Multiquantal release underlies the distribution of synaptic efficacies in the neocortex

Synaptic boutons are smaller in chandelier cell cartridges in autism

Chandelier (Ch) cells are cortical interneurons with axon terminal structures known as cartridges that synapse on the axon initial segment of excitatory pyramidal neurons. Previous studies indicate that the number of Ch cells is decreased in autism, and that GABA receptors are decreased in the Ch cell synaptic target in the prefrontal cortex. To further identify Ch cell alterations, we examined whether the length of cartridges, and the number, density, and size of Ch cell synaptic boutons, differed in the prefrontal cortex of cases with autism versus control cases. We collected samples of postmortem human prefrontal cortex (Brodmann Area (BA) 9, 46, and 47) from 20 cases with autism and 20 age- and sex-matched control cases. Ch cells were labeled using an antibody against parvalbumin, a marker that labeles soma, cartridges, and synaptic boutons. We found no significant difference in the average length of cartridges, or in the total number or density of boutons in control subjects vs. subjects with autism. However, we found a significant decrease in the size of Ch cell boutons in those with autism. The reduced size of Ch cell boutons may result in reduced inhibitory signal transmission and impact the balance of excitation to inhibition in the prefrontal cortex in autism.

The network configuration in Parkinsonian state compensates network activity change caused by loss of dopamine

Parkinson's disease is a movement disorder caused by dopamine depletion in the basal ganglia. Neural activity of the subthalamic nucleus (STN) and globus pallidus externus (GPe) in the basal ganglia are closely related to motor symptoms of Parkinson's disease. However, the pathogenesis of the disease and the transition from the normal state to the pathological state have yet to be elucidated. The functional organization of the GPe is gaining attention due to the recent finding that it consists of two distinct cell populations, namely prototypic GPe neurons and arkypallidal neurons. Identifying connectivity structures between these cell populations, as well as STN neurons, in relation to the dependence of the network activity on the dopaminergic effects is vital. In the present study, using a computational model of the STN-GPe network, we explored biologically plausible connectivity structures between these cell populations. We evaluated the experimentally reported neural activities of these cell types to elucidate the effects of dopaminergic modulation and changes caused by chronic dopamine depletion, such as strengthened connections in the neural activity of the STN-GPe network. Our results indicate that the arkypallidal neurons receive cortical inputs separately from the source for prototypic and STN neurons, suggesting that arkypallidal neurons might be responsible for an additional pathway with the cortex. Furthermore, changes caused by chronic dopamine depletion compensate for the loss of dopaminergic modulation. Changes caused by dopamine depletion itself likely induce the pathological activity observed in patients with Parkinson's disease. However, such changes counteract those of firing rates caused by loss of dopaminergic modulation. In addition, we observed that the STN-GPe tends to exhibit activity with pathological characteristics as side effects.

Synaptic connectivity to L2/3 of primary visual cortex measured by two-photon optogenetic stimulation

Understanding cortical microcircuits requires thorough measurement of physiological properties of synaptic connections formed within and between diverse subclasses of neurons. Towards this goal, we combined spatially precise optogenetic stimulation with multicellular recording to deeply characterize intralaminar and translaminar monosynaptic connections to supragranular (L2/3) neurons in the mouse visual cortex. The reliability and specificity of multiphoton optogenetic stimulation were measured across multiple Cre lines, and measurements of connectivity were verified by comparison to paired recordings and targeted patching of optically identified presynaptic cells. With a focus on translaminar pathways, excitatory and inhibitory synaptic connections from genetically defined presynaptic populations were characterized by their relative abundance, spatial profiles, strength, and short-term dynamics. Consistent with the canonical cortical microcircuit, layer 4 excitatory neurons and interneurons within L2/3 represented the most common sources of input to L2/3 pyramidal cells. More surprisingly, we also observed strong excitatory connections from layer 5 intratelencephalic neurons and potent translaminar inhibition from multiple interneuron subclasses. The hybrid approach revealed convergence to and divergence from excitatory and inhibitory neurons within and across cortical layers. Divergent excitatory connections often spanned hundreds of microns of horizontal space. In contrast, divergent inhibitory connections were more frequently measured from postsynaptic targets near each other.

Structure and function of a neocortical synapse

In 1986, electron microscopy was used to reconstruct by hand the entire nervous system of a roundworm, the nematode Caenorhabditis elegans¹. Since this landmark study, high-throughput electron-microscopic techniques have enabled reconstructions of much larger mammalian brain circuits at synaptic resolution^2,3. Nevertheless, it remains unknown how the structure of a synapse relates to its physiological transmission strength-a key limitation for inferring brain function from neuronal wiring diagrams. Here we combine slice electrophysiology of synaptically connected pyramidal neurons in the mouse somatosensory cortex with correlated light microscopy and high-resolution electron microscopy of all putative synaptic contacts between the recorded neurons. We find a linear relationship between synapse size and strength, providing the missing link in assigning physiological weights to synapses reconstructed from electron microscopy. Quantal analysis also reveals that synapses contain at least 2.7 neurotransmitter-release sites on average. This challenges existing release models and provides further evidence that neocortical synapses operate with multivesicular release^4-6, suggesting that they are more complex computational devices than thought, and therefore expanding the computational power of the canonical cortical microcircuitry.

