Relevant bibliographies by topics / Modeling Neural Glial Circuits

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Modeling Neural Glial Circuits'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Modeling Neural Glial Circuits"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Modeling Neural Glial Circuits"

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Nadkarni, Suhita. "Dynamics of Dressed Neurons: Modeling the Neural-Glial Circuit and Exploring its Normal and Pathological Implications." Ohio : Ohio University, 2005. http://www.ohiolink.edu/etd/view.cgi?ohiou1125689320.

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Rayas-Sanchez, Jose Ernesto. "Neural space mapping methods for modeling and design of microwave circuits /." *McMaster only, 2001.

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Patel, Girish N. "A neuromorphic architecture for modeling intersegmental coordination." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/13528.

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Han, Seung Soo. "Modeling and optimization of plasma-enhanced chemical vapor deposition using neural networks and genetic algorithms." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/14904.

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Kumar, Sharad Kumar. "Analysis of Machine Learning Modeling Attacks on Ring Oscillator based Hardware Security." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1541759752027838.

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Bol, Kieran G. "Redundant Input Cancellation by a Bursting Neural Network." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20061.

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One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs.
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Buhry, Laure. "Estimation de paramètres de modèles de neurones biologiques sur une plate-forme de SNN (Spiking Neural Network) implantés "insilico"." Thesis, Bordeaux 1, 2010. http://www.theses.fr/2010BOR14057/document.

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Ces travaux de thèse, réalisés dans une équipe concevant des circuits analogiques neuromimétiques suivant le modèle d’Hodgkin-Huxley, concernent la modélisation de neurones biologiques, plus précisément, l’estimation des paramètres de modèles de neurones. Une première partie de ce manuscrit s’attache à faire le lien entre la modélisation neuronale et l’optimisation. L’accent est mis sur le modèle d’Hodgkin- Huxley pour lequel il existait déjà une méthode d’extraction des paramètres associée à une technique de mesures électrophysiologiques (le voltage-clamp) mais dont les approximations successives rendaient impossible la détermination précise de certains paramètres. Nous proposons dans une seconde partie une méthode alternative d’estimation des paramètres du modèle d’Hodgkin-Huxley s’appuyant sur l’algorithme d’évolution différentielle et qui pallie les limitations de la méthode classique. Cette alternative permet d’estimer conjointement tous les paramètres d’un même canal ionique. Le troisième chapitre est divisé en trois sections. Dans les deux premières, nous appliquons notre nouvelle technique à l’estimation des paramètres du même modèle à partir de données biologiques, puis développons un protocole automatisé de réglage de circuits neuromimétiques, canal ionique par canal ionique. La troisième section présente une méthode d’estimation des paramètres à partir d’enregistrements de la tension de membrane d’un neurone, données dont l’acquisition est plus aisée que celle des courants ioniques. Le quatrième et dernier chapitre, quant à lui, est une ouverture vers l’utilisation de petits réseaux d’une centaine de neurones électroniques : nous réalisons une étude logicielle de l’influence des propriétés intrinsèques de la cellule sur le comportement global du réseau dans le cadre des oscillations gamma
These works, which were conducted in a research group designing neuromimetic integrated circuits based on the Hodgkin-Huxley model, deal with the parameter estimation of biological neuron models. The first part of the manuscript tries to bridge the gap between neuron modeling and optimization. We focus our interest on the Hodgkin-Huxley model because it is used in the group. There already existed an estimation method associated to the voltage-clamp technique. Nevertheless, this classical estimation method does not allow to extract precisely all parameters of the model, so in the second part, we propose an alternative method to jointly estimate all parameters of one ionic channel avoiding the usual approximations. This method is based on the differential evolution algorithm. The third chaper is divided into three sections : the first two sections present the application of our new estimation method to two different problems, model fitting from biological data and development of an automated tuning of neuromimetic chips. In the third section, we propose an estimation technique using only membrane voltage recordings – easier to mesure than ionic currents. Finally, the fourth and last chapter is a theoretical study preparing the implementation of small neural networks on neuromimetic chips. More specifically, we try to study the influence of cellular intrinsic properties on the global behavior of a neural network in the context of gamma oscillations
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Nasser, Yehya. "An Efficient Computer-Aided Design Methodology for FPGA&ASIC High-Level Power Estimation Based on Machine Learning." Thesis, Rennes, INSA, 2019. http://www.theses.fr/2019ISAR0014.

