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Journal articles on the topic 'Modeling Neural Glial Circuits'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Xavier, Anna L., João R. L. Menezes, Steven A. Goldman, and Maiken Nedergaard. "Fine-tuning the central nervous system: microglial modelling of cells and synapses." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1654 (October 19, 2014): 20130593. http://dx.doi.org/10.1098/rstb.2013.0593.

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Microglia constitute as much as 10–15% of all cells in the mammalian central nervous system (CNS) and are the only glial cells that do not arise from the neuroectoderm. As the principal CNS immune cells, microglial cells represent the first line of defence in response to exogenous threats. Past studies have largely been dedicated to defining the complex immune functions of microglial cells. However, our understanding of the roles of microglia has expanded radically over the past years. It is now clear that microglia are critically involved in shaping neural circuits in both the developing and adult CNS, and in modulating synaptic transmission in the adult brain. Intriguingly, microglial cells appear to use the same sets of tools, including cytokine and chemokine release as well as phagocytosis, whether modulating neural function or mediating the brain's innate immune responses. This review will discuss recent developments that have broadened our views of neuro-glial signalling to include the contribution of microglial cells.

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2

Hoffmann, Anke, Michael Ziller, and Dietmar Spengler. "Progress in iPSC-Based Modeling of Psychiatric Disorders." International Journal of Molecular Sciences 20, no. 19 (October 2, 2019): 4896. http://dx.doi.org/10.3390/ijms20194896.

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Progress in iPSC-based cellular systems provides new insights into human brain development and early neurodevelopmental deviations in psychiatric disorders. Among these, studies on schizophrenia (SCZ) take a prominent role owing to its high heritability and multifarious evidence that it evolves from a genetically induced vulnerability in brain development. Recent iPSC studies on patients with SCZ indicate that functional impairments of neural progenitor cells (NPCs) in monolayer culture extend to brain organoids by disrupting neocorticogenesis in an in vitro model. In addition, the formation of hippocampal circuit-like structures in vitro is impaired in patients with SCZ as is the case for glia development. Intriguingly, chimeric-mice experiments show altered oligodendrocyte and astrocyte development in vivo that highlights the importance of cell–cell interactions in the pathogenesis of early-onset SCZ. Likewise, cortical imbalances in excitatory–inhibitory signaling may result from a cell-autonomous defect in cortical interneuron (cIN) development. Overall, these findings indicate that genetic risk in SCZ impacts neocorticogenesis, hippocampal circuit formation, and the development of distinct glial and neuronal subtypes. In light of this remarkable progress, we discuss current limitations and further steps necessary to harvest the full potential of iPSC-based investigations on psychiatric disorders.

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3

Nadkarni, Suhita, and Peter Jung. "Dressed neurons: modeling neural–glial interactions." Physical Biology 1, no. 1 (February 12, 2004): 35–41. http://dx.doi.org/10.1088/1478-3967/1/1/004.

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4

Postnov, D. E., L. S. Ryazanova, and O. V. Sosnovtseva. "Functional modeling of neural–glial interaction." Biosystems 89, no. 1-3 (May 2007): 84–91. http://dx.doi.org/10.1016/j.biosystems.2006.04.012.

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5

KONISHI, EIJI. "MODELING QUANTUM MECHANICAL OBSERVERS VIA NEURAL-GLIAL NETWORKS." International Journal of Modern Physics B 26, no. 09 (April 10, 2012): 1250060. http://dx.doi.org/10.1142/s0217979212500609.

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We investigate the theory of observers in the quantum mechanical world by using a novel model of the human brain which incorporates the glial network into the Hopfield model of the neural network. Our model is based on a microscopic construction of a quantum Hamiltonian of the synaptic junctions. Using the Eguchi–Kawai large N reduction, we show that, when the number of neurons and astrocytes is exponentially large, the degrees of freedom (d.o.f) of the dynamics of the neural and glial networks can be completely removed and, consequently, that the retention time of the superposition of the wavefunctions in the brain is as long as that of the microscopic quantum system of pre-synaptics sites. Based on this model, the classical information entropy of the neural-glial network is introduced. Using this quantity, we propose a criterion for the brain to be a quantum mechanical observer.

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6

Andrejevic, Miona, and Vanco Litovski. "Electronic circuits modeling using artificial neural networks." Journal of Automatic Control 13, no. 1 (2003): 31–37. http://dx.doi.org/10.2298/jac0301031a.