Short-term depression and long-term plasticity together tune sensitive range of synaptic plasticity

Synaptic efficacy is subjected to activity-dependent changes on short- and long time scales. While short-term changes decay over minutes, long-term modifications last from hours up to a lifetime and are thought to constitute the basis of learning and memory. Both plasticity mechanisms have been studied extensively but how their interaction shapes synaptic dynamics is little known. To investigate how both short- and long-term plasticity together control the induction of synaptic depression and potentiation, we used numerical simulations and mathematical analysis of a calcium-based model, where pre- and postsynaptic activity induces calcium transients driving synaptic long-term plasticity. We found that the model implementing known synaptic short-term dynamics in the calcium transients can be successfully fitted to long-term plasticity data obtained in visual- and somatosensory cortex. Interestingly, the impact of spike-timing and firing rate changes on plasticity occurs in the prevalent firing rate range, which is different in both cortical areas considered here. Our findings suggest that short- and long-term plasticity are together tuned to adapt plasticity to area-specific activity statistics such as firing rates.

Synaptic long-term plasticity, the long-lasting change in efficacy of connections between neurons, is believed to underlie learning and memory. Synapses furthermore change their efficacy reversibly in an activity-dependent manner on the subsecond time scale, referred to as short-term plasticity. It is not known how both synaptic plasticity mechanisms—long- and short-term—interact during activity epochs. To address this question, we used a biologically-inspired plasticity model in which calcium drives changes in synaptic efficacy. We applied the model to plasticity data from visual- and somatosensory cortex and found that synaptic changes occur in very different firing rate ranges, which correspond to the prevalent firing rates in both structures. Our results suggest that short- and long-term plasticity act in a well concerted fashion.

Data-driven integration of hippocampal CA1 synaptic physiology in silico

The anatomy and physiology of monosynaptic connections in rodent hippocampal CA1 have been extensively studied in recent decades. Yet, the resulting knowledge remains disparate and difficult to reconcile. Here, we present a data‐driven approach to integrate the current state‐of‐the‐art knowledge on the synaptic anatomy and physiology of rodent hippocampal CA1, including axo‐dendritic innervation patterns, number of synapses per connection, quantal conductances, neurotransmitter release probability, and short‐term plasticity into a single coherent resource. First, we undertook an extensive literature review of paired recordings of hippocampal neurons and compiled experimental data on their synaptic anatomy and physiology. The data collected in this manner is sparse and inhomogeneous due to the diversity of experimental techniques used by different groups, which necessitates the need for an integrative framework to unify these data. To this end, we extended a previously developed workflow for the neocortex to constrain a unifying in silico reconstruction of the synaptic physiology of CA1 connections. Our work identifies gaps in the existing knowledge and provides a complementary resource toward a more complete quantification of synaptic anatomy and physiology in the rodent hippocampal CA1 region.

Dysregulated Prefrontal Cortex Inhibition in Prepubescent and Adolescent Fragile X Mouse Model

Changes in excitation and inhibition are associated with the pathobiology of neurodevelopmental disorders of intellectual disability and autism and are widely described in Fragile X syndrome (FXS). In the prefrontal cortex (PFC), essential for cognitive processing, excitatory connectivity and plasticity are found altered in the FXS mouse model, however, little is known about the state of inhibition. To that end, we investigated GABAergic signaling in the Fragile X Mental Retardation 1 (FMR1) knock out (Fmr1-KO) mouse medial PFC (mPFC). We report changes at the molecular, and functional levels of inhibition at three (prepubescence) and six (adolescence) postnatal weeks. Functional changes were most prominent during early postnatal development, resulting in stronger inhibition, through increased synaptic inhibitory drive and amplitude, and reduction of inhibitory short-term synaptic depression. Noise analysis of prepubescent post-synaptic currents demonstrated an increased number of receptors opening during peak current in Fmr1-KO inhibitory synapses. During adolescence amplitudes and plasticity changes normalized, however, the inhibitory drive was now reduced in Fmr1-KO, while synaptic kinetics were prolonged. Finally, adolescent GABA_A receptor subunit α2 and GABA_B receptor subtype B1 expression levels were different in Fmr1-KOs than WT littermate controls. Together these results extend the degree of synaptic GABAergic alterations in FXS, now to the mPFC of Fmr1-KO mice, a behaviourally relevant brain region in neurodevelopmental disorder pathology.

Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in the Neocortex

Previous studies based on the 'Quantal Model' for synaptic transmission suggest that neurotransmitter release is mediated by a single release site at individual synaptic contacts in the neocortex. However, recent studies seem to contradict this hypothesis and indicate that multi-vesicular release (MVR) could better explain the synaptic response variability observed in vitro. In this study we present a novel method to estimate the number of release sites per synapse, also known as the size of the readily releasable pool (N_RRP), from paired whole-cell recordings of connections between layer 5 thick tufted pyramidal cell (L5_TTPC) in the juvenile rat somatosensory cortex. Our approach extends the work of Loebel et al. (2009) by leveraging a recently published data-driven biophysical model of neocortical tissue. Using this approach, we estimated N_RRP to be between two to three for synaptic connections between L5_TTPCs. To constrain N_RRP values for other connections in the microcircuit, we developed and validated a generalization approach using published data on the coefficient of variation (CV) of the amplitudes of post-synaptic potentials (PSPs) from literature and comparing them against in silico experiments. Our study predicts that transmitter release at synaptic connections in the neocortex could be mediated by MVR and provides a data-driven approach to constrain the MVR model parameters in the microcircuit.

Cortical reliability amid noise and chaos

Typical responses of cortical neurons to identical sensory stimuli appear highly variable. It has thus been proposed that the cortex primarily uses a rate code. However, other studies have argued for spike-time coding under certain conditions. The potential role of spike-time coding is directly limited by the internally generated variability of cortical circuits, which remains largely unexplored. Here, we quantify this internally generated variability using a biophysical model of rat neocortical microcircuitry with biologically realistic noise sources. We find that stochastic neurotransmitter release is a critical component of internally generated variability, causing rapidly diverging, chaotic recurrent network dynamics. Surprisingly, the same nonlinear recurrent network dynamics can transiently overcome the chaos in response to weak feed-forward thalamocortical inputs, and support reliable spike times with millisecond precision. Our model shows that the noisy and chaotic network dynamics of recurrent cortical microcircuitry are compatible with stimulus-evoked, millisecond spike-time reliability, resolving a long-standing debate.

Model-Based Inference of Synaptic Transmission

Synaptic computation is believed to underlie many forms of animal behavior. A correct identification of synaptic transmission properties is thus crucial for a better understanding of how the brain processes information, stores memories and learns. Recently, a number of new statistical methods for inferring synaptic transmission parameters have been introduced. Here we review and contrast these developments, with a focus on methods aimed at inferring both synaptic release statistics and synaptic dynamics. Furthermore, based on recent proposals we discuss how such methods can be applied to data across different levels of investigation: from intracellular paired experiments to in vivo network-wide recordings. Overall, these developments open the window to reliably estimating synaptic parameters in behaving animals.

Transmission of temporally correlated spike trains through synapses with short-term depression

Short-term synaptic depression, caused by depletion of releasable neurotransmitter, modulates the strength of neuronal connections in a history-dependent manner. Quantifying the statistics of synaptic transmission requires stochastic models that link probabilistic neurotransmitter release with presynaptic spike-train statistics. Common approaches are to model the presynaptic spike train as either regular or a memory-less Poisson process: few analytical results are available that describe depressing synapses when the afferent spike train has more complex, temporally correlated statistics such as bursts. Here we present a series of analytical results—from vesicle release-site occupancy statistics, via neurotransmitter release, to the post-synaptic voltage mean and variance—for depressing synapses driven by correlated presynaptic spike trains. The class of presynaptic drive considered is that fully characterised by the inter-spike-interval distribution and encompasses a broad range of models used for neuronal circuit and network analyses, such as integrate-and-fire models with a complete post-spike reset and receiving sufficiently short-time correlated drive. We further demonstrate that the derived post-synaptic voltage mean and variance allow for a simple and accurate approximation of the firing rate of the post-synaptic neuron, using the exponential integrate-and-fire model as an example. These results extend the level of biological detail included in models of synaptic transmission and will allow for the incorporation of more complex and physiologically relevant firing patterns into future studies of neuronal networks.

Synapses between neurons transmit signals with strengths that vary with the history of their activity, over scales from milliseconds to decades. Short-term changes in synaptic strength modulate and sculpt ongoing neuronal activity, whereas long-term changes underpin memory formation. Here we focus on changes of strength over timescales of less than a second caused by transitory depletion of the neurotransmitters that carry signals across the synapse. Neurotransmitters are stored in small vesicles that release their contents, with a certain probability, when the presynaptic neuron is active. Once a vesicle has been used it is replenished after a variable delay. There is therefore a complex interaction between the pattern of incoming signals to the synapse and the probablistic release and restock of packaged neurotransmitter. Here we extend existing models to examine how correlated synaptic activity is transmitted through synapses and affects the voltage fluctuations and firing rate of the target neuron. Our results provide a framework that will allow for the inclusion of biophysically realistic synaptic behaviour in studies of neuronal circuits.

