Firing synchronization and temporal order in noisy neuronal networks

Noise-induced complete synchronization and frequency synchronization in coupled spiking and bursting neurons are studied firstly. The effects of noise and coupling are discussed. It is found that bursting neurons are easier to achieve firing synchronization than spiking ones, which means that bursting activities are more important for information transfer in neuronal networks. Secondly, the effects of noise on firing synchronization in a noisy map neuronal network are presented. Noise-induced synchronization and temporal order are investigated by means of the firing rate function and the order index. Firing synchronization and temporal order of excitatory neurons can be greatly enhanced by subthreshold stimuli with resonance frequency. Finally, it is concluded that random perturbations play an important role in firing activities and temporal order in neuronal networks.     

Glial cells in neuronal network function

Numerous evidence demonstrates that astrocytes, a type of glial cell, are integral functional elements of the synapses, responding to neuronal activity and regulating synaptic transmission and plasticity. Consequently, they are actively involved in the processing, transfer and storage of information by the nervous system, which challenges the accepted paradigm that brain function results exclusively from neuronal network activity, and suggests that nervous system function actually arises from the activity of neuron–glia networks. Most of our knowledge of the properties and physiological consequences of the bidirectional communication between astrocytes and neurons resides at cellular and molecular levels. In contrast, much less is known at higher level of complexity, i.e. networks of cells, and the actual impact of astrocytes in the neuronal network function remains largely unexplored. In the present article, we summarize the current evidence that supports the notion that astrocytes are integral components of nervous system networks and we discuss some functional properties of intercellular signalling in neuron–glia networks.     

狂犬病毒作为神经示踪剂的研究进展

狂犬病毒是仅有的完全特异的示踪剂,因为它能逆向通过化学突触进行神经示踪且不改变神经细胞的代谢,可以逐级的,时间依赖的方式感染大量的突触联系的神经网络.根据狂犬病毒的特性解释该病毒作为神经示踪剂的优势,总结狂犬病毒跨神经进行示踪的方法,并对基因修饰的狂犬病毒的新兴技术进行讨论和展望.     

Estimating Temporal Causal Interaction between Spike Trains with Permutation and Transfer Entropy

Estimating the causal interaction between neurons is very important for better understanding the functional connectivity in neuronal networks. We propose a method called normalized permutation transfer entropy (NPTE) to evaluate the temporal causal interaction between spike trains, which quantifies the fraction of ordinal information in a neuron that has presented in another one. The performance of this method is evaluated with the spike trains generated by an Izhikevich’s neuronal model. Results show that the NPTE method can effectively estimate the causal interaction between two neurons without influence of data length. Considering both the precision of time delay estimated and the robustness of information flow estimated against neuronal firing rate, the NPTE method is superior to other information theoretic method including normalized transfer entropy, symbolic transfer entropy and permutation conditional mutual information. To test the performance of NPTE on analyzing simulated biophysically realistic synapses, an Izhikevich’s cortical network that based on the neuronal model is employed. It is found that the NPTE method is able to characterize mutual interactions and identify spurious causality in a network of three neurons exactly. We conclude that the proposed method can obtain more reliable comparison of interactions between different pairs of neurons and is a promising tool to uncover more details on the neural coding.     

Synaptic information transfer in computer models of neocortical columns

Understanding the direction and quantity of information flowing in neuronal networks is a fundamental problem in neuroscience. Brains and neuronal networks must at the same time store information about the world and react to information in the world. We sought to measure how the activity of the network alters information flow from inputs to output patterns. Using neocortical column neuronal network simulations, we demonstrated that networks with greater internal connectivity reduced input/output correlations from excitatory synapses and decreased negative correlations from inhibitory synapses, measured by Kendall’s τ correlation. Both of these changes were associated with reduction in information flow, measured by normalized transfer entropy (nTE). Information handling by the network reflected the degree of internal connectivity. With no internal connectivity, the feedforward network transformed inputs through nonlinear summation and thresholding. With greater connectivity strength, the recurrent network translated activity and information due to contribution of activity from intrinsic network dynamics. This dynamic contribution amounts to added information drawn from that stored in the network. At still higher internal synaptic strength, the network corrupted the external information, producing a state where little external information came through. The association of increased information retrieved from the network with increased gamma power supports the notion of gamma oscillations playing a role in information processing.     

