons in experiment: when inferred via small sample sizes, many networks may be indistinguishable despite being globally distinct. We develop a connectivity measure that successfully classifies networks even when estimated locally with a few neurons at a time. We show that data from rat cortex is consistent with a network in which the likelihood of a connection between neurons depends on spatial distance and on nonspatial, asymmetric clustering.

SynEM, automated synapse detection for connectomics

Nerve tissue contains a high density of chemical synapses, about 1 per µm3 in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.

Modulating STDP Balance Impacts the Dendritic Mosaic

The ability for cortical neurons to adapt their input/output characteristics and information processing capabilities ultimately relies on the interplay between synaptic plasticity, synapse location, and the nonlinear properties of the dendrite. Collectively, they shape both the strengths and spatial arrangements of convergent afferent inputs to neuronal dendrites. Recent experimental and theoretical studies support a clustered plasticity model, a view that synaptic plasticity promotes the formation of clusters or hotspots of synapses sharing similar properties. We have previously shown that spike timing-dependent plasticity (STDP) can lead to synaptic efficacies being arranged into spatially segregated clusters. This effectively partitions the dendritic tree into a tessellated imprint which we have called a dendritic mosaic. Here, using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and STDP learning, we investigated the impact of altered STDP balance on forming such a spatial organization. We show that cluster formation and extend depend on several factors, including the balance between potentiation and depression, the afferents' mean firing rate and crucially on the dendritic morphology. We find that STDP balance has an important role to play for this emergent mode of spatial organization since any imbalances lead to severe degradation- and in some case even destruction- of the mosaic. Our model suggests that, over a broad range of of STDP parameters, synaptic plasticity shapes the spatial arrangement of synapses, favoring the formation of clustered efficacy engrams.

Exact analytical results for integrate-and-fire neurons driven by excitatory shot noise

A neuron receives input from other neurons via electrical pulses, so-called spikes. The pulse-like nature of the input is frequently neglected in analytical studies; instead, the input is usually approximated to be Gaussian. Recent experimental studies have shown, however, that an assumption underlying this approximation is often not met: Individual presynaptic spikes can have a significant effect on a neuron's dynamics. It is thus desirable to explicitly account for the pulse-like nature of neural input, i.e. consider neurons driven by a shot noise - a long-standing problem that is mathematically challenging. In this work, we exploit the fact that excitatory shot noise with exponentially distributed weights can be obtained as a limit case of dichotomous noise, a Markovian two-state process. This allows us to obtain novel exact expressions for the stationary voltage density and the moments of the interspike-interval density of general integrate-and-fire neurons driven by such an input. For the special case of leaky integrate-and-fire neurons, we also give expressions for the power spectrum and the linear response to a signal. We verify and illustrate our expressions by comparison to simulations of leaky-, quadratic- and exponential integrate-and-fire neurons.

From single cells and single columns to cortical networks: dendritic excitability, coincidence detection and synaptic transmission in brain slices and brains

Although patch pipettes were initially designed to record extracellularly the elementary current events from muscle and neuron membranes, the whole-cell and loose cell-attached recording configurations proved to be useful tools for examination of signalling within and between nerve cells. In this Paton Prize Lecture, I will initially summarize work on electrical signalling within single neurons, describing communication between the dendritic compartments, soma and nerve terminals via forward- and backward-propagating action potentials. The newly discovered dendritic excitability endows neurons with the capacity for coincidence detection of spatially separated subthreshold inputs. When these are occurring during a time window of tens of milliseconds, this information is broadcast to other cells by the initiation of bursts of action potentials (AP bursts). The occurrence of AP bursts critically impacts signalling between neurons that are controlled by target-cell-specific transmitter release mechanisms at downstream synapses even in different terminals of the same neuron. This can, in turn, induce mechanisms that underly synaptic plasticity when AP bursts occur within a short time window, both presynaptically in terminals and postsynaptically in dendrites. A fundamental question that arises from these findings is: 'what are the possible functions of active dendritic excitability with respect to network dynamics in the intact cortex of behaving animals?' To answer this question, I highlight in this review the functional and anatomical architectures of an average cortical column in the vibrissal (whisker) field of the somatosensory cortex (vS1), with an emphasis on the functions of layer 5 thick-tufted cells (L5tt) embedded in this structure. Sensory-evoked synaptic and action potential responses of these major cortical output neurons are compared with responses in the afferent pathway, viz. the neurons in primary somatosensory thalamus and in one of their efferent targets, the secondary somatosensory thalamus. Coincidence-detection mechanisms appear to be implemented in vivo as judged from the occurrence of AP bursts. Three-dimensional reconstructions of anatomical projections suggest that inputs of several combinations of thalamocortical projections and intra- and transcolumnar connections, specifically those from infragranular layers, could trigger active dendritic mechanisms that generate AP bursts. Finally, recordings from target cells of a column reveal the importance of AP bursts for signal transfer to these cells. The observations lead to the hypothesis that in vS1 cortex, the sensory afferent sensory code is transformed, at least in part, from a rate to an interval (burst) code that broadcasts the occurrence of whisker touch to different targets of L5tt cells. In addition, the occurrence of pre- and postsynaptic AP bursts may, in the long run, alter touch representation in cortex.

GABAb Receptor Mediates Opposing Adaptations of GABA Release From Two Types of Prefrontal Interneurons After Observational Fear

The observational fear (OF) paradigm in rodents, in which the subject is exposed to a distressed conspecific, elicits contextual fear learning and enhances future passive avoidance learning, which may model certain behavioral traits resulting from traumatic experiences in humans. As these behaviors affected by the OF require dorso-medial prefrontal cortex (dmPFC), we searched for synaptic adaptations in dmPFC resulting from OF in mice by recording synaptic responses in dmPFC layer V pyramidal neurons elicited by repeated 5 Hz electrical stimulation of dmPFC layer I or by optogenetic stimulation of specific interneurons ex vivo 1 day after OF. OF increased depression of inhibitory postsynaptic currents (IPSCs) along IPSC trains evoked by the 5 Hz electrical stimulation, but, surprisingly, decreased depression of dendritic IPSCs isolated after blocking GABAa receptor on the soma. Subsequent optogenetic analyses revealed increased depression of IPSCs originating from perisomatically projecting parvalbumin interneurons (PV-IPSCs), but decreased depression of IPSCs from dendritically projecting somatostatin cells (SOM-IPSCs). These changes were no longer detectable in the presence of a GABAb receptor antagonist CGP52432. Meanwhile, OF decreased the sensitivity of SOM-IPSCs, but not PV-IPSCs to a GABAb receptor agonist baclofen. Thus, OF causes opposing changes in GABAb receptor mediated suppression of GABA release from PV-positive and SOM-positive interneurons. Such adaptations may alter dmPFC connectivity with brain areas that target its deep vs superficial layers and thereby contribute to the behavioral consequences of the aversive experiences.