Functional consequences of pre- and postsynaptic expression of synaptic plasticity

Growing experimental evidence shows that both homeostatic and Hebbian synaptic plasticity can be expressed presynaptically as well as postsynaptically. In this review, we start by discussing this evidence and methods used to determine expression loci. Next, we discuss the functional consequences of this diversity in pre- and postsynaptic expression of both homeostatic and Hebbian synaptic plasticity. In particular, we explore the functional consequences of a biologically tuned model of pre- and postsynaptically expressed spike-timing-dependent plasticity complemented with postsynaptic homeostatic control. The pre- and postsynaptic expression in this model predicts (i) more reliable receptive fields and sensory perception, (ii) rapid recovery of forgotten information (memory savings), and (iii) reduced response latencies, compared with a model with postsynaptic expression only. Finally, we discuss open questions that will require a considerable research effort to better elucidate how the specific locus of expression of homeostatic and Hebbian plasticity alters synaptic and network computations.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'.

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.

Bayesian Inference of Synaptic Quantal Parameters from Correlated Vesicle Release

Synaptic transmission is both history-dependent and stochastic, resulting in varying responses to presentations of the same presynaptic stimulus. This complicates attempts to infer synaptic parameters and has led to the proposal of a number of different strategies for their quantification. Recently Bayesian approaches have been applied to make more efficient use of the data collected in paired intracellular recordings. Methods have been developed that either provide a complete model of the distribution of amplitudes for isolated responses or approximate the amplitude distributions of a train of post-synaptic potentials, with correct short-term synaptic dynamics but neglecting correlations. In both cases the methods provided significantly improved inference of model parameters as compared to existing mean-variance fitting approaches. However, for synapses with high release probability, low vesicle number or relatively low restock rate and for data in which only one or few repeats of the same pattern are available, correlations between serial events can allow for the extraction of significantly more information from experiment: a more complete Bayesian approach would take this into account also. This has not been possible previously because of the technical difficulty in calculating the likelihood of amplitudes seen in correlated post-synaptic potential trains; however, recent theoretical advances have now rendered the likelihood calculation tractable for a broad class of synaptic dynamics models. Here we present a compact mathematical form for the likelihood in terms of a matrix product and demonstrate how marginals of the posterior provide information on covariance of parameter distributions. The associated computer code for Bayesian parameter inference for a variety of models of synaptic dynamics is provided in the Supplementary Material allowing for quantal and dynamical parameters to be readily inferred from experimental data sets.

Sniff-Like Patterned Input Results in Long-Term Plasticity at the Rat Olfactory Bulb Mitral and Tufted Cell to Granule Cell Synapse

During odor sensing the activity of principal neurons of the mammalian olfactory bulb, the mitral and tufted cells (MTCs), occurs in repetitive bursts that are synchronized to respiration, reminiscent of hippocampal theta-gamma coupling. Axonless granule cells (GCs) mediate self- and lateral inhibitory interactions between the excitatory MTCs via reciprocal dendrodendritic synapses. We have explored long-term plasticity at this synapse by using a theta burst stimulation (TBS) protocol and variations thereof. GCs were excited via glomerular stimulation in acute brain slices. We find that TBS induces exclusively long-term depression in the majority of experiments, whereas single bursts ("single-sniff paradigm") can elicit both long-term potentiation and depression. Statistical analysis predicts that the mechanism underlying this bidirectional plasticity involves the proportional addition or removal of presynaptic release sites. Gamma stimulation with the same number of APs as in TBS was less efficient in inducing plasticity. Both TBS- and "single-sniff paradigm"-induced plasticity depend on NMDA receptor activation. Since the onset of plasticity is very rapid and requires little extra activity, we propose that these forms of plasticity might play a role already during an ongoing search for odor sources. Our results imply that components of both short-term and long-term olfactory memory may be encoded at this synapse.

A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity

The prefrontal cortex is centrally involved in a wide range of cognitive functions and their impairment in psychiatric disorders. Yet, the computational principles that govern the dynamics of prefrontal neural networks, and link their physiological, biochemical and anatomical properties to cognitive functions, are not well understood. Computational models can help to bridge the gap between these different levels of description, provided they are sufficiently constrained by experimental data and capable of predicting key properties of the intact cortex. Here, we present a detailed network model of the prefrontal cortex, based on a simple computationally efficient single neuron model (simpAdEx), with all parameters derived from in vitro electrophysiological and anatomical data. Without additional tuning, this model could be shown to quantitatively reproduce a wide range of measures from in vivo electrophysiological recordings, to a degree where simulated and experimentally observed activities were statistically indistinguishable. These measures include spike train statistics, membrane potential fluctuations, local field potentials, and the transmission of transient stimulus information across layers. We further demonstrate that model predictions are robust against moderate changes in key parameters, and that synaptic heterogeneity is a crucial ingredient to the quantitative reproduction of in vivo-like electrophysiological behavior. Thus, we have produced a physiologically highly valid, in a quantitative sense, yet computationally efficient PFC network model, which helped to identify key properties underlying spike time dynamics as observed in vivo, and can be harvested for in-depth investigation of the links between physiology and cognition.