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In this paper artificial neural networks (ANN) are applied to modeling of electronic circuits. ANNs are used for application of the black-box modeling concept in the time domain. Modeling process is described, so the topology of the ANN, the testing signal used for excitation, together with the complexity of ANN are considered. The procedure is first exemplified in modeling of resistive circuits. MOS transistor, as a four-terminal device, is modeled. Then nonlinear negative resistive characteristic is modeled in order to be used as a piece-wise linear resistor in Chua's circuit. Examples of modeling nonlinear dynamic circuits are given encompassing a variety of modeling problems. A nonlinear circuit containing quartz oscillator is considered for modeling. Verification of the concept is performed by verifying the ability of the model to generalize i.e. to create acceptable responses to excitations not used during training. Implementation of these models within a behavioral simulator is exemplified. Every model is implemented in realistic surrounding in order to show its interaction, and of course, its usage and purpose.

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7

Tanaka, Takuma. "Modeling Cortical Neural Circuits with the Infomax Principle." Brain & Neural Networks 25, no. 3 (September 5, 2018): 104–12. http://dx.doi.org/10.3902/jnns.25.104.

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8

Selverston, A. I. "Modeling of Neural Circuits: What Have We Learned?" Annual Review of Neuroscience 16, no. 1 (March 1993): 531–46. http://dx.doi.org/10.1146/annurev.ne.16.030193.002531.

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9

Jianjun Xu, M. C. E. Yagoub, Runtao Ding, and Qi-Jun Zhang. "Neural-based dynamic modeling of nonlinear microwave circuits." IEEE Transactions on Microwave Theory and Techniques 50, no. 12 (December 2002): 2769–80. http://dx.doi.org/10.1109/tmtt.2002.805192.

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10

Gibson, William, Les Farnell, and Max Bennett. "A neural-glial network for modeling spreading depression in cortex." BMC Neuroscience 9, Suppl 1 (2008): O11. http://dx.doi.org/10.1186/1471-2202-9-s1-o11.

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11

ARADI, ILDIKÓ, and PÉTER ÉRDI. "MULTICOMPARTMENTAL MODELING OF NEURAL CIRCUITS IN THE OLFACTORY BULB." International Journal of Neural Systems 07, no. 04 (September 1996): 519–27. http://dx.doi.org/10.1142/s0129065796000506.

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The generation and propagation of action potentials in the excitatory and inhibitory cell types of the olfactory bulb are simulated by applying multi-compartmental modeling technique. Detailed models of the main cell types of the olfactory bulb have been presented previously.1 Further simulations on granule and periglomerular cells have been done to find proper parameters matching to the physiological recordings.22 Elementary synaptic interactions and dynamic behaviour of small networks of excitatory and inhibitory neurons have been studied. To investigate the relationship between the anatomical structure of the neural circuits of whole olfactory bulb and the generated firing patterns, further series of simulations have been done.

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12

Bang, Seokyoung, Kyeong Seob Hwang, Sohyeon Jeong, Il-Joo Cho, Nakwon Choi, Jongbaeg Kim, and Hong Nam Kim. "Engineered neural circuits for modeling brain physiology and neuropathology." Acta Biomaterialia 132 (September 2021): 379–400. http://dx.doi.org/10.1016/j.actbio.2021.06.024.

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13

Miller, Paul. "Dynamical systems, attractors, and neural circuits." F1000Research 5 (May 24, 2016): 992. http://dx.doi.org/10.12688/f1000research.7698.1.

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Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic—they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

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14

Le Masson, S., A. Laflaquiere, T. Bal, and G. Le Masson. "Analog circuits for modeling biological neural networks: design and applications." IEEE Transactions on Biomedical Engineering 46, no. 6 (June 1999): 638–45. http://dx.doi.org/10.1109/10.764940.

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15

Vai, M. M., Shuichi Wu, Bin Li, and S. Prasad. "Reverse modeling of microwave circuits with bidirectional neural network models." IEEE Transactions on Microwave Theory and Techniques 46, no. 10 (1998): 1492–94. http://dx.doi.org/10.1109/22.721152.

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16

Hanbay, Davut, Ibrahim Turkoglu, and Yakup Demir. "Modeling switched circuits based on wavelet decomposition and neural networks." Journal of the Franklin Institute 347, no. 3 (April 2010): 607–17. http://dx.doi.org/10.1016/j.jfranklin.2010.01.004.