Multiassociative Memory: Recurrent Synapses Increase Storage Capacity

The connection density of nearby neurons in the cortex has been observed to be around 0.1, whereas the longer-range connections are present with much sparser density (Kalisman, Silberberg, & Markram, 2005 ). We propose a memory association model that qualitatively explains these empirical observations. The model we consider is a multiassociative, sparse, Willshaw-like model consisting of binary threshold neurons and binary synapses. It uses recurrent synapses for iterative retrieval of stored memories. We quantify the usefulness of recurrent synapses by simulating the model for small network sizes and by doing a precise mathematical analysis for large network sizes. Given the network parameters, we can determine the precise values of recurrent and afferent synapse densities that optimize the storage capacity of the network. If the network size is like that of a cortical column, then the predicted optimal recurrent density lies in a range that is compatible with biological measurements. Furthermore, we show that our model is able to surpass the standard Willshaw model in the multiassociative case if the information capacity is normalized per strong synapse or per bits required to store the model, as considered in Knoblauch, Palm, and Sommer ( 2010 ).

Optimal Degrees of Synaptic Connectivity

Synaptic connectivity varies widely across neuronal types. Cerebellar granule cells receive five orders of magnitude fewer inputs than the Purkinje cells they innervate, and cerebellum-like circuits, including the insect mushroom body, also exhibit large divergences in connectivity. In contrast, the number of inputs per neuron in cerebral cortex is more uniform and large. We investigate how the dimension of a representation formed by a population of neurons depends on how many inputs each neuron receives and what this implies for learning associations. Our theory predicts that the dimensions of the cerebellar granule-cell and Drosophila Kenyon-cell representations are maximized at degrees of synaptic connectivity that match those observed anatomically, showing that sparse connectivity is sometimes superior to dense connectivity. When input synapses are subject to supervised plasticity, however, dense wiring becomes advantageous, suggesting that the type of plasticity exhibited by a set of synapses is a major determinant of connection density.

Micro-connectomics: probing the organization of neuronal networks at the cellular scale

Defining the organizational principles of neuronal networks at the cellular scale, or micro-connectomics, is a key challenge of modern neuroscience. In this Review, we focus on graph theoretical parameters of micro-connectome topology, often informed by economical principles that conceptually originated with Ramón y Cajal's conservation laws. First, we summarize results from studies in intact small organisms and in samples from larger nervous systems. We then evaluate the evidence for an economical trade-off between biological cost and functional value in the organization of neuronal networks. Various results suggest that many aspects of neuronal network organization are indeed the outcome of competition between these two fundamental selection pressures.

Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface

Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity.

Nonrandom network connectivity comes in pairs

Overrepresentation of bidirectional connections in local cortical networks has been repeatedly reported and is a focus of the ongoing discussion of nonrandom connectivity. Here we show in a brief mathematical analysis that in a network in which connection probabilities are symmetric in pairs, P_ij = P_ji, the occurrences of bidirectional connections and nonrandom structures are inherently linked; an overabundance of reciprocally connected pairs emerges necessarily when some pairs of neurons are more likely to be connected than others. Our numerical results imply that such overrepresentation can also be sustained when connection probabilities are only approximately symmetric.

Understanding the specific connectivity of neural circuits is an important challenge of modern neuroscience. In this study we address an important feature of neural connectivity, the abundance of bidirectionally connected neuron pairs, which far exceeds what would be expected in a random network. Our theoretical analysis reveals a simple condition under which such an overrepresentation of bidirectionally connected pairs necessarily occurs: Any network in which both directions of connection are equally likely to exist in any given pair of neurons, but in which some pairs are more likely to be connected than others, must exhibit an abundance of reciprocal connections. This insight should guide the analysis and interpretation of future connectomics datasets.

Cell assemblies at multiple time scales with arbitrary lag constellations

Hebb's idea of a cell assembly as the fundamental unit of neural information processing has dominated neuroscience like no other theoretical concept within the past 60 years. A range of different physiological phenomena, from precisely synchronized spiking to broadly simultaneous rate increases, has been subsumed under this term. Yet progress in this area is hampered by the lack of statistical tools that would enable to extract assemblies with arbitrary constellations of time lags, and at multiple temporal scales, partly due to the severe computational burden. Here we present such a unifying methodological and conceptual framework which detects assembly structure at many different time scales, levels of precision, and with arbitrary internal organization. Applying this methodology to multiple single unit recordings from various cortical areas, we find that there is no universal cortical coding scheme, but that assembly structure and precision significantly depends on the brain area recorded and ongoing task demands.

DOI:http://dx.doi.org/10.7554/eLife.19428.001

Dendritic and Axonal Propagation Delays Determine Emergent Structures of Neuronal Networks with Plastic Synapses

Spike-timing-dependent plasticity (STDP) modifies synaptic strengths based on the relative timing of pre- and postsynaptic spikes. The temporal order of spikes turned out to be crucial. We here take into account how propagation delays, composed of dendritic and axonal delay times, may affect the temporal order of spikes. In a minimal setting, characterized by neglecting dendritic and axonal propagation delays, STDP eliminates bidirectional connections between two coupled neurons and turns them into unidirectional connections. In this paper, however, we show that depending on the dendritic and axonal propagation delays, the temporal order of spikes at the synapses can be different from those in the cell bodies and, consequently, qualitatively different connectivity patterns emerge. In particular, we show that for a system of two coupled oscillatory neurons, bidirectional synapses can be preserved and potentiated. Intriguingly, this finding also translates to large networks of type-II phase oscillators and, hence, crucially impacts on the overall hierarchical connectivity patterns of oscillatory neuronal networks.