Computational network models are an important tool for linking physiological and neuro-dynamical processes to cognition. However, harvesting network models for this purpose may less depend on how much biophysical detail is included, but more on how well the model can capture the functional network physiology. Here, we present the first network model of the prefrontal cortex which has not only its single neuron properties and anatomical layout tightly constrained by experimental data, but is also able to quantitatively reproduce a large range of spiking, field potential, and membrane voltage statistics obtained from in vivo data, without need of specific parameter tuning. It thus represents a novel computational tool for addressing questions about the neuro-dynamics of cognition in health and disease.

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.

Chandelier Cells in Functional and Dysfunctional Neural Circuits

Chandelier cells (ChCs; also called axo-axonic cells) are a specialized GABAergic interneuron subtype that selectively innervates pyramidal neurons at the axon initial segment (AIS), the site of action potential generation. ChC connectivity allows for powerful yet precise modulation of large populations of pyramidal cells, suggesting ChCs have a critical role in brain functions. Dysfunctions in ChC connectivity are associated with brain disorders such as epilepsy and schizophrenia; however, whether this is causative, contributory or compensatory is not known. A likely stumbling block toward mechanistic discoveries and uncovering potential therapeutic targets is the apparent lack of rudimentary understanding of ChCs. For example, whether cortical ChCs are inhibitory or excitatory remains unresolved, and thus whether altered ChC activity results in altered inhibition or excitation is not clear. Recent studies have shed some light onto this excitation-inhibition controversy. In addition, new findings have identified preferential cell-type connectivities established by cortical ChCs, greatly expanding our understanding of the role of ChCs in the cortical microcircuit. Here we aim to bring more attention to ChC connectivity to better understand its role in neural circuits, address whether ChCs are inhibitory or excitatory in light of recent findings and discuss ChC dysfunctions in brain disorders.

Modulation of Synaptic Plasticity by Glutamatergic Gliotransmission: A Modeling Study

Glutamatergic gliotransmission, that is, the release of glutamate from perisynaptic astrocyte processes in an activity-dependent manner, has emerged as a potentially crucial signaling pathway for regulation of synaptic plasticity, yet its modes of expression and function in vivo remain unclear. Here, we focus on two experimentally well-identified gliotransmitter pathways, (i) modulations of synaptic release and (ii) postsynaptic slow inward currents mediated by glutamate released from astrocytes, and investigate their possible functional relevance on synaptic plasticity in a biophysical model of an astrocyte-regulated synapse. Our model predicts that both pathways could profoundly affect both short- and long-term plasticity. In particular, activity-dependent glutamate release from astrocytes could dramatically change spike-timing-dependent plasticity, turning potentiation into depression (and vice versa) for the same induction protocol.

A Phenomenological Synapse Model for Asynchronous Neurotransmitter Release

Neurons communicate with each other via synapses. Action potentials cause release of neurotransmitters at the axon terminal. Typically, this neurotransmitter release is tightly time-locked to the arrival of an action potential and is thus called synchronous release. However, neurotransmitter release is stochastic and the rate of release of small quanta of neurotransmitters can be considerably elevated even long after the ceasing of spiking activity, leading to asynchronous release of neurotransmitters. Such asynchronous release varies for tissue and neuron types and has been shown recently to be pronounced in fast-spiking neurons. Notably, it was found that asynchronous release is enhanced in human epileptic tissue implicating a possibly important role in generating abnormal neural activity. Current neural network models for simulating and studying neural activity virtually only consider synchronous release and ignore asynchronous transmitter release. Here, we develop a phenomenological model for asynchronous neurotransmitter release, which, on one hand, captures the fundamental features of the asynchronous release process, and, on the other hand, is simple enough to be incorporated in large-size network simulations. Our proposed model is based on the well-known equations for short-term dynamical synaptic interactions and includes an additional stochastic term for modeling asynchronous release. We use experimental data obtained from inhibitory fast-spiking synapses of human epileptic tissue to fit the model parameters, and demonstrate that our model reproduces the characteristics of realistic asynchronous transmitter release.

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.

