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.     

Dendritic normalisation improves learning in sparsely connected artificial neural networks

Artificial neural networks, taking inspiration from biological neurons, have become an invaluable tool for machine learning applications. Recent studies have developed techniques to effectively tune the connectivity of sparsely-connected artificial neural networks, which have the potential to be more computationally efficient than their fully-connected counterparts and more closely resemble the architectures of biological systems. We here present a normalisation, based on the biophysical behaviour of neuronal dendrites receiving distributed synaptic inputs, that divides the weight of an artificial neuron’s afferent contacts by their number. We apply this dendritic normalisation to various sparsely-connected feedforward network architectures, as well as simple recurrent and self-organised networks with spatially extended units. The learning performance is significantly increased, providing an improvement over other widely-used normalisations in sparse networks. The results are two-fold, being both a practical advance in machine learning and an insight into how the structure of neuronal dendritic arbours may contribute to computation.     

人工神经网络模型在水稻群体分蘖动态模拟中外推能力的测试

研究利用人工神经网络模型 ,以水稻群体分蘖动态为例 ,采用交互验证和独立验证的方式 ,对水稻生长 BP网络模型进行了训练与模拟 ,其结果与水稻群体分蘖的积温统计模型、基本动力学模型和复合分蘖模型进行了比较。研究结果表明 ,神经网络模型具有一定的外推能力 ,但其外推能力依赖于大量的训练样本。神经网络模型具有较好的拟合能力 ,是因为有较多的模型参数 ,因此对神经网络模型的训练需要大量的参数来保证其参数不致过度吻合。具有外推能力神经网络模型的最少训练样本数应大于 6 .75倍于神经网络参数数目 ,小于 13.5倍于神经网络参数数目。因此在应用神经网络模型时 ,如果神经网络模型包括较多的输入变量时 ,可考虑采用主成分分析、对应分析等技术对输入变量进行信息综合 ,相应地减少网络模型的参数。另一方面 ,当训练样本不足时 ,最好只用神经网络模型对同一系统的情况进行模拟 ,应谨慎使用神经网络模型进行外推。神经网络模型给作物模拟研究的科学工作者提供了一个“傻瓜”式工具 ,对数学建模不熟悉的农业研究人员 ,人工神经网络可以替代数学建模进行仿真实验 ;对于精通数学建模的研究人员来说 ,它至少是一种补充和可作为比较的非线性数据处理方法     

Neuron–Astroglial Interactions in Cell-Fate Commitment and Maturation in the Central Nervous System

Neuron–astroglia interactions play a key role in several events of brain development, such as neuronal generation, migration, survival, and differentiation; axonal growth; and synapse formation and function. While there is compelling evidence of the effects of astrocyte factors on neurons, their effects on astrocytes have not been fully determined. In this review, we will focus on the role of neurons in astrocyte generation and maturation. Further, we highlight the great heterogeneity and diversity of astroglial and neural progenitors such as radial glia cells, and discuss the importance of the variety of cellular interactions in controlling the structural and functional organization of the brain. Finally, we present recent data on a new role of astrocytes in neuronal maturation, as mediators of the action of biolipids in the cerebral cortex. We will argue that the functional architecture of the brain depends on an intimate neuron-glia partnership, by briefly discussing the emerging view of how neuron-astrocyte dysfunctions might be associated with neurodegenerative diseases and neurological disorders.     

Urocortin modulates inflammatory response and neurotoxicity induced by microglial activation

Microglia are the major inflammatory cells in the brain. Recent studies have highlighted the reciprocal roles of other brain cells in modulating the microglial inflammatory responses. Urocortin (UCN) is a member of the corticotropin-releasing hormone (CRH) family of neuropeptides that function to regulate stress responses. In the present study, we demonstrated that expression of UCN in rat substantia nigra was found to be localized principally to dopaminergic neurons. In cell culture models, the CRH receptors were expressed in microglia, and CRHR expression was up-regulated by treatment with LPS. Thus, it might be proposed that UCN regulates cellular communication between dopaminergic neurons and microglia. We show that femtomolar concentrations of UCN could inhibit LPS-induced TNF-alpha production in cultured microglia. Investigation of the underlying signaling pathway that mediated the anti-inflammatory effect of UCN the involved PI3K/Akt and glycogen synthase kinase-3beta pathway, but not cAMP pathway. Furthermore, UCN protected dopaminergic neurons against LPS-induced neurotoxicity by inhibiting microglial activation in LPS-treated mesencephalic neuron-glia cultures. These results suggest that endogenous UCN and its receptors might be involved in a complex network of paracrine interaction between dopaminergic neurons and glia.     