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17

Real, Raquel, Manuel Peter, Antonio Trabalza, Shabana Khan, Mark A. Smith, Joana Dopp, Samuel J. Barnes, et al. "In vivo modeling of human neuron dynamics and Down syndrome." Science 362, no. 6416 (October 11, 2018): eaau1810. http://dx.doi.org/10.1126/science.aau1810.

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Harnessing the potential of human stem cells for modeling the physiology and diseases of cortical circuitry requires monitoring cellular dynamics in vivo. We show that human induced pluripotent stem cell (iPSC)–derived cortical neurons transplanted into the adult mouse cortex consistently organized into large (up to ~100 mm3) vascularized neuron-glia territories with complex cytoarchitecture. Longitudinal imaging of >4000 grafted developing human neurons revealed that neuronal arbors refined via branch-specific retraction; human synaptic networks substantially restructured over 4 months, with balanced rates of synapse formation and elimination; and oscillatory population activity mirrored the patterns of fetal neural networks. Lastly, we found increased synaptic stability and reduced oscillations in transplants from two individuals with Down syndrome, demonstrating the potential of in vivo imaging in human tissue grafts for patient-specific modeling of cortical development, physiology, and pathogenesis.

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18

Суханова, Наталия, and Nataliya Sukhanova. "ELECTRONIC CIRCUIT FAILURE MODELING USING NEURAL NETWORKS." Bulletin of Bryansk state technical university 2018, no. 8 (October 25, 2018): 76–83. http://dx.doi.org/10.30987/article_5bb5e6f323cf39.47317213.

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The object of researches is electronic circuits. For elements of the circuit there are defined characteristics of input and output signals in a working condition and at a state of non-operability. The subject of researches is a reliability of electronic circuits (EC). The purpose of the work consists in the automation of reliability tests at the expense of the failure simulation of electronic circuit elements with the aid of artificial neural networks (ANN). There is developed a method for carrying out EC reliability tests with the use of automation means. During tests one simulates different failures of circuit elements. For element failure simulation there are used ANN trained fragments. The ANN fragments are trained with the use of the selection of input and output signals of the element in a working condition and at a state non-operability. For the signal formation of a working condition a signal generator is used. For the signal formation of a state of nonoperability the signals from outputs of a noise generator are added. To reduce time and costs for training there is offered for use the ANN of a special switch type which allows copying, replicating, modifying ANN, training and forming ANN from its fragments.

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19

Ceprian, Maria, and Daniel Fulton. "Glial Cell AMPA Receptors in Nervous System Health, Injury and Disease." International Journal of Molecular Sciences 20, no. 10 (May 17, 2019): 2450. http://dx.doi.org/10.3390/ijms20102450.

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Glia form a central component of the nervous system whose varied activities sustain an environment that is optimised for healthy development and neuronal function. Alpha-amino-3-hydroxy-5-methyl-4-isoxazole (AMPA)-type glutamate receptors (AMPAR) are a central mediator of glutamatergic excitatory synaptic transmission, yet they are also expressed in a wide range of glial cells where they influence a variety of important cellular functions. AMPAR enable glial cells to sense the activity of neighbouring axons and synapses, and as such many aspects of glial cell development and function are influenced by the activity of neural circuits. However, these AMPAR also render glia sensitive to elevations of the extracellular concentration of glutamate, which are associated with a broad range of pathological conditions. Excessive activation of AMPAR under these conditions may induce excitotoxic injury in glial cells, and trigger pathophysiological responses threatening other neural cells and amplifying ongoing disease processes. The aim of this review is to gather information on AMPAR function from across the broad diversity of glial cells, identify their contribution to pathophysiological processes, and highlight new areas of research whose progress may increase our understanding of nervous system dysfunction and disease.

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20

Chotard, Carole, and Iris Salecker. "Glial cell development and function in the Drosophila visual system." Neuron Glia Biology 3, no. 1 (February 2007): 17–25. http://dx.doi.org/10.1017/s1740925x07000592.

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AbstractIn the developing nervous system, building a functional neuronal network relies on coordinating the formation, specification and survival to diverse neuronal and glial cell subtypes. The establishment of neuronal connections further depends on sequential neuron–neuron and neuron–glia interactions that regulate cell-migration patterns and axon guidance. The visual system of Drosophila has a highly regular, retinotopic organization into reiterated interconnected synaptic circuits. It is therefore an excellent invertebrate model to investigate basic cellular strategies and molecular determinants regulating the different developmental processes that lead to network formation. Studies in the visual system have provided important insights into the mechanisms by which photoreceptor axons connect with their synaptic partners within the optic lobe. In this review, we highlight that this system is also well suited for uncovering general principles that underlie glial cell biology. We describe the glial cell subtypes in the visual system and discuss recent findings about their development and migration. Finally, we outline the pivotal roles of glial cells in mediating neural circuit assembly, boundary formation, neural proliferation and survival, as well as synaptic function.