High bandwidth synaptic communication and frequency tracking in human neocortex

Neuronal firing, synaptic transmission, and its plasticity form the building blocks for processing and storage of information in the brain. It is unknown whether adult human synapses are more efficient in transferring information between neurons than rodent synapses. To test this, we recorded from connected pairs of pyramidal neurons in acute brain slices of adult human and mouse temporal cortex and probed the dynamical properties of use-dependent plasticity. We found that human synaptic connections were purely depressing and that they recovered three to four times more swiftly from depression than synapses in rodent neocortex. Thereby, during realistic spike trains, the temporal resolution of synaptic information exchange in human synapses substantially surpasses that in mice. Using information theory, we calculate that information transfer between human pyramidal neurons exceeds that of mouse pyramidal neurons by four to nine times, well into the beta and gamma frequency range. In addition, we found that human principal cells tracked fine temporal features, conveyed in received synaptic inputs, at a wider bandwidth than for rodents. Action potential firing probability was reliably phase-locked to input transients up to 1,000 cycles/s because of a steep onset of action potentials in human pyramidal neurons during spike trains, unlike in rodent neurons. Our data show that, in contrast to the widely held views of limited information transfer in rodent depressing synapses, fast recovering synapses of human neurons can actually transfer substantial amounts of information during spike trains. In addition, human pyramidal neurons are equipped to encode high synaptic information content. Thus, adult human cortical microcircuits relay information at a wider bandwidth than rodent microcircuits.

Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits

Short-term synaptic plasticity is highly diverse across brain area, cortical layer, cell type, and developmental stage. Since short-term plasticity (STP) strongly shapes neural dynamics, this diversity suggests a specific and essential role in neural information processing. Therefore, a correct characterization of short-term synaptic plasticity is an important step towards understanding and modeling neural systems. Phenomenological models have been developed, but they are usually fitted to experimental data using least-mean-square methods. We demonstrate that for typical synaptic dynamics such fitting may give unreliable results. As a solution, we introduce a Bayesian formulation, which yields the posterior distribution over the model parameters given the data. First, we show that common STP protocols yield broad distributions over some model parameters. Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters. Next, we infer the model parameters using experimental data from three different neocortical excitatory connection types. This reveals connection-specific distributions, which we use to classify synaptic dynamics. Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data.

Matched pre- and post-synaptic changes underlie synaptic plasticity over long time scales

Modifications of synaptic efficacies are considered essential for learning and memory. However, it is not known how the underlying functional components of synaptic transmission change over long time scales. To address this question, we studied cortical synapses from young Wistar rats before and after 12 h intervals of spontaneous or glutamate-induced spiking activity. We found that, under these conditions, synaptic efficacies can increase or decrease by up to 10-fold. Statistical analyses reveal that these changes reflect modifications in the number of presynaptic release sites, together with postsynaptic changes that maintain the quantal size per release site. The quantitative relation between the presynaptic and postsynaptic transmission components was not affected when synaptic plasticity was enhanced or reduced using a broad range of pharmacological agents. These findings suggest that ongoing synaptic plasticity results in matched presynaptic and postsynaptic modifications, in which elementary modules that span the synaptic cleft are added or removed as a function of experience.

Theoretical models of synaptic short term plasticity

Short term plasticity is a highly abundant form of rapid, activity-dependent modulation of synaptic efficacy. A shared set of mechanisms can cause both depression and enhancement of the postsynaptic response at different synapses, with important consequences for information processing. Mathematical models have been extensively used to study the mechanisms and roles of short term plasticity. This review provides an overview of existing models and their biological basis, and of their main properties. Special attention will be given to slow processes such as calcium channel inactivation and the effect of activation of presynaptic autoreceptors.

Short-term synaptic depression and stochastic vesicle dynamics reduce and shape neuronal correlations

Correlated neuronal activity is an important feature in many neural codes, a neural correlate of a variety of cognitive states, as well as a signature of several disease states in the nervous system. The cellular and circuit mechanics of neural correlations is a vibrant area of research. Synapses throughout the cortex exhibit a form of short-term depression where increased presynaptic firing rates deplete neurotransmitter vesicles, which transiently reduces synaptic efficacy. The release and recovery of these vesicles are inherently stochastic, and this stochasticity introduces variability into the conductance elicited by depressing synapses. The impact of spiking and subthreshold membrane dynamics on the transfer of neuronal correlations has been studied intensively, but an investigation of the impact of short-term synaptic depression and stochastic vesicle dynamics on correlation transfer is lacking. We find that short-term synaptic depression and stochastic vesicle dynamics can substantially reduce correlations, shape the timescale over which these correlations occur, and alter the dependence of spiking correlations on firing rate. Our results show that short-term depression and stochastic vesicle dynamics need to be taken into account when modeling correlations in neuronal populations.