Synaptic scaling rule preserves excitatory-inhibitory balance and salient neuronal network dynamics

The balance between excitation and inhibition (E-I balance) is maintained across brain regions though the network size, strength and number of synaptic connections, and connection architecture may vary substantially. We use a culture preparation to examine the homeostatic synaptic scaling rules that produce E-I balance and in vivo-like activity. We show that synaptic strength scales with the number of connections K as ∼ , close to the ideal theoretical value. Using optogenetic techniques, we delivered spatiotemporally patterned stimuli to neurons and confirmed key theoretical predictions: E-I balance is maintained, active decorrelation occurs and the spiking correlation increases with firing rate. Moreover, the trial-to-trial response variability decreased during stimulation, as observed in vivo. These results-obtained in generic cultures, predicted by theory and observed in the intact brain-suggest that the synaptic scaling rule and resultant dynamics are emergent properties of networks in general.

Morphological Characteristics of Electrophysiologically Characterized Layer Vb Pyramidal Cells in Rat Barrel Cortex

Layer Vb pyramidal cells are the major output neurons of the neocortex and transmit the outcome of cortical columnar signal processing to distant target areas. At the same time they contribute to local tactile information processing by emitting recurrent axonal collaterals into the columnar microcircuitry. It is, however, not known how exactly the two types of pyramidal cells, called slender-tufted and thick-tufted, contribute to the local circuitry. Here, we investigated in the rat barrel cortex the detailed quantitative morphology of biocytin-filled layer Vb pyramidal cells in vitro, which were characterized for their intrinsic electrophysiology with special emphasis on their action potential firing pattern. Since we stained the same slices for cytochrome oxidase, we could also perform layer- and column-related analyses. Our results suggest that in layer Vb the unambiguous action potential firing patterns "regular spiking (RS)" and "repetitive burst spiking (RB)" (previously called intrinsically burst spiking) correlate well with a distinct morphology. RS pyramidal cells are somatodendritically of the slender-tufted type and possess numerous local intralaminar and intracolumnar axonal collaterals, mostly reaching layer I. By contrast, their transcolumnar projections are less well developed. The RB pyramidal cells are somatodendritically of the thick-tufted type and show only relatively sparse local axonal collaterals, which are preferentially emitted as long horizontal or oblique infragranular collaterals. However, contrary to many previous slice studies, a substantial number of these neurons also showed axonal collaterals reaching layer I. Thus, electrophysiologically defined pyramidal cells of layer Vb show an input and output pattern which suggests RS cells to be more "locally segregating" signal processors whereas RB cells seem to act more on a "global integrative" scale.

Serotonergic Suppression of Mouse Prefrontal Circuits Implicated in Task Attention

Serotonin (5-HT) regulates attention by neurobiological mechanisms that are not well understood. Layer 6 (L6) pyramidal neurons of prefrontal cortex play an important role in attention and express 5-HT receptors, but the serotonergic modulation of this layer and its excitatory output is not known. Here, we performed whole-cell recordings and pharmacological manipulations in acute brain slices from wild-type and transgenic mice expressing either eGFP or eGFP-channelrhodopsin in prefrontal L6 pyramidal neurons. Excitatory circuits between L6 pyramidal neurons and L5 GABAergic interneurons, including a population of interneurons essential for task attention, were investigated using optogenetic techniques. Our experiments show that prefrontal L6 pyramidal neurons are subject to strong serotonergic inhibition and demonstrate direct 5-HT-sensitive connections between prefrontal L6 pyramidal neurons and two classes of L5 interneurons. This work helps to build a neurobiological framework to appreciate serotonergic disruption of task attention and yields insight into the disruptions of attention observed in psychiatric disorders with altered 5-HT receptors and signaling.

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

Integration of autopatching with automated pipette and cell detection in vitro

Patch clamp is the main technique for measuring electrical properties of individual cells. Since its discovery in 1976 by Neher and Sakmann, patch clamp has been instrumental in broadening our understanding of the fundamental properties of ion channels and synapses in neurons. The conventional patch-clamp method requires manual, precise positioning of a glass micropipette against the cell membrane of a visually identified target neuron. Subsequently, a tight "gigaseal" connection between the pipette and the cell membrane is established, and suction is applied to establish the whole cell patch configuration to perform electrophysiological recordings. This procedure is repeated manually for each individual cell, making it labor intensive and time consuming. In this article we describe the development of a new automatic patch-clamp system for brain slices, which integrates all steps of the patch-clamp process: image acquisition through a microscope, computer vision-based identification of a patch pipette and fluorescently labeled neurons, micromanipulator control, and automated patching. We validated our system in brain slices from wild-type and transgenic mice expressing channelrhodopsin 2 under the Thy1 promoter (line 18) or injected with a herpes simplex virus-expressing archaerhodopsin, ArchT. Our computer vision-based algorithm makes the fluorescent cell detection and targeting user independent. Compared with manual patching, our system is superior in both success rate and average trial duration. It provides more reliable trial-to-trial control of the patching process and improves reproducibility of experiments.

Jittering waves in rings of pulse oscillators

Rings of oscillators with delayed pulse coupling are studied analytically, numerically, and experimentally. The basic regimes observed in such rings are rotating waves with constant interspike intervals and phase lags between the neighbors. We show that these rotating waves may destabilize leading to the so-called jittering waves. For these regimes, the interspike intervals are no more equal but form a periodic sequence in time. Analytic criterion for the emergence of jittering waves is derived and confirmed by the numerical and experimental data. The obtained results contribute to the hypothesis that the multijitter instability is universal in systems with pulse coupling.

Opposing Effects of Neuronal Activity on Structural Plasticity

The connectivity of the brain is continuously adjusted to new environmental influences by several activity-dependent adaptive processes. The most investigated adaptive mechanism is activity-dependent functional or synaptic plasticity regulating the transmission efficacy of existing synapses. Another important but less prominently discussed adaptive process is structural plasticity, which changes the connectivity by the formation and deletion of synapses. In this review, we show, based on experimental evidence, that structural plasticity can be classified similar to synaptic plasticity into two categories: (i) Hebbian structural plasticity, which leads to an increase (decrease) of the number of synapses during phases of high (low) neuronal activity and (ii) homeostatic structural plasticity, which balances these changes by removing and adding synapses. Furthermore, based on experimental and theoretical insights, we argue that each type of structural plasticity fulfills a different function. While Hebbian structural changes enhance memory lifetime, storage capacity, and memory robustness, homeostatic structural plasticity self-organizes the connectivity of the neural network to assure stability. However, the link between functional synaptic and structural plasticity as well as the detailed interactions between Hebbian and homeostatic structural plasticity are more complex. This implies even richer dynamics requiring further experimental and theoretical investigations.