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Aujourd’hui, des systèmes numériques avancés sont nécessaires pour mettre en œuvre des fonctionnalités complexes. Cette complexité impose au concepteur de respecter différentes contraintes de conception telles que la performance, la surface, la consommation électrique et le délai de mise sur le marché. Pour effectuer une conception efficace, les concepteurs doivent rapidement évaluer les différentes architectures possibles. Dans cette thèse, nous nous concentrons sur l’évaluation de la consommation d’énergie afin de fournir une méthode d’estimation de puissance rapide, précise et flexible. Nous présentons NeuPow qui est une méthode s’appliquant aux FPGA et ASIC. Cette approche système est basée sur des techniques d’apprentissage statistique.Notamment, nous exploitons les réseaux neuronaux pour aider les concepteurs à explorer la consommation d’énergie dynamique. NeuPow s’appuie sur la propagation des signaux à travers des modèles neuronaux connectés pour prédire la consommation d’énergie d’un système composite à haut niveau d’abstraction. La méthodologie permet de prendre en compte la fréquence de fonctionnement et les différentes technologies de circuits (ASIC et FPGA). Les résultats montrent une très bonne précision d’estimation avec moins de 10% d’erreur relative indépendamment de la technologie et de la taille du circuit. NeuPow permet d’obtenir une productivité de conception élevée. Les temps de simulation obtenus sont significativement améliorés par rapport à ceux obtenus avec les outils de conception conventionnels
Nowadays, advanced digital systems are required to address complex functionnalities in a very wide range of applications. Systems complexity imposes designers to respect different design constraints such as the performance, area, power consumption and the time-to-market. The best design choice is the one that respects all of these constraints. To select an efficient design, designers need to quickly assess the possible architectures. In this thesis, we focus on facilitating the evaluation of the power consumption for both signal processing and hardware design engineers, so that it is possible to maintain fast, accurate and flexible power estimation. We present NeuPowas a system-level FPGA/ASIC power estimation method based on machine learning. We exploit neural networks to aid the designers in exploring the dynamic power consumption of possible architectural solutions. NeuPow relies on propagating the signals throughout connected neural models to predict the power consumption of a composite system at high-level of abstractions. We also provide an upgraded version that is frequency aware estimation. To prove the effectiveness of the proposed methodology, assessments such as technology and scalability studies have been conducted on ASIC and FPGA. Results show very good estimationaccuracy with less than 10% of relative error independently from the technology and the design size. NeuPow maintains high design productivity, where the simulation time obtained is significantly improved compared to those obtained with conventional design tools
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Syue, Jhih-Jie, and 薛智杰. "On High-Level Power Modeling Using Recurrent Neural Networks for Sequential Circuits." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/02447554794088269893.

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碩士
國立中央大學
電機工程研究所
92
Nowadays, the increasing in chip density and operating frequency have also made power dissipation a critical problem during IC design. In order to avoid costly redesign steps in IC design process, accurately estimate power dissipation on high-level of abstract is very important and necessary. In sequential circuits, power estimation is considerably more difficult than combinational circuits, because the power dissipation also depend on internal states and strong temporal correlations often exist in the input sequences, which are also hard to handle their effects in traditional approaches. For this reason, we propose a novel power model for CMOS sequential circuits by using recurrent neural networks (RNNs) to learn the relationship between input/output signal statistics and the corresponding power dissipation, because the RNNs has the internal feedback to handle temporal correlations. Unlike traditional approach of power estimation (for example: building lookup table), not only our neural power model automatically consider the non-linear characteristic of power distributions and the temporal correlation of the input sequences but also has very low complexity. More importantly, using our neural power model for power estimation does not require any transistor-level or gate-level description of the circuits. So it is suitable for IP protection. The experiment results have shown that the estimations are still accurate even for short sequence with only 50 pattern-pairs. It shows that our power model can be used in various applications.
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Books on the topic "Modeling Neural Glial Circuits"