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21

Gwak, Young S., Claire E. Hulsebosch, and Joong Woo Leem. "Neuronal-Glial Interactions Maintain Chronic Neuropathic Pain after Spinal Cord Injury." Neural Plasticity 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/2480689.

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The hyperactive state of sensory neurons in the spinal cord enhances pain transmission. Spinal glial cells have also been implicated in enhanced excitability of spinal dorsal horn neurons, resulting in pain amplification and distortions. Traumatic injuries of the neural system such as spinal cord injury (SCI) induce neuronal hyperactivity and glial activation, causing maladaptive synaptic plasticity in the spinal cord. Recent studies demonstrate that SCI causes persistent glial activation with concomitant neuronal hyperactivity, thus providing the substrate for central neuropathic pain. Hyperactive sensory neurons and activated glial cells increase intracellular and extracellular glutamate, neuropeptides, adenosine triphosphates, proinflammatory cytokines, and reactive oxygen species concentrations, all of which enhance pain transmission. In addition, hyperactive sensory neurons and glial cells overexpress receptors and ion channels that maintain this enhanced pain transmission. Therefore, post-SCI neuronal-glial interactions create maladaptive synaptic circuits and activate intracellular signaling events that permanently contribute to enhanced neuropathic pain. In this review, we describe how hyperactivity of sensory neurons contributes to the maintenance of chronic neuropathic pain via neuronal-glial interactions following SCI.

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22

Zhang, Ji, Sheng Chang, Hao Wang, Jin He, and Qi Jun Huang. "Artificial Neural Network Based CNTFETs Modeling." Applied Mechanics and Materials 667 (October 2014): 390–95. http://dx.doi.org/10.4028/www.scientific.net/amm.667.390.

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Based on artificial neural network (ANN), a new method of modeling carbon nanotube field effect transistors (CNTFETs) is developed. This paper presents two ANN CNTFET models, including P-type CNTFET (PCNTFET) and N-type CNTFET (NCNTFET). In order to describe the devices more accurately, a segmentation voltage of the voltage between gate and source is defined for each type of CNTFET to segment the workspace of CNTFET. With the smooth connection by a quasi-Fermi function for, the two segmented networks of CNTFET are integrated into a whole device model and implemented by Verilog-A. To validate the ANN CNTFET models, quantitative test with different device intrinsic parameters are done. Furthermore, a complementary CNTFET inverter is designed using these NCNTFET and PCNTFET ANN models. The performances of the inverter show that our models are both efficient and accurate for simulation of nanometer scale circuits.

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23

JIE, Shao, Shu JIAN, Wang LI, and Reza Malekian. "An Improved Modeling of Nonlinear Circuits based on Elman Neural Network." Applied Mathematics & Information Sciences 8, no. 4 (July 1, 2014): 1685–90. http://dx.doi.org/10.12785/amis/080424.

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24

Lewicki, Michael S. "Bayesian Modeling and Classification of Neural Signals." Neural Computation 6, no. 5 (September 1994): 1005–30. http://dx.doi.org/10.1162/neco.1994.6.5.1005.

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Identifying and classifying action potential shapes in extracellular neural waveforms have long been the subject of research, and although several algorithms for this purpose have been successfully applied, their use has been limited by some outstanding problems. The first is how to determine shapes of the action potentials in the waveform and, second, how to decide how many shapes are distinct. A harder problem is that action potentials frequently overlap making difficult both the determination of the shapes and the classification of the spikes. In this report, a solution to each of these problems is obtained by applying Bayesian probability theory. By defining a probabilistic model of the waveform, the probability of both the form and number of spike shapes can be quantified. In addition, this framework is used to obtain an efficient algorithm for the decomposition of arbitrarily complex overlap sequences. This algorithm can extract many times more information than previous methods and facilitates the extracellular investigation of neuronal classes and of interactions within neuronal circuits.

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25

Chakraborty, Mriganka. "Artificial Neural Network for Performance Modeling and Optimization of CMOS Analog Circuits." International Journal of Computer Applications 58, no. 18 (November 15, 2012): 6–12. http://dx.doi.org/10.5120/9380-3731.