A compartmental approach in modeling a neuronal network. Role of inhibitory and excitatory processes]

The conception and main principles of the compartmental approach to the modeling of neuronal networks are presented. Excitatory and inhibitory ties are described, and reducible and nonreducible neuronal networks are considered. The periodical solution in models of neuronal networks is the model of normal rhythmic (e.g., respiratory) activity. The role of external (chemoreceptor drives) is discussed. The biological interpretation of the results of modeling is given. The observed biological phenomena are considered.     

Differential regulation at functionally divergent release sites along a common axon

Information transfer within neuronal networks requires the precise coordination of distinct neuronal populations within a given circuit. Evidence from a variety of central pathways indicates that such coordination is mediated in part by the ability of neurons to differentially regulate release properties at functionally divergent presynaptic elements along their individual axons according to the identity of the postsynaptic cell being innervated. Recent findings have revealed the cellular mechanisms by which central afferents modify release properties at individual presynaptic sites independent of neighboring terminals. Such autonomy of presynaptic regulation enables target-cell-dependent short-term and long-term synaptic plasticity and ensures that distinct features of afferent activity are relayed to divergent target-cell populations.     

Orientation Selectivity in Inhibition-Dominated Networks of Spiking Neurons: Effect of Single Neuron Properties and Network Dynamics

The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are robust and do not depend on the state of network dynamics, and hold equally well for mean-driven and fluctuation-driven regimes of activity.     

Two-photon compatibility and single-voxel, single-trial detection of subthreshold neuronal activity by a two-component optical voltage sensor

Minimally invasive measurements of neuronal activity are essential for understanding how signal processing is performed by neuronal networks. While optical strategies for making such measurements hold great promise, optical sensors generally lack the speed and sensitivity necessary to record neuronal activity on a single-trial, single-neuron basis. Here we present additional biophysical characterization and practical improvements of a two-component optical voltage sensor (2cVoS), comprised of the neuronal tracer dye, DiO, and dipicrylamine (DiO/DPA). Using laser spot illumination we demonstrate that membrane potential-dependent fluorescence changes can be obtained in a wide variety of cell types within brain slices. We show a correlation between membrane labeling and the sensitivity of the magnitude of fluorescence signal, such that neurons with the brightest membrane labeling yield the largest ΔF/F values per action potential (AP; ∼40%). By substituting a blue-shifted donor for DiO we confirm that DiO/DPA works, at least in part, via a Förster resonance energy transfer (FRET) mechanism. We also describe a straightforward iontophoretic method for labeling multiple neurons with DiO and show that DiO/DPA is compatible with two-photon (2P) imaging. Finally, exploiting the high sensitivity of DiO/DPA, we demonstrate AP-induced fluorescence transients (fAPs) recorded from single spines of hippocampal pyramidal neurons and single-trial measurements of subthreshold synaptic inputs to granule cell dendrites. Our findings suggest that the 2cVoS, DiO/DPA, enables optical measurements of trial-to-trial voltage fluctuations with very high spatial and temporal resolution, properties well suited for monitoring electrical signals from multiple neurons within intact neuronal networks.     

体外培养的神经元网络可塑性

神经元网络是大脑执行高级认知行为的结构基础,研究证明学习记忆及神经退行性疾病与神经元网络可塑性密切相关。因此,揭示调控和改变神经元网络可塑性的机制对理解神经系统信息交互以及疾病治疗具有重大意义。目前,基于微电极阵列（microelectrode array, MEA）培养的神经元网络是体外探究学习和记忆机制的理想模型,同时针对该模型的研究为预防和治疗神经退行性疾病提供了独特的视角。本文综述了基于MEA采集体外培养神经元网络的放电信号来构建功能网络的相关研究,分别从二维神经元网络和三维脑类器官发育,以及开环和闭环电刺激对神经元网络可塑性影响的角度,总结了体外培养神经元网络可塑性的相关研究,最后对该方向的应用前景进行了展望。     