Distinct Spatiotemporal Activation Patterns of the Perirhinal-Entorhinal Network in Response to Cortical and Amygdala Input

The perirhinal (PER) and entorhinal cortex (EC) receive input from the agranular insular cortex (AiP) and the subcortical lateral amygdala (LA) and the main output area is the hippocampus. Information transfer through the PER/EC network however, is not always guaranteed. It is hypothesized that this network actively regulates the (sub)cortical activity transfer to the hippocampal network and that the inhibitory system is involved in this function. This study determined the recruitment by the AiP and LA afferents in PER/EC network with the use of voltage sensitive dye (VSD) imaging in horizontal mouse brain slices. Electrical stimulation (500 μA) of the AiP induced activity that gradually propagated predominantly in the rostro-caudal direction: from the PER to the lateral EC (LEC). In the presence of 1 μM of the competitive γ-aminobutyric acid (GABA_A) receptor antagonist bicuculline, AiP stimulation recruited the medial EC (MEC) as well. In contrast, LA stimulation (500 μA) only induced activity in the deep layers of the PER. In the presence of bicuculline, the initial population activity in the PER propagated further towards the superficial layers and the EC after a delay. The latency of evoked responses decreased with increasing stimulus intensities (50–500 μA) for both the AiP and LA stimuli. The stimulation threshold for evoking responses in the PER/EC network was higher for the LA than for the AiP. This study showed that the extent of the PER/EC network activation depends on release of inhibition. When GABA_A dependent inhibition is reduced, both the AiP and the LA activate spatially overlapping regions, although in a distinct spatiotemporal fashion. It is therefore hypothesized that the inhibitory network regulates excitatory activity from both cortical and subcortical areas that has to be transmitted through the PER/EC network.

Multitarget Multiscale Simulation for Pharmacological Treatment of Dystonia in Motor Cortex

A large number of physiomic pathologies can produce hyperexcitability in cortex. Depending on severity, cortical hyperexcitability may manifest clinically as a hyperkinetic movement disorder or as epilpesy. We focus here on dystonia, a movement disorder that produces involuntary muscle contractions and involves pathology in multiple brain areas including basal ganglia, thalamus, cerebellum, and sensory and motor cortices. Most research in dystonia has focused on basal ganglia, while much pharmacological treatment is provided directly at muscles to prevent contraction. Motor cortex is another potential target for therapy that exhibits pathological dynamics in dystonia, including heightened activity and altered beta oscillations. We developed a multiscale model of primary motor cortex, ranging from molecular, up to cellular, and network levels, containing 1715 compartmental model neurons with multiple ion channels and intracellular molecular dynamics. We wired the model based on electrophysiological data obtained from mouse motor cortex circuit mapping experiments. We used the model to reproduce patterns of heightened activity seen in dystonia by applying independent random variations in parameters to identify pathological parameter sets. These models demonstrated degeneracy, meaning that there were many ways of obtaining the pathological syndrome. There was no single parameter alteration which would consistently distinguish pathological from physiological dynamics. At higher dimensions in parameter space, we were able to use support vector machines to distinguish the two patterns in different regions of space and thereby trace multitarget routes from dystonic to physiological dynamics. These results suggest the use of in silico models for discovery of multitarget drug cocktails.

Compressive sensing reconstruction of feed-forward connectivity in pulse-coupled nonlinear networks

Utilizing the sparsity ubiquitous in real-world network connectivity, we develop a theoretical framework for efficiently reconstructing sparse feed-forward connections in a pulse-coupled nonlinear network through its output activities. Using only a small ensemble of random inputs, we solve this inverse problem through the compressive sensing theory based on a hidden linear structure intrinsic to the nonlinear network dynamics. The accuracy of the reconstruction is further verified by the fact that complex inputs can be well recovered using the reconstructed connectivity. We expect this Rapid Communication provides a new perspective for understanding the structure-function relationship as well as compressive sensing principle in nonlinear network dynamics.

Quantifying Repetitive Transmission at Chemical Synapses: A Generative-Model Approach

The dependence of the synaptic responses on the history of activation and their large variability are both distinctive features of repetitive transmission at chemical synapses. Quantitative investigations have mostly focused on trial-averaged responses to characterize dynamic aspects of the transmission—thus disregarding variability—or on the fluctuations of the responses in steady conditions to characterize variability—thus disregarding dynamics. We present a statistically principled framework to quantify the dynamics of the probability distribution of synaptic responses under arbitrary patterns of activation. This is achieved by constructing a generative model of repetitive transmission, which includes an explicit description of the sources of stochasticity present in the process. The underlying parameters are then selected via an expectation-maximization algorithm that is exact for a large class of models of synaptic transmission, so as to maximize the likelihood of the observed responses. The method exploits the information contained in the correlation between responses to produce highly accurate estimates of both quantal and dynamic parameters from the same recordings. The method also provides important conceptual and technical advances over existing state-of-the-art techniques. In particular, the repetition of the same stimulation in identical conditions becomes unnecessary. This paves the way to the design of optimal protocols to estimate synaptic parameters, to the quantitative comparison of synaptic models over benchmark datasets, and, most importantly, to the study of repetitive transmission under physiologically relevant patterns of synaptic activation.

Array tomography of physiologically-characterized CNS synapses

BACKGROUND

The ability to correlate plastic changes in synaptic physiology with changes in synaptic anatomy has been very limited in the central nervous system because of shortcomings in existing methods for recording the activity of specific CNS synapses and then identifying and studying the same individual synapses on an anatomical level.

NEW METHOD

We introduce here a novel approach that combines two existing methods: paired neuron electrophysiological recording and array tomography, allowing for the detailed molecular and anatomical study of synapses with known physiological properties.

RESULTS

The complete mapping of a neuronal pair allows determining the exact number of synapses in the pair and their location. We have found that the majority of close appositions between the presynaptic axon and the postsynaptic dendrite in the pair contain synaptic specializations. The average release probability of the synapses between the two neurons in the pair is low, below 0.2, consistent with previous studies of these connections. Other questions, such as receptor distribution within synapses, can be addressed more efficiently by identifying only a subset of synapses using targeted partial reconstructions. In addition, time sensitive events can be captured with fast chemical fixation.