Modelling the modulation of cortical Up-Down state switching by astrocytes

Up-Down synchronization in neuronal networks refers to spontaneous switches between periods of high collective firing activity (Up state) and periods of silence (Down state). Recent experimental reports have shown that astrocytes can control the emergence of such Up-Down regimes in neural networks, although the molecular or cellular mechanisms that are involved are still uncertain. Here we propose neural network models made of three populations of cells: excitatory neurons, inhibitory neurons and astrocytes, interconnected by synaptic and gliotransmission events, to explore how astrocytes can control this phenomenon. The presence of astrocytes in the models is indeed observed to promote the emergence of Up-Down regimes with realistic characteristics. Our models show that the difference of signalling timescales between astrocytes and neurons (seconds versus milliseconds) can induce a regime where the frequency of gliotransmission events released by the astrocytes does not synchronize with the Up and Down phases of the neurons, but remains essentially stable. However, these gliotransmission events are found to change the localization of the bifurcations in the parameter space so that with the addition of astrocytes, the network enters a bistability region of the dynamics that corresponds to Up-Down synchronization. Taken together, our work provides a theoretical framework to test scenarios and hypotheses on the modulation of Up-Down dynamics by gliotransmission from astrocytes.     

人工神经网络模型及其在农业和生态学研究中的应用

对于一些复杂的农业生态系统,人们对其生态过程了解较少,且这些系统的不确定性和模糊性较大,用传统的方法难以模拟这些系统的行为,神经网络模型因为能较精确地模拟这些系统的行为,而引起生态学者们的广泛兴趣。该文着重介绍了误差逆传神经网络模型的结构、算法及其在农业和生态学中的应用研究。误差逆传神经网络模型一般采用三层神经网络模型结构,三层的神经网络模型能模拟任意复杂程度的连续函数,而且因为它的结构小而不容易产生与训练数据的过度吻合。误差逆传神经网络模型算法的主要特征是:利用当前的输入误差对权值进行调整。在生态学和农业研究中,误差逆传神经网络模型通常作为非线性函数模拟器用于预测作物产量、生物生产量、生物与环境之间的关系等。已有的研究表明:误差逆传神经网络模型的模拟精度要远远高于多元线性方程,类似于非线性方程,而在样本量足够的情况下,有一定的外推能力。但是误差逆传神经网络模型需要大量的样本量来保证所求取参数的可靠性,但这在实际研究中很难做到,因而限制了误差逆传神经网络模型的应用。近年来人们提出了强制训练停止、复合模型等多种技术来提高误差逆传神经网络模型的外推能力,也提出了Garson算法、敏感性分析以及随机化检验等技术对误差逆传神经网络模型的机理进行解释。误差逆传神经网络模型的真正优势在于模拟人们了解较少或不确定性和模糊性较大系统的行为,这些是传统模型所无法实现的,因而是对传统机理模型的重要补充。     

Bioprocess model building using artificial neural networks

Artificial neural networks are made upon of highly interconnected layers of simple neuron-like nodes. The neurons act as non-linear processing elements within the network. An attractive property of artificial neural networks is that given the appropriate network topology, they are capable of learning and characterising non-linear functional relationships. Furthermore, the structure of the resulting neural network based process model may be considered generic, in the sense that little prior process knowledge is required in its determination. The methodology therefore provides a cost efficient and reliable process modelling technique. One area where such a technique could be useful is biotechnological systems. Here, for example, the use of a process model within an estimation scheme has long been considered an effective means of overcoming inherent on-line measurement problems. However, the development of an accurate process model is extremely time consuming and often results in a model of limited applicability. Artificial neural networks could therefore prove to be a useful model building tool when striving to improve bioprocess operability. Two large scale industrial fermentation systems have been considered as test cases; a fed-batch penicillin fermentation and a continuous mycelial fermentation. Both systems serve to demonstrate the utility, flexibility and potential of the artificial neural network approach to process modelling.     

Regulation of rhythm genesis by volume-limited,astroglia-like signals in neural networks

Rhythmic activity of the brain often depends on synchronized spiking of interneuronal networks interacting with principal neurons. The quest for physiological mechanisms regulating network synchronization has therefore been firmly focused on synaptic circuits. However, it has recently emerged that synaptic efficacy could be influenced by astrocytes that release signalling molecules into their macroscopic vicinity. To understand how this volume-limited synaptic regulation can affect oscillations in neural populations, here we explore an established artificial neural network mimicking hippocampal basket cells receiving inputs from pyramidal cells. We find that network oscillation frequencies and average cell firing rates are resilient to changes in excitatory input even when such changes occur in a significant proportion of participating interneurons, be they randomly distributed or clustered in space. The astroglia-like, volume-limited regulation of excitatory synaptic input appears to better preserve network synchronization (compared with a similar action evenly spread across the network) while leading to a structural segmentation of the network into cell subgroups with distinct firing patterns. These observations provide us with some previously unknown insights into the basic principles of neural network control by astroglia.     