Decorrelation of neural-network activity by inhibitory feedback

Correlations in spike-train ensembles can seriously impair the encoding of information by their spatio-temporal structure. An inevitable source of correlation in finite neural networks is common presynaptic input to pairs of neurons. Recent studies demonstrate that spike correlations in recurrent neural networks are considerably smaller than expected based on the amount of shared presynaptic input. Here, we explain this observation by means of a linear network model and simulations of networks of leaky integrate-and-fire neurons. We show that inhibitory feedback efficiently suppresses pairwise correlations and, hence, population-rate fluctuations, thereby assigning inhibitory neurons the new role of active decorrelation. We quantify this decorrelation by comparing the responses of the intact recurrent network (feedback system) and systems where the statistics of the feedback channel is perturbed (feedforward system). Manipulations of the feedback statistics can lead to a significant increase in the power and coherence of the population response. In particular, neglecting correlations within the ensemble of feedback channels or between the external stimulus and the feedback amplifies population-rate fluctuations by orders of magnitude. The fluctuation suppression in homogeneous inhibitory networks is explained by a negative feedback loop in the one-dimensional dynamics of the compound activity. Similarly, a change of coordinates exposes an effective negative feedback loop in the compound dynamics of stable excitatory-inhibitory networks. The suppression of input correlations in finite networks is explained by the population averaged correlations in the linear network model: In purely inhibitory networks, shared-input correlations are canceled by negative spike-train correlations. In excitatory-inhibitory networks, spike-train correlations are typically positive. Here, the suppression of input correlations is not a result of the mere existence of correlations between excitatory (E) and inhibitory (I) neurons, but a consequence of a particular structure of correlations among the three possible pairings (EE, EI, II).

Short term synaptic depression imposes a frequency dependent filter on synaptic information transfer

Depletion of synaptic neurotransmitter vesicles induces a form of short term depression in synapses throughout the nervous system. This plasticity affects how synapses filter presynaptic spike trains. The filtering properties of short term depression are often studied using a deterministic synapse model that predicts the mean synaptic response to a presynaptic spike train, but ignores variability introduced by the probabilistic nature of vesicle release and stochasticity in synaptic recovery time. We show that this additional variability has important consequences for the synaptic filtering of presynaptic information. In particular, a synapse model with stochastic vesicle dynamics suppresses information encoded at lower frequencies more than information encoded at higher frequencies, while a model that ignores this stochasticity transfers information encoded at any frequency equally well. This distinction between the two models persists even when large numbers of synaptic contacts are considered. Our study provides strong evidence that the stochastic nature neurotransmitter vesicle dynamics must be considered when analyzing the information flow across a synapse.

Development of synaptic connectivity onto interneurons in stratum radiatum in the CA1 region of the rat hippocampus

Background

The impact of a given presynaptic neuron on the firing probability of the postsynaptic neuron critically depends on the number of functional release sites that connect the two neurons. One way of determining the average functional synaptic connectivity onto a postsynaptic neuron is to compare the amplitudes of action potential dependent spontaneous synaptic currents with the amplitude of the synaptic currents that are independent of action potentials ("minis"). With this method it has been found that average synaptic connectivity between glutamatergic CA3 and CA1 pyramidal cells increases from single connections in the neonatal rat, to multiple connections in the young adult rat. On the other hand, γ-aminobutyric acid (GABA)ergic interneurons form multiple connections onto CA1 pyramidal cells already in the neonatal rat, and the degree of multiple GABAergic connectivity is preserved into adulthood. In the present study, we have examined the development of glutamate and GABA connectivity onto GABAergic CA1 stratum radiatum interneurons in the hippocampal slice, and compared this to the connectivity onto CA1 pyramidal neurons.

Results

In GABAergic interneurons in the CA1 stratum radiatum, irrespective of developmental stage, we found that the average amplitude of action potential dependent spontaneous AMPA receptor-mediated synaptic currents were of the same magnitude as the mini AMPA receptor mediated synaptic currents. This finding indicates that these GABAergic interneurons, in contrast to the CA1 pyramidal neurons, preserve single glutamate connectivity throughout development. For GABA connectivity, on the other hand, we found multiple functional synaptic connections onto the interneurons, as onto the pyramidal cells.

Conclusions

The results presented here confirm that glutamate and GABA synaptic connectivity develop very differently in the hippocampal CA1 region. Thus, whereas average GABA connectivity is multiple throughout the development, glutamate connectivity is unitary early in development. Our results further suggest that the development of glutamate synaptic connectivity differs markedly between pyramidal cells and GABAergic interneurons in stratum radiatum, such that a given presynaptic glutamatergic cell appears not allowed to increase its connectivity onto the postsynaptic stratum radiatum interneuron, as it may do onto the postsynaptic CA1 pyramidal cell.

Hyperconnectivity and slow synapses during early development of medial prefrontal cortex in a mouse model for mental retardation and autism

Neuronal theories of neurodevelopmental disorders (NDDs) of autism and mental retardation propose that abnormal connectivity underlies deficits in attentional processing. We tested this theory by studying unitary synaptic connections between layer 5 pyramidal neurons within medial prefrontal cortex (mPFC) networks in the Fmr1-KO mouse model for mental retardation and autism. In line with predictions from neurocognitive theory, we found that neighboring pyramidal neurons were hyperconnected during a critical period in early mPFC development. Surprisingly, excitatory synaptic connections between Fmr1-KO pyramidal neurons were significantly slower and failed to recover from short-term depression as quickly as wild type (WT) synapses. By 4-5 weeks of mPFC development, connectivity rates were identical for both KO and WT pyramidal neurons and synapse dynamics changed from depressing to facilitating responses with similar properties in both groups. We propose that the early alteration in connectivity and synaptic recovery are tightly linked: using a network model, we show that slower synapses are essential to counterbalance hyperconnectivity in order to maintain a dynamic range of excitatory activity. However, the slow synaptic time constants induce decreased responsiveness to low-frequency stimulation, which may explain deficits in integration and early information processing in attentional neuronal networks in NDDs.