COMPARISON WITH EXISTING METHODS

Compared to existing methods, the present approach is the only one that can provide detailed molecular and anatomical information of electrophysiologically-characterized individual synapses.

CONCLUSIONS

This method will allow for addressing specific questions about the properties of identified CNS synapses, even when they are buried within a cloud of millions of other brain circuit elements.

The Diversity of Cortical Inhibitory Synapses

The most typical and well known inhibitory action in the cortical microcircuit is a strong inhibition on the target neuron by axo-somatic synapses. However, it has become clear that synaptic inhibition in the cortex is much more diverse and complicated. Firstly, at least ten or more inhibitory non-pyramidal cell subtypes engage in diverse inhibitory functions to produce the elaborate activity characteristic of the different cortical states. Each distinct non-pyramidal cell subtype has its own independent inhibitory function. Secondly, the inhibitory synapses innervate different neuronal domains, such as axons, spines, dendrites and soma, and their inhibitory postsynaptic potential (IPSP) size is not uniform. Thus, cortical inhibition is highly complex, with a wide variety of anatomical and physiological modes. Moreover, the functional significance of the various inhibitory synapse innervation styles and their unique structural dynamic behaviors differ from those of excitatory synapses. In this review, we summarize our current understanding of the inhibitory mechanisms of the cortical microcircuit.

Is cortical connectivity optimized for storing information?

Cortical networks are thought to be shaped by experience-dependent synaptic plasticity. Theoretical studies have shown that synaptic plasticity allows a network to store a memory of patterns of activity such that they become attractors of the dynamics of the network. Here we study the properties of the excitatory synaptic connectivity in a network that maximizes the number of stored patterns of activity in a robust fashion. We show that the resulting synaptic connectivity matrix has the following properties: it is sparse, with a large fraction of zero synaptic weights ('potential' synapses); bidirectionally coupled pairs of neurons are over-represented in comparison to a random network; and bidirectionally connected pairs have stronger synapses on average than unidirectionally connected pairs. All these features reproduce quantitatively available data on connectivity in cortex. This suggests synaptic connectivity in cortex is optimized to store a large number of attractor states in a robust fashion.

Aberrant excitatory rewiring of layer V pyramidal neurons early after neocortical trauma

Lesioned neuronal circuits form new functional connections after a traumatic brain injury (TBI). In humans and animal models, aberrant excitatory connections that form after TBI may contribute to the pathogenesis of post-traumatic epilepsy. Partial neocortical isolation ("undercut" or "UC") leads to altered neuronal circuitry and network hyperexcitability recorded in vivo and in brain slices from chronically lesioned neocortex. Recent data suggest a critical period for maladaptive excitatory circuit formation within the first 3days post UC injury (Graber and Prince 1999, 2004; Li et al. 2011, 2012b). The present study focuses on alterations in excitatory connectivity within this critical period. Immunoreactivity (IR) for growth-associated protein (GAP)-43 was increased in the UC cortex 3days after injury. Some GAP-43-expressing excitatory terminals targeted the somata of layer V pyramidal (Pyr) neurons, a domain usually innervated predominantly by inhibitory terminals. Immunocytochemical analysis of pre- and postsynaptic markers showed that putative excitatory synapses were present on somata of these neurons in UC neocortex. Excitatory postsynaptic currents from UC layer V Pyr cells displayed properties consistent with perisomatic inputs and also reflected an increase in the number of synaptic contacts. Laser scanning photostimulation (LSPS) experiments demonstrated reorganized excitatory connectivity after injury within the UC. Concurrent with these changes, spontaneous epileptiform bursts developed in UC slices. Results suggest that aberrant reorganization of excitatory connectivity contributes to early neocortical hyperexcitability in this model. The findings are relevant for understanding the pathophysiology of neocortical post-traumatic epileptogenesis and are important in terms of the timing of potential prophylactic treatments.

Calcium regulation of HCN channels supports persistent activity in a multiscale model of neocortex

Neuronal persistent activity has been primarily assessed in terms of electrical mechanisms, without attention to the complex array of molecular events that also control cell excitability. We developed a multiscale neocortical model proceeding from the molecular to the network level to assess the contributions of calcium (Ca(2+)) regulation of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels in providing additional and complementary support of continuing activation in the network. The network contained 776 compartmental neurons arranged in the cortical layers, connected using synapses containing AMPA/NMDA/GABAA/GABAB receptors. Metabotropic glutamate receptors (mGluR) produced inositol triphosphate (IP3) which caused the release of Ca(2+) from endoplasmic reticulum (ER) stores, with reuptake by sarco/ER Ca(2+)-ATP-ase pumps (SERCA), and influence on HCN channels. Stimulus-induced depolarization led to Ca(2+) influx via NMDA and voltage-gated Ca(2+) channels (VGCCs). After a delay, mGluR activation led to ER Ca(2+) release via IP3 receptors. These factors increased HCN channel conductance and produced firing lasting for ∼1min. The model displayed inter-scale synergies among synaptic weights, excitation/inhibition balance, firing rates, membrane depolarization, Ca(2+) levels, regulation of HCN channels, and induction of persistent activity. The interaction between inhibition and Ca(2+) at the HCN channel nexus determined a limited range of inhibition strengths for which intracellular Ca(2+) could prepare population-specific persistent activity. Interactions between metabotropic and ionotropic inputs to the neuron demonstrated how multiple pathways could contribute in a complementary manner to persistent activity. Such redundancy and complementarity via multiple pathways is a critical feature of biological systems. Mediation of activation at different time scales, and through different pathways, would be expected to protect against disruption, in this case providing stability for persistent activity.

In vivo models of cortical acquired epilepsy

The neocortex is the site of origin of several forms of acquired epilepsy. Here we provide a brief review of experimental models that were recently developed to study neocortical epileptogenesis as well as some major results obtained with these methods. Most of neocortical seizures appear to be nocturnal and it is known that neuronal activities reveal high levels of synchrony during slow-wave sleep. Therefore, we start the review with a description of mechanisms of neuronal synchronization and major forms of synchronized normal and pathological activities. Then, we describe three experimental models of seizures and epileptogenesis: ketamine-xylazine anesthesia as feline seizure triggered factor, cortical undercut as cortical penetrating wound model and neocortical kindling. Besides specific technical details describing these models we also provide major features of pathological brain activities recorded during epileptogenesis and seizures. The most common feature of all models of neocortical epileptogenesis is the increased duration of network silent states that up-regulates neuronal excitability and eventually leads to epilepsy.