Small is beautiful: models of small neuronal networks

Modeling has contributed a great deal to our understanding of how individual neurons and neuronal networks function. In this review, we focus on models of the small neuronal networks of invertebrates, especially rhythmically active CPG networks. Models have elucidated many aspects of these networks, from identifying key interacting membrane properties to pointing out gaps in our understanding, for example missing neurons. Even the complex CPGs of vertebrates, such as those that underlie respiration, have been reduced to small network models to great effect. Modeling of these networks spans from simplified models, which are amenable to mathematical analyses, to very complicated biophysical models. Some researchers have now adopted a population approach, where they generate and analyze many related models that differ in a few to several judiciously chosen free parameters; often these parameters show variability across animals and thus justify the approach. Models of small neuronal networks will continue to expand and refine our understanding of how neuronal networks in all animals program motor output, process sensory information and learn.     

Neurochemical aspects of interneuronal communication

It is suggested that the term neurotransmission, which is used to designate neuronal communication at synaptic level, be associated to the less restrictive term neuromodulation. These two types of intercellular communication seem in fact to be two basically different mechanisms, both of which contribute to neuronal integration. The integration of neuronal information at cellular level appears to be more complex than the simple addition of excitatory plus inhibitory influences eliciting postsynaptic responses. Evidence has been obtained that non synaptic transmission can alter the capacity of a given synapse to transfer neuronal information from the presynaptic element to the postsynaptic neuron. For instance, presynaptic mechanisms provide evidence for the functional independence of the nerve terminals, since the release of neuromediators by the latter is sometimes independent of the axonal firing rate. Similarly, the somato-dendritic part of some neurons exhibits intrinsic functions, such as a dendritic release of neuromediator, suggesting that the control of the axonal firing rate takes place partly at this somato-dendritic level and does not depend for the totality on afferent axonic information. The intercellular operations which organize individual neurons into neuronal networks will also occur either at somato-dendritic level or at the level of specific nerve terminals selected as the result of presynaptic interactions. This integration of neuronal information also seems to take place at postsynaptic level, where cooperative interactions have been shown to occur between various receptors. These mechanisms will function at the level of a single nerve terminal containing more than one neuromediator. Neuromodulation can therefore be said to involve very efficient adaptive processes, which help to account for the fact that such large behavioral responses are expressed by such a small number of neuronal elements.     

Pharmacological Characterization of Cultivated Neuronal Networks: Relevance to Synaptogenesis and Synaptic Connectivity

Mental disorders, such as schizophrenia or Alzheimer’s disease, are associated with impaired synaptogenesis and/or synaptic communication. During development, neurons assemble into neuronal networks, the primary supracellular mediators of information processing. In addition to the orchestrated activation of genetic programs, spontaneous electrical activity and associated calcium signaling have been shown to be critically involved in the maturation of such neuronal networks. We established an in vitro model that recapitulates the maturation of neuronal networks, including spontaneous electrical activity. Upon plating, mouse primary hippocampal neurons grow neurites and interconnect via synapses to form a dish-wide neuronal network. Via live cell calcium imaging, we identified a limited period of time in which the spontaneous activity synchronizes across neurons, indicative of the formation of a functional network. After establishment of network activity, the neurons grow dendritic spines, the density of which was used as a morphological readout for neuronal maturity and connectivity. Hence, quantification of neurite outgrowth, synapse density, spontaneous neuronal activity, and dendritic spine density allowed to study neuronal network maturation from the day of plating until the presence of mature neuronal networks. Via acute pharmacological intervention, we show that synchronized network activity is mediated by the NMDA-R. The balance between kynurenic and quinolinic acid, both neuro-active intermediates in the tryptophan/kynurenine pathway, was shown to be decisive for the maintenance of network activity. Chronic modulation of the neurotrophic support influenced the network formation and revealed the extreme sensitivity of calcium imaging to detect subtle alterations in neuronal physiology. Given the reproducible cultivation in a 96-well setup in combination with fully automated analysis of the calcium recordings, this approach can be used to build a high-content screening assay usable for neurotoxicity screening, target identification/validation, or phenotypic drug screening.     