GABAergic interneuron to astrocyte signalling: a neglected form of cell communication in the brain

GABAergic interneurons represent a minority of all cortical neurons and yet they efficiently control neural network activities in all brain areas. In parallel, glial cell astrocytes exert a broad control of brain tissue homeostasis and metabolism, modulate synaptic transmission and contribute to brain information processing in a dynamic interaction with neurons that is finely regulated in time and space. As most studies have focused on glutamatergic neurons and excitatory transmission, our knowledge of functional interactions between GABAergic interneurons and astrocytes is largely defective. Here, we critically discuss the currently available literature that hints at a potential relevance of this specific signalling in brain function. Astrocytes can respond to GABA through different mechanisms that include GABA receptors and transporters. GABA-activated astrocytes can, in turn, modulate local neuronal activity by releasing gliotransmitters including glutamate and ATP. In addition, astrocyte activation by different signals can modulate GABAergic neurotransmission. Full clarification of the reciprocal signalling between different GABAergic interneurons and astrocytes will improve our understanding of brain network complexity and has the potential to unveil novel therapeutic strategies for brain disorders.     

Gamma-interferon promotes differentiation of cultured cortical and hippocampal neurons

Clinical and experimental evidence suggests that the development of the brain may be modulated by soluble growth factors traditionally associated with cells of the immune system. As part of an investigation into agents modulating early neural differentiation, we examined the effects of the lymphokine gamma-interferon (IFN-gamma) on the development of cultured cortical and hippocampal neurons from embryonic rats and mice. We report here that recombinant IFN-gamma, at concentrations of 0.2-10 U/ml (50-2500 pg/ml, 3-150 pM), affects the differentiation of embryonic central neurons. IFN-gamma increased the number of cells expressing neurofilament (NF) protein, the growth of primary and secondary neurites on NF-expressing somas, and the extent of cell aggregation observed in culture. IFN-gamma-induced increases in the numbers of NF-positive cells were seen in the virtual absence of differentiated astrocytes, and in mixed neuron-glia cultures. Our results thus indicate that at physiologically relevant concentrations IFN-gamma acts, either directly on neurons and their precursor cells and/or indirectly via nonneuronal cell stimulation, to promote the differentiation of immature neurons.     

Computational neurogenetic modelling: a pathway to new discoveries in genetic neuroscience

The paper presents a methodology for using computational neurogenetic modelling (CNGM) to bring new original insights into how genes influence the dynamics of brain neural networks. CNGM is a novel computational approach to brain neural network modelling that integrates dynamic gene networks with artificial neural network model (ANN). Interaction of genes in neurons affects the dynamics of the whole ANN model through neuronal parameters, which are no longer constant but change as a function of gene expression. Through optimization of interactions within the internal gene regulatory network (GRN), initial gene/protein expression values and ANN parameters, particular target states of the neural network behaviour can be achieved, and statistics about gene interactions can be extracted. In such a way, we have obtained an abstract GRN that contains predictions about particular gene interactions in neurons for subunit genes of AMPA, GABAA and NMDA neuro-receptors. The extent of sequence conservation for 20 subunit proteins of all these receptors was analysed using standard bioinformatics multiple alignment procedures. We have observed abundance of conserved residues but the most interesting observation has been the consistent conservation of phenylalanine (F at position 269) and leucine (L at position 353) in all 20 proteins with no mutations. We hypothesise that these regions can be the basis for mutual interactions. Existing knowledge on evolutionary linkage of their protein families and analysis at molecular level indicate that the expression of these individual subunits should be coordinated, which provides the biological justification for our optimized GRN.     

Shapes of astrocyte networks in the juvenile brain

The high level of intercellular communication mediated by gap junctions between astrocytes indicates that, besides individual astrocytic domains, a second level of organization might exist for these glial cells as they form communicating networks. Therefore,the contribution of astrocytes to brain function should also be considered to result from coordinated groups of cells. To evaluate the shape and extent of these networks we have studied the expression of connexin 43, a major gap junction protein in astrocytes, and the intercellular diffusion of gap junction tracers in two structures of the developing brain, the hippocampus and the cerebral cortex. We report that the shape of astrocytic networks depends on their location within neuronal compartments ina defined brain structure. Interestingly, not all astrocytes are coupled, which indicates that connections within these networks are restricted. As gap junctional communication in astrocytes is reported to contribute to several glial functions, differences in the shape of astrocytic networks might have consequences on neuronal activity and survival.     