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26

Naghibi, Zohreh, Sayed Alireza Sadrossadat, and Saeed Safari. "Time-domain modeling of nonlinear circuits using deep recurrent neural network technique." AEU - International Journal of Electronics and Communications 100 (February 2019): 66–74. http://dx.doi.org/10.1016/j.aeue.2018.12.010.

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27

Hajian, Ali, and Saeed Safari. "Modeling Soft Error Propagation in Near-Threshold Combinational Circuits Using Neural Networks." Journal of Electronic Testing 35, no. 3 (May 29, 2019): 401–12. http://dx.doi.org/10.1007/s10836-019-05796-x.

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28

Morita, Kenji. "Dynamical Foundations of the Neural Circuit for Bayesian Decision Making." Journal of Neurophysiology 102, no. 1 (July 2009): 1–6. http://dx.doi.org/10.1152/jn.00239.2009.

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On the basis of accumulating behavioral and neural evidences, it has recently been proposed that the brain neural circuits of humans and animals are equipped with several specific properties, which ensure that perceptual decision making implemented by the circuits can be nearly optimal in terms of Bayesian inference. Here, I introduce the basic ideas of such a proposal and discuss its implications from the standpoint of biophysical modeling developed in the framework of dynamical systems.

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29

LITOVSKI, VANČO B., MIONA V. ANDREJEVIĆ, PREDRAG M. PETKOVIĆ, and ROBERT I. DAMPER. "ANN APPLICATION TO MODELING OF THE D/A AND A/D INTERFACE FOR MIXED-MODE BEHAVIORAL SIMULATION." Journal of Circuits, Systems and Computers 13, no. 01 (February 2004): 181–92. http://dx.doi.org/10.1142/s0218126604001325.

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Artificial neural networks are applied for modeling the input and output circuits of the digital part of the digital–analog and analog–digital interface, respectively, in CMOS mixed-mode circuits. The generalization property of the neural networks is exploited to apply the models in a set of previously unknown situations, the most important being loading the model generated from the unloaded circuit. The models developed are applicable in mixed-signal behavioral simulations.

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30

Maoz, Ori, Gašper Tkačik, Mohamad Saleh Esteki, Roozbeh Kiani, and Elad Schneidman. "Learning probabilistic neural representations with randomly connected circuits." Proceedings of the National Academy of Sciences 117, no. 40 (September 18, 2020): 25066–73. http://dx.doi.org/10.1073/pnas.1912804117.

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The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficient, learnable, and realistic neural implementation. This model’s performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable with or better than that of state-of-the-art models. Importantly, the model can be learned using a small number of samples and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation.

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31

Allen, Nicola J., and David A. Lyons. "Glia as architects of central nervous system formation and function." Science 362, no. 6411 (October 11, 2018): 181–85. http://dx.doi.org/10.1126/science.aat0473.

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Glia constitute roughly half of the cells of the central nervous system (CNS) but were long-considered to be static bystanders to its formation and function. Here we provide an overview of how the diverse and dynamic functions of glial cells orchestrate essentially all aspects of nervous system formation and function. Radial glia, astrocytes, oligodendrocyte progenitor cells, oligodendrocytes, and microglia each influence nervous system development, from neuronal birth, migration, axon specification, and growth through circuit assembly and synaptogenesis. As neural circuits mature, distinct glia fulfill key roles in synaptic communication, plasticity, homeostasis, and network-level activity through dynamic monitoring and alteration of CNS structure and function. Continued elucidation of glial cell biology, and the dynamic interactions of neurons and glia, will enrich our understanding of nervous system formation, health, and function.

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32

Bensetti, Mohamed, Fabrice Duval, and Blaise Ravelo. "THERMAL EFFECT MODELING ON PASSIVE CIRCUITS WITH MLP NEURAL NETWORK FOR EMC APPLICATION." Progress In Electromagnetics Research M 19 (2011): 39–52. http://dx.doi.org/10.2528/pierm11042602.

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33

Naghibi, Zohreh, Sayed Alireza Sadrossadat, and Saeed Safari. "Dynamic behavioral modeling of nonlinear circuits using a novel recurrent neural network technique." International Journal of Circuit Theory and Applications 47, no. 7 (April 17, 2019): 1071–85. http://dx.doi.org/10.1002/cta.2631.

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34

Yi Cao and Qi-Jun Zhang. "A New Training Approach for Robust Recurrent Neural-Network Modeling of Nonlinear Circuits." IEEE Transactions on Microwave Theory and Techniques 57, no. 6 (June 2009): 1539–53. http://dx.doi.org/10.1109/tmtt.2009.2020832.