Optical detection of neuron connectivity by random access two-photon microscopy

BACKGROUND

Knowledge about the distribution, strength, and direction of synaptic connections within neuronal networks are crucial for understanding brain function. Electrophysiology using multiple electrodes provides a very high temporal resolution, but does not yield sufficient spatial information for resolving neuronal connection topology. Optical recording techniques using single-cell resolution have provided promise for providing spatial information. Although calcium imaging from hundreds of neurons has provided a novel view of the neural connections within the network, the kinetics of calcium responses are not fast enough to resolve each action potential event with high fidelity. Therefore, it is not possible to detect the direction of neuronal connections.

NEW METHOD

We took advantage of the fast kinetics and large dynamic range of the DiO/DPA combination of voltage sensitive dye and the fast scan speed of a custom-made random-access two-photon microscope to resolve each action potential event from multiple neurons in culture.

RESULTS

Long-duration recording up to 100min from cultured hippocampal neurons yielded sufficient numbers of spike events for analyzing synaptic connections. Cross-correlation analysis of neuron pairs clearly distinguished synaptically connected neuron pairs with the connection direction.

COMPARISON WITH EXISTING METHOD

The long duration recording of action potentials with voltage-sensitive dye utilized in the present study is much longer than in previous studies. Simultaneous optical voltage and calcium measurements revealed that voltage-sensitive dye is able to detect firing events more reliably than calcium indicators.

CONCLUSIONS

This novel method reveals a new view of the functional structure of neuronal networks.

Paired Burst Stimulation Causes GABAA Receptor-Dependent Spike Firing Facilitation in CA1 of Rat Hippocampal Slices

The theta oscillation (4–8 Hz) is a pivotal form of oscillatory activity in the hippocampus that is intermittently concurrent with gamma (25–100 Hz) burst events. In in vitro preparation, a stimulation protocol that mimics the theta oscillation, theta burst stimulation (TBS), is used to induce long-term potentiation. Thus, TBS is thought to have a distinct role in the neural network of the hippocampal slice preparation. However, the specific mechanisms that make TBS induce such neural circuit modifications are still unknown. Using electrophysiology and voltage-sensitive dye imaging (VSDI), we have found that TBS induces augmentation of spike firing. The augmentation was apparent in the first couple of brief burst stimulation (100 Hz four pulses) on a TBS-train in a presence of NMDA receptor blocker (APV 50 μM). In this study, we focused on the characterizes of the NMDA independent augmentation caused by a pair of the brief burst stimulation (the first pair of the TBS; paired burst stimulation-PBS). We found that PBS enhanced membrane potential responses on VSDI signal and intracellular recordings while it was absent in the current recording under whole-cell clamp condition. The enhancement of the response accompanied the augmentation of excitatory postsynaptic potential (EPSP) to spike firing (E-S) coupling. The paired burst facilitation (PBF) reached a plateau when the number of the first burst stimulation (priming burst) exceeds three. The interval between the bursts of 150 ms resulted in the maximum PBF. Gabazine (a GABA_A receptor antagonist) abolished PBF. The threshold for spike generation of the postsynaptic cells measured with a current injection to cells was not lowered by the priming burst of PBS. These results indicate that PBS activates the GABAergic system to cause short-term E-S augmentation without raising postsynaptic excitability. We propose that a GABAergic system of area CA1 of the hippocampus produce the short-term E-S plasticity that could cause exaggerated spike-firing upon a theta-gamma activity distinctively, thus making the neural circuit of the CA1 act as a specific amplifier of the oscillation signal.

PyramidalExplorer: A New Interactive Tool to Explore Morpho-Functional Relations of Human Pyramidal Neurons

This work presents PyramidalExplorer, a new tool to interactively explore and reveal the detailed organization of the microanatomy of pyramidal neurons with functionally related models. It consists of a set of functionalities that allow possible regional differences in the pyramidal cell architecture to be interactively discovered by combining quantitative morphological information about the structure of the cell with implemented functional models. The key contribution of this tool is the morpho-functional oriented design that allows the user to navigate within the 3D dataset, filter and perform Content-Based Retrieval operations. As a case study, we present a human pyramidal neuron with over 9000 dendritic spines in its apical and basal dendritic trees. Using PyramidalExplorer, we were able to find unexpected differential morphological attributes of dendritic spines in particular compartments of the neuron, revealing new aspects of the morpho-functional organization of the pyramidal neuron.

Coherent and intermittent ensemble oscillations emerge from networks of irregular spiking neurons

Local field potential (LFP) recordings from spatially distant cortical circuits reveal episodes of coherent gamma oscillations that are intermittent, and of variable peak frequency and duration. Concurrently, single neuron spiking remains largely irregular and of low rate. The underlying potential mechanisms of this emergent network activity have long been debated. Here we reproduce such intermittent ensemble oscillations in a model network, consisting of excitatory and inhibitory model neurons with the characteristics of regular-spiking (RS) pyramidal neurons, and fast-spiking (FS) and low-threshold spiking (LTS) interneurons. We find that fluctuations in the external inputs trigger reciprocally connected and irregularly spiking RS and FS neurons in episodes of ensemble oscillations, which are terminated by the recruitment of the LTS population with concurrent accumulation of inhibitory conductance in both RS and FS neurons. The model qualitatively reproduces experimentally observed phase drift, oscillation episode duration distributions, variation in the peak frequency, and the concurrent irregular single-neuron spiking at low rate. Furthermore, consistent with previous experimental studies using optogenetic manipulation, periodic activation of FS, but not RS, model neurons causes enhancement of gamma oscillations. In addition, increasing the coupling between two model networks from low to high reveals a transition from independent intermittent oscillations to coherent intermittent oscillations. In conclusion, the model network suggests biologically plausible mechanisms for the generation of episodes of coherent intermittent ensemble oscillations with irregular spiking neurons in cortical circuits.

Principles of connectivity among morphologically defined cell types in adult neocortex

Since the work of Ramón y Cajal in the late 19th and early 20th centuries, neuroscientists have speculated that a complete understanding of neuronal cell types and their connections is key to explaining complex brain functions. However, a complete census of the constituent cell types and their wiring diagram in mature neocortex remains elusive. By combining octuple whole-cell recordings with an optimized avidin-biotin-peroxidase staining technique, we carried out a morphological and electrophysiological census of neuronal types in layers 1, 2/3, and 5 of mature neocortex and mapped the connectivity between more than 11,000 pairs of identified neurons. We categorized 15 types of interneurons, and each exhibited a characteristic pattern of connectivity with other interneuron types and pyramidal cells. The essential connectivity structure of the neocortical microcircuit could be captured by only a few connectivity motifs.