Neurons set the tone of gap junctional communication in astrocytic networks

A number of studies have contributed to demonstrate that neurons and astrocytes tightly and actively interact. Indeed, the presence of astrocytes in neuronal cultures increases the number of synapses and their efficiency, and thanks to enzymatic and uptake processes, astrocytes play a role in neuroprotection. A typical feature of astrocytes is that they establish cell-cell communication in vitro, as well as in situ, through intercellular channels forming specialized membrane areas defined as gap junctions. These channels are composed of junctional proteins termed connexins (Cxs): in astrocytes connexin 43 (Cx43) and 30 (Cx30) have been shown to prevail. Several recent works indicate that gap junctional communication (GJC) and/or connexin expression in astrocytes are controlled by neurons. Altogether, these observations lead to the concept that neuronal and astrocytic networks interact through mutual setting of their respective mode of communication and that astrocyte gap junctions represent a target in neuroglial interaction.     

ATP mediates calcium signaling between astrocytes and microglial cells: modulation by IFN-gamma

Calcium-mediated intercellular communication is a mechanism by which astrocytes communicate with each other and modulate the activity of adjacent cells, including neurons and oligodendrocytes. We have investigated whether microglia, the immune effector cells involved in several diseases of the CNS, are actively involved in this communication network. To address this issue, we analyzed calcium dynamics in fura-2-loaded cocultures of astrocytes and microglia under physiological conditions and in the presence of the inflammatory cytokine IFN-gamma. The intracellular calcium increases in astrocytes, occurring spontaneously or as a result of mechanical or bradykinin stimulation, induced the release of ATP, which, in turn, was responsible for triggering a delayed calcium response in microglial cells. Repeated stimulations of microglial cells by astrocyte-released ATP activated P2X(7) purinergic receptor on microglial cells and greatly increased membrane permeability, eventually leading to microglial apoptosis. IFN-gamma increased ATP release and potentiated the P2X(7)-mediated cytolytic effect. This is the first study showing that ATP mediates a form of calcium signaling between astrocytes and microglia. This mechanism of intercellular communication may be involved in controlling the number and function of microglial cells under pathophysiologic CNS conditions.     

Towards an artificial brain

Three components of a brain model operating on neuromolecular computing principles are described. The first component comprises neurons whose input-output behavior is controlled by significant internal dynamics. Models of discrete enzymatic neurons, reaction-diffusion neurons operating on the basis of the cyclic nucleotide cascade, and neurons controlled by cytoskeletal dynamics are described. The second component of the model is an evolutionary learning algorithm which is used to mold the behavior of enzyme-driven neurons or small networks of these neurons for specific function, usually pattern recognition or target seeking tasks. The evolutionary learning algorithm may be interpreted either as representing the mechanism of variation and natural selection acting on a phylogenetic time scale, or as a conceivable ontogenetic adaptation mechanism. The third component of the model is a memory manipulation scheme, called the reference neuron scheme. In principle it is capable of orchestrating a repertoire of enzyme-driven neurons for coherent function. The existing implementations, however, utilize simple neurons without internal dynamics. Spatial navigation and simple game playing (using tic-tac-toe) provide the task environments that have been used to study the properties of the reference neuron model. A memory-based evolutionary learning algorithm has been developed that can assign credit to the individual neurons in a network. It has been run on standard benchmark tasks, and appears to be quite effective both for conventional neural nets and for networks of discrete enzymatic neurons. The models have the character of artificial worlds in that they map the hierarchy of processes in the brain (at the molecular, neuronal, and network levels), provide a task environment, and use this relatively self-contained setup to develop and evaluate learning and adaptation algorithms.     

The functional role of different neural activation profiles during precision grip: An artificial neural network approach

A dynamic and recurrent artificial neural network was used to investigate the functional properties of firing patterns observed in the primary motor (M1) and the primary somatosensory (S1) cortex of the behaving monkey during control of precision grip force. In the behaving monkey it was found that neurons in M1 and in S1 increase their firing activity with increasing grip force, as do the intrinsic and extrinsic hand muscles implicated in the task. However, some neurons also decreased their activity as a function of increasing force. The functional implication of these latter neurons is not clear and has not been elucidated so far. In order to explore their functional implication, we therefore simulated patterns of neural activity in artificial neural networks that represent cortical, spinal and afferent neural populations and tested whether particular activity profiles would emerge as a function of the input and of the connectivity of these networks. The functional implication of units with emergent or imposed decreasing activity was then explored.Decreasing patterns of activity in M1 units did not emerge from the networks. However, the same networks generated decreasing activity if imposed as target patterns. As indicated by the emerging weight space, M1 projection units with decreasing patterns are functionally less involved in driving alpha motoneurons than units with increasing profiles. Furthermore, these units did not provide significant fusimotor drive, whereas those with increasing profiles did. Fusimotor drive was a function of the (imposed) form of muscle spindle afferent activity: with gamma (fusimotor) drive, muscle spindle afferents provided signals other than muscle length (as observed experimentally). The network solutions thus predict a functional dichotomy between increasing and decreasing M1 neurons: the former primarily drive alpha and gamma motoneurons, the latter only weakly alpha motoneurons.