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35

Nowotny, Thomas, Attila Szücs, Rafael Levi, and Allen I. Selverston. "Models Wagging the Dog: Are Circuits Constructed with Disparate Parameters?" Neural Computation 19, no. 8 (August 2007): 1985–2003. http://dx.doi.org/10.1162/neco.2007.19.8.1985.

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In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken.

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36

Kim, Min Soo, Da-Hyun Kim, Hyun Kyoung Kang, Myung Geun Kook, Soon Won Choi, and Kyung-Sun Kang. "Modeling of Hypoxic Brain Injury through 3D Human Neural Organoids." Cells 10, no. 2 (January 25, 2021): 234. http://dx.doi.org/10.3390/cells10020234.

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Brain organoids have emerged as a novel model system for neural development, neurodegenerative diseases, and human-based drug screening. However, the heterogeneous nature and immature neuronal development of brain organoids generated from pluripotent stem cells pose challenges. Moreover, there are no previous reports of a three-dimensional (3D) hypoxic brain injury model generated from neural stem cells. Here, we generated self-organized 3D human neural organoids from adult dermal fibroblast-derived neural stem cells. Radial glial cells in these human neural organoids exhibited characteristics of the human cerebral cortex trend, including an inner (ventricular zone) and an outer layer (early and late cortical plate zones). These data suggest that neural organoids reflect the distinctive radial organization of the human cerebral cortex and allow for the study of neuronal proliferation and maturation. To utilize this 3D model, we subjected our neural organoids to hypoxic injury. We investigated neuronal damage and regeneration after hypoxic injury and reoxygenation. Interestingly, after hypoxic injury, reoxygenation restored neuronal cell proliferation but not neuronal maturation. This study suggests that human neural organoids generated from neural stem cells provide new opportunities for the development of drug screening platforms and personalized modeling of neurodegenerative diseases, including hypoxic brain injury.

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37

Kaneko, N., V. Herranz-Pérez, T. Otsuka, H. Sano, N. Ohno, T. Omata, H. B. Nguyen, et al. "New neurons use Slit-Robo signaling to migrate through the glial meshwork and approach a lesion for functional regeneration." Science Advances 4, no. 12 (December 2018): eaav0618. http://dx.doi.org/10.1126/sciadv.aav0618.

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After brain injury, neural stem cell–derived neuronal precursors (neuroblasts) in the ventricular-subventricular zone migrate toward the lesion. However, the ability of the mammalian brain to regenerate neuronal circuits for functional recovery is quite limited. Here, using a mouse model for ischemic stroke, we show that neuroblast migration is restricted by reactive astrocytes in and around the lesion. To migrate, the neuroblasts use Slit1-Robo2 signaling to disrupt the actin cytoskeleton in reactive astrocytes at the site of contact. Slit1-overexpressing neuroblasts transplanted into the poststroke brain migrated closer to the lesion than did control neuroblasts. These neuroblasts matured into striatal neurons and efficiently regenerated neuronal circuits, resulting in functional recovery in the poststroke mice. These results suggest that the positioning of new neurons will be critical for functional neuronal regeneration in stem/progenitor cell–based therapies for brain injury.

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38

Wilton, Daniel K., Lasse Dissing-Olesen, and Beth Stevens. "Neuron-Glia Signaling in Synapse Elimination." Annual Review of Neuroscience 42, no. 1 (July 8, 2019): 107–27. http://dx.doi.org/10.1146/annurev-neuro-070918-050306.

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Maturation of neuronal circuits requires selective elimination of synaptic connections. Although neuron-intrinsic mechanisms are important in this process, it is increasingly recognized that glial cells also play a critical role. Without proper functioning of these cells, the number, morphology, and function of synaptic contacts are profoundly altered, resulting in abnormal connectivity and behavioral abnormalities. In addition to their role in synaptic refinement, glial cells have also been implicated in pathological synapse loss and dysfunction following injury or nervous system degeneration in adults. Although mechanisms regulating glia-mediated synaptic elimination are still being uncovered, it is clear this complex process involves many cues that promote and inhibit the removal of specific synaptic connections. Gaining a greater understanding of these signals and the contribution of different cell types will not only provide insight into this critical biological event but also be instrumental in advancing knowledge of brain development and neural disease.

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Jie, Shao, Wang Li, Zhao WeiSong, Zhong YaQin, and Reza Malekian. "Numerical Analysis of Modeling Based on Improved Elman Neural Network." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/271593.

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A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance.