Emerging Roles of Filopodia and Dendritic Spines in Motoneuron Plasticity during Development and Disease

Motoneurons develop extensive dendritic trees for receiving excitatory and inhibitory synaptic inputs to perform a variety of complex motor tasks. At birth, the somatodendritic domains of mouse hypoglossal and lumbar motoneurons have dense filopodia and spines. Consistent with Vaughn's synaptotropic hypothesis, we propose a developmental unified-hybrid model implicating filopodia in motoneuron spinogenesis/synaptogenesis and dendritic growth and branching critical for circuit formation and synaptic plasticity at embryonic/prenatal/neonatal period. Filopodia density decreases and spine density initially increases until postnatal day 15 (P15) and then decreases by P30. Spine distribution shifts towards the distal dendrites, and spines become shorter (stubby), coinciding with decreases in frequency and increases in amplitude of excitatory postsynaptic currents with maturation. In transgenic mice, either overexpressing the mutated human Cu/Zn-superoxide dismutase (hSOD1^G93A) gene or deficient in GABAergic/glycinergic synaptic transmission (gephyrin, GAD-67, or VGAT gene knockout), hypoglossal motoneurons develop excitatory glutamatergic synaptic hyperactivity. Functional synaptic hyperactivity is associated with increased dendritic growth, branching, and increased spine and filopodia density, involving actin-based cytoskeletal and structural remodelling. Energy-dependent ionic pumps that maintain intracellular sodium/calcium homeostasis are chronically challenged by activity and selectively overwhelmed by hyperactivity which eventually causes sustained membrane depolarization leading to excitotoxicity, activating microglia to phagocytose degenerating neurons under neuropathological conditions.

Formation and Maintenance of Robust Long-Term Information Storage in the Presence of Synaptic Turnover

A long-standing problem is how memories can be stored for very long times despite the volatility of the underlying neural substrate, most notably the high turnover of dendritic spines and synapses. To address this problem, here we are using a generic and simple probabilistic model for the creation and removal of synapses. We show that information can be stored for several months when utilizing the intrinsic dynamics of multi-synapse connections. In such systems, single synapses can still show high turnover, which enables fast learning of new information, but this will not perturb prior stored information (slow forgetting), which is represented by the compound state of the connections. The model matches the time course of recent experimental spine data during learning and memory in mice supporting the assumption of multi-synapse connections as the basis for long-term storage.

It is widely believed that information is stored in the connectivity, i.e. the synapses of neural networks. Yet, the morphological correlates of excitatory synapses, the dendritic spines, have been found to undergo a remarkable turnover on daily basis. This poses the question, how information can be retained on such a variable substrate. In this study, using connections with multiple synapses, we show that connections which follow the experimentally measured bimodal distribution in the number of synapses can store information orders of magnitude longer than the lifetime of a single synapse. This is a consequence of the underlying bistable collective dynamic of multiple synapses: Single synapses can appear and disappear without disturbing the memory as a whole. Furthermore, increasing or decreasing neural activity changes the distribution of the number of synapses of multi-synaptic connections such that only one of the peaks remains. This leads to a desirable property: information about these altered activities can be stored much faster than it is forgotten. Remarkably, the resulting model dynamics match recent experimental data investigating the long-term effect of learning on the dynamics of dendritic spines.

Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.

In Vivo Monosynaptic Excitatory Transmission between Layer 2 Cortical Pyramidal Neurons

Little is known about the properties of monosynaptic connections between identified neurons in vivo. We made multiple (two to four) two-photon targeted whole-cell recordings from neighboring layer 2 mouse somatosensory barrel cortex pyramidal neurons in vivo to investigate excitatory monosynaptic transmission in the hyperpolarized downstate. We report that pyramidal neurons form a sparsely connected (6.7% connectivity) network with an overrepresentation of bidirectional connections. The majority of unitary excitatory postsynaptic potentials were small in amplitude (<0.5 mV), with a small minority >1 mV. The coefficient of variation (CV = 0.74) could largely be explained by the presence of synaptic failures (22%). Both the CV and failure rates were reduced with increasing amplitude. The mean paired-pulse ratio was 1.15 and positively correlated with the CV. Our approach will help bridge the gap between connectivity and function and allow investigations into the impact of brain state on monosynaptic transmission and integration.

Synapse-type-specific plasticity in local circuits

Neuroscientists spent decades debating whether synaptic plasticity was presynaptically or postsynaptically expressed. It was eventually concluded that plasticity depends on many factors, including cell type. More recently, it has become increasingly clear that plasticity is regulated at an even finer grained level; it is specific to the synapse type, a concept we denote synapse-type-specific plasticity (STSP). Here, we review recent developments in the field of STSP, discussing both long-term and short-term variants and with particular emphasis on neocortical function. As there are dozens of neocortical cell types, there is a multiplicity of forms of STSP, the vast majority of which have never been explored. We argue that to understand the brain and synaptic diseases, we have to grapple with STSP.

Nanoconnectomic upper bound on the variability of synaptic plasticity

Information in a computer is quantified by the number of bits that can be stored and recovered. An important question about the brain is how much information can be stored at a synapse through synaptic plasticity, which depends on the history of probabilistic synaptic activity. The strong correlation between size and efficacy of a synapse allowed us to estimate the variability of synaptic plasticity. In an EM reconstruction of hippocampal neuropil we found single axons making two or more synaptic contacts onto the same dendrites, having shared histories of presynaptic and postsynaptic activity. The spine heads and neck diameters, but not neck lengths, of these pairs were nearly identical in size. We found that there is a minimum of 26 distinguishable synaptic strengths, corresponding to storing 4.7 bits of information at each synapse. Because of stochastic variability of synaptic activation the observed precision requires averaging activity over several minutes.

DOI:http://dx.doi.org/10.7554/eLife.10778.001

What is the memory capacity of a human brain? The storage capacity in a computer memory is measured in bits, each of which can have a value of 0 or 1. In the brain, information is stored in the form of synaptic strength, a measure of how strongly activity in one neuron influences another neuron to which it is connected. The number of different strengths can be measured in bits. The total storage capacity of the brain therefore depends on both the number of synapses and the number of distinguishable synaptic strengths.