Enduring medial perforant path short-term synaptic depression at high pressure

The high pressure neurological syndrome develops during deep-diving (>1.1 MPa) involving impairment of cognitive functions, alteration of synaptic transmission and increased excitability in cortico-hippocampal areas. The medial perforant path (MPP), connecting entorhinal cortex with the hippocampal formation, displays synaptic frequency-dependent-depression (FDD) under normal conditions. Synaptic FDD is essential for specific functions of various neuronal networks. We used rat cortico-hippocampal slices and computer simulations for studying the effects of pressure and its interaction with extracellular Ca²⁺ ([Ca²⁺]_o) on FDD at the MPP synapses. At atmospheric pressure, high [Ca²⁺]_o (4–6 mM) saturated single MPP field EPSP (fEPSP) and increased FDD in response to short trains at 50 Hz. High pressure (HP; 10.1 MPa) depressed single fEPSPs by 50%. Increasing [Ca²⁺]_o to 4 mM at HP saturated synaptic response at a subnormal level (only 20% recovery of single fEPSPs), but generated a FDD similar to atmospheric pressure. Mathematical model analysis of the fractions of synaptic resources used by each fEPSP during trains (normalized to their maximum) and the total fraction utilized within a train indicate that HP depresses synaptic activity also by reducing synaptic resources. This data suggest that MPP synapses may be modulated, in addition to depression of single events, by reduction of synaptic resources and then may have the ability to conserve their dynamic properties under different conditions.

Multivesicular release differentiates the reliability of synaptic transmission between the visual cortex and the somatosensory cortex

Neurons in layer 4 (L4) of the cortex play an important role in transferring signals from thalamus to other layers of the cortex. Understanding the fundamental properties of synaptic transmission between L4 neurons helps us gain a clear picture of how the neuronal network in L4 cooperates to process sensory information. In the present study, we have determined the underlying parameters that govern synaptic strength, such as quantal size, size of readily releasable vesicle pool, and release probability (Pr) of excitatory synaptic connections within L4 of the visual cortex (V1) and the somatosensory cortex (S1) in mice. Although only a single vesicle is released per release site under physiological conditions at V1 synapses, multivesicular release (MVR) is observed at S1 synapses. In addition, we observed a saturation of postsynaptic receptors at S1 synapses. Other synaptic properties are similar in both cortices. Dynamic clamp experiments suggest that higher Pr and MVR at S1 synapses lower the requirement of the number of synaptic inputs to generate postsynaptic action potentials. In addition, the slower decay of synaptic current and the intrinsic membrane properties of the postsynaptic neuron also contribute to the reliable transmission between S1 neurons.

Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials

The trajectory of the somatic membrane potential of a cortical neuron exactly reflects the computations performed on its afferent inputs. However, the spikes of such a neuron are a very low-dimensional and discrete projection of this continually evolving signal. We explored the possibility that the neuron's efferent synapses perform the critical computational step of estimating the membrane potential trajectory from the spikes. We found that short-term changes in synaptic efficacy can be interpreted as implementing an optimal estimator of this trajectory. Short-term depression arose when presynaptic spiking was sufficiently intense as to reduce the uncertainty associated with the estimate; short-term facilitation reflected structural features of the statistics of the presynaptic neuron such as up and down states. Our analysis provides a unifying account of a powerful, but puzzling, form of plasticity.

Nonequilibrium dynamics of stochastic point processes with refractoriness

Stochastic point processes with refractoriness appear frequently in the quantitative analysis of physical and biological systems, such as the generation of action potentials by nerve cells, the release and reuptake of vesicles at a synapse, and the counting of particles by detector devices. Here we present an extension of renewal theory to describe ensembles of point processes with time varying input. This is made possible by a representation in terms of occupation numbers of two states: active and refractory. The dynamics of these occupation numbers follows a distributed delay differential equation. In particular, our theory enables us to uncover the effect of refractoriness on the time-dependent rate of an ensemble of encoding point processes in response to modulation of the input. We present exact solutions that demonstrate generic features, such as stochastic transients and oscillations in the step response as well as resonances, phase jumps and frequency doubling in the transfer of periodic signals. We show that a large class of renewal processes can indeed be regarded as special cases of the model we analyze. Hence our approach represents a widely applicable framework to define and analyze nonstationary renewal processes.