Validation of long-term primary neuronal cultures and network activity through the integration of reversibly bonded microbioreactors and MEA substrates

In vitro recording of neuronal electrical activity is a widely used technique to understand brain functions and to study the effect of drugs on the central nervous system. The integration of microfluidic devices with microelectrode arrays (MEAs) enables the recording of networks activity in a controlled microenvironment. In this work, an integrated microfluidic system for neuronal cultures was developed, reversibly coupling a PDMS microfluidic device with a commercial flat MEA through magnetic forces. Neurons from mouse embryos were cultured in a 100 μm channel and their activity was followed up to 18 days in vitro. The maturation of the networks and their morphological and functional characteristics were comparable with those of networks cultured in macro-environments and described in literature. In this work, we successfully demonstrated the ability of long-term culturing of primary neuronal cells in a reversible bonded microfluidic device (based on magnetism) that will be fundamental for neuropharmacological studies.     

Coemergence of regularity and complexity during neural network development

With the growing recognition that rhythmic and oscillatory patterns are widespread in the brain and play important roles in all aspects of the function of our nervous system, there has been a resurgence of interest in neuronal synchronized bursting activity. Here, we were interested in understanding the development of synchronized bursts as information-bearing neuronal activity patterns. For that, we have monitored the morphological organization and spontaneous activity of neuronal networks cultured on multielectrode-arrays during their self-executed evolvement from a mixture of dissociated cells into an active network. Complex collective network electrical activity evolved from sporadic firing patterns of the single neurons. On the system (network) level, the activity was marked by bursting events with interneuronal synchronization and nonarbitrary temporal ordering. We quantified these individual-to-collective activity transitions using newly-developed system level quantitative measures of time series regularity and complexity. We found that individual neuronal activity before synchronization was characterized by high regularity and low complexity. During neuronal wiring, there was a transient period of reorganization marked by low regularity, which then leads to coemergence of elevated regularity and functional (nonstochastic) complexity. We further investigated the morphology-activity interplay by modeling artificial neuronal networks with different topological organizations and connectivity schemes. The simulations support our experimental results by showing increased levels of complexity of neuronal activity patterns when neurons are wired up and organized in clusters (similar to mature real networks), as well as network-level activity regulation once collective activity forms.     

Organization of neocortical neuronal networks

Multiple unit activity in deep layers of the frontal and motor cortices was recorded by chronically implanted semimicroelectrodes in waking cats with different levels of food motivation. From four to seven neuronal spike trains were selected from the recorded multiunit activity. Interactions between neighbouring neurons in the motor and frontal areas of the neocortex (within the local neuronal networks) and between the neurons of these areas (distributed neuronal networks) were estimated by means of statistical crosscorrelation analysis of spike trains within the range of delays from 0 to 100 ms. Neurons in the local networks were divided in two subgroups: the neurons with higher spike amplitudes with the dominance of divergent connections and neurons with lower spike amplitudes with the dominance of convergent connections. Strong monosynaptic connections (discharges with a delay of less than 2 ms) between the neurons with high- and low-amplitude spikes formed the background of the local networks. Connections between low-amplitude neurons in the frontal cortex and high-amplitude neurons in the motor cortex dominated in the distributed networks. A 24-hour food deprivation predominantly altered the late interneuronal crosscorrelations with time delays within the range of 2-100 ms in both local and distributed networks.     

Network reconfiguration and neuronal plasticity in rhythm-generating networks

Neuronal networks are highly plastic and reconfigure in a state-dependent manner. The plasticity at the network level emerges through multiple intrinsic and synaptic membrane properties that imbue neurons and their interactions with numerous nonlinear properties. These properties are continuously regulated by neuromodulators and homeostatic mechanisms that are critical to maintain not only network stability and also adapt networks in a short- and long-term manner to changes in behavioral, developmental, metabolic, and environmental conditions. This review provides concrete examples from neuronal networks in invertebrates and vertebrates, and illustrates that the concepts and rules that govern neuronal networks and behaviors are universal.     

Die Systemtheorie der homogenen Schichten

A systems theory describing signal transmission in neuronal layer structures is presented. Such structures having the quality of linearity and homogenity can easily be treated by using a multiple Fourier transformation method with respect to the space coordinates and to the time. The characteristic laws of this transformation are discussed. In analogy to linear networks the transfer function, the impulse response function and the step response function are defined which characterizes the layer system. Appropriate test functions are derived. Typical structures of standing and moving patterns are finally discussed. Biological application and simulation of the theory by a system using coherent optics will be presented later.