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Hayati, Mohsen, Abbas Rezaei, and Majid Seifi. "CNT-MOSFET modeling based on artificial neural network: Application to simulation of nanoscale circuits." Solid-State Electronics 54, no. 1 (January 2010): 52–57. http://dx.doi.org/10.1016/j.sse.2009.09.027.

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Cao, Yi, Ihsan Erdin, and Qi-Jun Zhang. "Transient Behavioral Modeling of Nonlinear I/O Drivers Combining Neural Networks and Equivalent Circuits." IEEE Microwave and Wireless Components Letters 20, no. 12 (December 2010): 645–47. http://dx.doi.org/10.1109/lmwc.2010.2080670.

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Sharma, Hitaish, and Qi-Jun Zhang. "Automated time domain modeling of linear and nonlinear microwave circuits using recurrent neural networks." International Journal of RF and Microwave Computer-Aided Engineering 18, no. 3 (2008): 195–208. http://dx.doi.org/10.1002/mmce.20276.

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Wong-Lin, KongFatt, Da-Hui Wang, and Alok Joshi. "Multiscale modeling and analytical methods in neuroscience: Molecules, neural circuits, cognition and brain disorders." Journal of Neuroscience Methods 359 (July 2021): 109225. http://dx.doi.org/10.1016/j.jneumeth.2021.109225.

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Esser, Steve K., Sean L. Hill, and Giulio Tononi. "Modeling the Effects of Transcranial Magnetic Stimulation on Cortical Circuits." Journal of Neurophysiology 94, no. 1 (July 2005): 622–39. http://dx.doi.org/10.1152/jn.01230.2004.

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Transcranial magnetic stimulation (TMS) is commonly used to activate or inactivate specific cortical areas in a noninvasive manner. Because of technical constraints, the precise effects of TMS on cortical circuits are difficult to assess experimentally. Here, this issue is investigated by constructing a detailed model of a portion of the thalamocortical system and examining the effects of the simulated delivery of a TMS pulse. The model, which incorporates a large number of physiological and anatomical constraints, includes 33,000 spiking neurons arranged in a 3-layered motor cortex and over 5 million intra- and interlayer synaptic connections. The model was validated by reproducing several results from the experimental literature. These include the frequency, timing, dose response, and pharmacological modulation of epidurally recorded responses to TMS (the so-called I-waves), as well as paired-pulse response curves consistent with data from several experimental studies. The modeled responses to simulated TMS pulses in different experimental paradigms provide a detailed, self-consistent account of the neural and synaptic activities evoked by TMS within prototypical cortical circuits.

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Mareschal, Denis, and Thomas R. Shultz. "From neural constructivism to children's cognitive development: Bridging the gap." Behavioral and Brain Sciences 20, no. 4 (December 1997): 571–72. http://dx.doi.org/10.1017/s0140525x97391584.

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Missing from Quartz & Sejnowski's (Q&S's) unique and valuable effort to relate cognitive development to neural constructivism is an examination of the global emergent properties of adding new neural circuits. Such emergent properties can be studied with computational models. Modeling with generative connectionist networks shows that synaptogenic mechanisms can account for progressive increases in children's representational power.

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Nasser, Yasmin, Ester Fernandez, Catherine M. Keenan, Winnie Ho, Lorraine D. Oland, Lee Anne Tibbles, Michael Schemann, Wallace K. MacNaughton, Anne Rühl, and Keith A. Sharkey. "Role of enteric glia in intestinal physiology: effects of the gliotoxin fluorocitrate on motor and secretory function." American Journal of Physiology-Gastrointestinal and Liver Physiology 291, no. 5 (November 2006): G912—G927. http://dx.doi.org/10.1152/ajpgi.00067.2006.

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The role of enteric glia in gastrointestinal physiology remains largely unexplored. We examined the actions of the gliotoxin fluorocitrate (FC) on intestinal motility, secretion, and inflammation after assessing its efficacy and specificity in vitro. FC (100 μM) caused a significant decrease in the phosphorylation of the glucose analog 2-[ N-(7-nitrobenz-2-oxa-1,3-diaz-4-yl)amino]-2-deoxyglucose in enteric glial cultures and a reduction in glial uptake of the fluorescent dipeptide Ala-Lys-7-amino-4-methylcoumarin-3-acetic acid in both the ileum and colon. Dipeptide uptake by resident murine macrophages or guinea pig myenteric neurons was unaffected by FC. Incubation of isolated guinea pig ileal segments with FC caused a specific and significant increase in glial expression of the phosphorylated form of ERK-1/2. Disruption of enteric glial function with FC in mice reduced small intestinal motility in vitro, including a significant decrease in basal tone and the amplitude of contractility in response to electrical field stimulation. Mice treated with 10 or 20 μmol/kg FC twice daily for 7 days demonstrated a concentration-dependent decrease in small intestinal transit. In contrast, no changes in colonic transit or ion transport in vitro were observed. There were no changes in glial or neuronal morphology, any signs of inflammation in the FC-treated mice, or any change in the number of myenteric nitric oxide synthase-expressing neurons. We conclude that FC treatment causes enteric glial dysfunction, without causing intestinal inflammation. Our data suggest that enteric glia are involved in the modulation of enteric neural circuits underlying the regulation of intestinal motility.