Structurally, neurons consist of a cell body that influences other neurons through a cable-like axon. The cell body bears numerous short branches called dendrites, which are covered in tiny protrusions, or “spines”. Most excitatory synapses are formed between the axon of one neuron and a dendritic spine on another. When two neurons on either side of a synapse are active simultaneously, that synapse becomes stronger, a form of memory. The dendritic spine also becomes larger to accommodate the extra molecular machinery needed to support a stronger synapse.

Some axons form two or more synapses with the same dendrite, but on different dendritic spines. These synapses should be the same strength because they will have experienced the same history of neural activity. Bartol et al. used a technique called serial section electron microscopy to create a 3D reconstruction of part of the brain that allowed the sizes of the dendritic spines these synapses form on to be compared. This revealed that the synaptic areas and volumes of the spine heads were nearly identical. This remarkable similarity can be used to estimate the number of bits of information that a single synapse can store, since the size of dendritic spines and their synapses can be used as proxies for synaptic strength.

Measurements in a small cube of brain tissue revealed 26 different dendritic spine sizes, each associated with a distinct synaptic strength. This number translates into a storage capacity of roughly 4.7 bits of information per synapse. This estimate is markedly higher than previous suggestions. It implies that the total memory capacity of the brain – with its many trillions of synapses – may have been underestimated by an order of magnitude. Additional measurements in the same and other brain regions are needed to confirm this possibility.

DOI:http://dx.doi.org/10.7554/eLife.10778.002

Dendritic integration: 60 years of progress

Understanding how individual neurons integrate the thousands of synaptic inputs they receive is critical to understanding how the brain works. Modeling studies in silico and experimental work in vitro, dating back more than half a century, have revealed that neurons can perform a variety of different passive and active forms of synaptic integration on their inputs. But how are synaptic inputs integrated in the intact brain? With the development of new techniques, this question has recently received substantial attention, with new findings suggesting that many of the forms of synaptic integration observed in vitro also occur in vivo, including in awake animals. Here we review six decades of progress, which collectively highlights the complex ways that single neurons integrate their inputs, emphasizing the critical role of dendrites in information processing in the brain.

Molecularly Defined Circuitry Reveals Input-Output Segregation in Deep Layers of the Medial Entorhinal Cortex

Deep layers of the medial entorhinal cortex are considered to relay signals from the hippocampus to other brain structures, but pathways for routing of signals to and from the deep layers are not well established. Delineating these pathways is important for a circuit level understanding of spatial cognition and memory. We find that neurons in layers 5a and 5b have distinct molecular identities, defined by the transcription factors Etv1 and Ctip2, and divergent targets, with extensive intratelencephalic projections originating in layer 5a, but not 5b. This segregation of outputs is mirrored by the organization of glutamatergic input from stellate cells in layer 2 and from the hippocampus, with both preferentially targeting layer 5b over 5a. Our results suggest a molecular and anatomical organization of input-output computations in deep layers of the MEC, reveal precise translaminar microcircuitry, and identify molecularly defined pathways for spatial signals to influence computation in deep layers.

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The transcription factors Etv1 and Ctip2 distinguish entorhinal layers 5a and 5b

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Layer 5a has extensive intratelencephalic projections, but layer 5b does not

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Terminals of layer 2 stellate, but not pyramidal cells, are enriched in deep layers

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Hippocampal and stellate cell inputs preferentially target layer 5b neurons

Developmental Profile, Morphology, and Synaptic Connectivity of Cajal-Retzius Cells in the Postnatal Mouse Hippocampus

Cajal–Retzius (CR) cells are early generated neurons, involved in the assembly of developing neocortical and hippocampal circuits. However, their roles in networks of the postnatal brain remain poorly understood. In order to get insights into these latter functions, we have studied their morphological and synaptic properties in the postnatal hippocampus of the CXCR4-EGFP mouse, where CR cells are easily identifiable. Our data indicate that CR cells are nonuniformly distributed along different subfields of the hippocampal formation, and that their postnatal decline is regulated in a region-specific manner. In fact, CR cells persist in distinct areas of fully mature animals. Subclasses of CR cells project and target either local (molecular layers) or distant regions [subicular complex and entorhinal cortex (EC)] of the hippocampal formation, but have similar firing patterns. Lastly, CR cells are biased toward targeting dendritic shafts compared with spines, and produce large-amplitude glutamatergic unitary postsynaptic potentials on γ-aminobutyric acid (GABA) containing interneurons. Taken together, our results suggest that CR cells are involved in a novel excitatory loop of the postnatal hippocampal formation, which potentially contributes to shaping the flow of information between the hippocampus, parahippocampal regions and entorhinal cortex, and to the low seizure threshold of these brain areas.

Direct projection from the lateral habenula to the trigeminal mesencephalic nucleus in rats

Trigeminal mesencephalic nucleus (Vmes) neurons are primary afferents conveying deep sensation from the masticatory muscle spindles or the periodontal mechanoreceptors, and are crucial for controlling jaw movements. Their cell bodies exist in the brain and receive descending commands from a variety of cortical and subcortical structures involved in limbic (emotional) systems. However, it remains unclear how the lateral habenula (LHb), a center of negative emotions (e.g., pain, stress and anxiety), can influence the control of jaw movements. To address this issue, we examined whether and how the LHb directly projects to the Vmes by means of neuronal tract tracing techniques in rats. After injections of a retrograde tracer Fluorogold in the rostral and caudal Vmes, a number of neurons were labeled in the lateral division of LHb (LHbl) bilaterally, whereas a few neurons were labeled in the medial division of LHb (LHbm) bilaterally. After injections of an anterograde tracer, biotinylated dextranamine (BDA) in the LHbl, a small number of labeled axons were distributed bilaterally in the rostral and caudal levels of Vmes, where some labeled axonal boutons contacted the cell body of rostral and caudal levels of Vmes neurons bilaterally. After the BDA injection into the LHbm, however, no axons were labeled bilaterally in the rostral and caudal levels of Vmes. Therefore, the present study for the first time demonstrated the direct projection from the LHbl to the Vmes and the detailed projection patterns, suggesting that jaw movements are modulated by negative emotions that are signaled by LHbl neurons.