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Hong, J. S., B. Z. Wang, B. J. Hu, and Y. W. Liu. "Neural network with knowledge-based neurons for the modeling of crossover discontinuities in stripline circuits." Microwave and Optical Technology Letters 34, no. 2 (June 19, 2002): 107–9. http://dx.doi.org/10.1002/mop.10387.

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Sakayori, Nobuyuki, Ryuichi Kimura, and Noriko Osumi. "Impact of Lipid Nutrition on Neural Stem/Progenitor Cells." Stem Cells International 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/973508.

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The neural system originates from neural stem/progenitor cells (NSPCs). Embryonic NSPCs first proliferate to increase their numbers and then produce neurons and glial cells that compose the complex neural circuits in the brain. New neurons are continually produced even after birth from adult NSPCs in the inner wall of the lateral ventricle and in the hippocampal dentate gyrus. These adult-born neurons are involved in various brain functions, including olfaction-related functions, learning and memory, pattern separation, and mood control. NSPCs are regulated by various intrinsic and extrinsic factors. Diet is one of such important extrinsic factors. Of dietary nutrients, lipids are important because they constitute the cell membrane, are a source of energy, and function as signaling molecules. Metabolites of some lipids can be strong lipid mediators that also regulate various biological activities. Recent findings have revealed that lipids are important regulators of both embryonic and adult NSPCs. We and other groups have shown that lipid signals including fat, fatty acids, their metabolites and intracellular carriers, cholesterol, and vitamins affect proliferation and differentiation of embryonic and adult NSPCs. A better understanding of the NSPCs regulation by lipids may provide important insight into the neural development and brain function.

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Bartolozzi, Chiara, and Giacomo Indiveri. "Synaptic Dynamics in Analog VLSI." Neural Computation 19, no. 10 (October 2007): 2581–603. http://dx.doi.org/10.1162/neco.2007.19.10.2581.

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Synapses are crucial elements for computation and information transfer in both real and artificial neural systems. Recent experimental findings and theoretical models of pulse-based neural networks suggest that synaptic dynamics can play a crucial role for learning neural codes and encoding spatiotemporal spike patterns. Within the context of hardware implementations of pulse-based neural networks, several analog VLSI circuits modeling synaptic functionality have been proposed. We present an overview of previously proposed circuits and describe a novel analog VLSI synaptic circuit suitable for integration in large VLSI spike-based neural systems. The circuit proposed is based on a computational model that fits the real postsynaptic currents with exponentials. We present experimental data showing how the circuit exhibits realistic dynamics and show how it can be connected to additional modules for implementing a wide range of synaptic properties.

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Rao, Hrishikesh M., J. Patrick Mayo, and Marc A. Sommer. "Circuits for presaccadic visual remapping." Journal of Neurophysiology 116, no. 6 (December 1, 2016): 2624–36. http://dx.doi.org/10.1152/jn.00182.2016.

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Saccadic eye movements rapidly displace the image of the world that is projected onto the retinas. In anticipation of each saccade, many neurons in the visual system shift their receptive fields. This presaccadic change in visual sensitivity, known as remapping, was first documented in the parietal cortex and has been studied in many other brain regions. Remapping requires information about upcoming saccades via corollary discharge. Analyses of neurons in a corollary discharge pathway that targets the frontal eye field (FEF) suggest that remapping may be assembled in the FEF's local microcircuitry. Complementary data from reversible inactivation, neural recording, and modeling studies provide evidence that remapping contributes to transsaccadic continuity of action and perception. Multiple forms of remapping have been reported in the FEF and other brain areas, however, and questions remain about the reasons for these differences. In this review of recent progress, we identify three hypotheses that may help to guide further investigations into the structure and function of circuits for remapping.

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