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Ahmari, Susanne E. Targeted Circuit Manipulations in the Modeling of OCD. Edited by Christopher Pittenger. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228163.003.0034.

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Work in animal models has great potential to shed light on the neural circuit perturbations that lead to OCD-related behaviors. Circuit-specific manipulations allow testing of the causal role of the brain network abnormalities observed in clinical imaging studies, with a precision that is not possible in investigations in humans. In recent years, circuit-specific manipulations in animals using a range of technologies have confirmed that abnormalities in the cortico-striatal circuitry can produce repetitive behaviors, such as excessive grooming. This chapter summarizes these advances. Refining our understanding of the contribution of particular neural circuits to OCD-relevant behaviors can inform the development of anatomically targeted treatments, such as deep brain stimulation.
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Kaplan, David M. Neural Computation, Multiple Realizability, and the Prospects for Mechanistic Explanation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199685509.003.0008.

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There is an ongoing philosophical and scientific debate concerning the nature of computational explanation in the neurosciences. Recently, some have cited modeling work involving so-called canonical neural computations—standard computational modules that apply the same fundamental operations across multiple brain areas—as evidence that computational neuroscientists sometimes employ a distinctive explanatory scheme from that of mechanistic explanation. Because these neural computations can rely on diverse circuits and mechanisms, modeling the underlying mechanisms is supposed to be of limited explanatory value. I argue that these conclusions about computational explanations in neuroscience are mistaken, and rest upon a number of confusions about the proper scope of mechanistic explanation and the relevance of multiple realizability considerations. Once these confusions are resolved, the mechanistic character of computational explanations can once again be appreciated.
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Book chapters on the topic "Modeling Neural Glial Circuits"

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Postnov, D. E., N. A. Brazhe, and O. V. Sosnovtseva. "Functional Modeling of Neural-Glial Interaction." In Biosimulation in Biomedical Research, Health Care and Drug Development, 133–51. Vienna: Springer Vienna, 2011. http://dx.doi.org/10.1007/978-3-7091-0418-7_6.

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Arle, Jeffrey E., Longzhi Mei, and Kristen W. Carlson. "Robustness in Neural Circuits." In Brain and Human Body Modeling 2020, 213–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45623-8_12.

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AbstractComplex systems are found everywhere – from scheduling to traffic, food to climate, economics to ecology, the brain, and the universe. Complex systems typically have many elements, many modes of interconnectedness of those elements, and often exhibit sensitivity to initial conditions. Complex systems by their nature are generally unpredictable and can be highly unstable.
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Chakradhar, Srimat T., Vishwani D. Agrawal, and Michael L. Bushneil. "Neural Modeling for Digital Circuits." In Neural Models and Algorithms for Digital Testing, 33–50. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3958-2_5.

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van Dongen, Marijn, and Wouter Serdijn. "Modeling the Activation of Neural Cells." In Analog Circuits and Signal Processing, 11–24. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28131-5_2.

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Psiha, Maria, and Panayiotis Vlamos. "Modeling Neural Circuits in Parkinson’s Disease." In Advances in Experimental Medicine and Biology, 139–47. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08927-0_15.

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Aguirre, Carlos, Doris Campos, Pedro Pascual, and Eduardo Serrano. "Effiects of different connectivity patterns in a model of cortical circuits." In Computational Methods in Neural Modeling, 78–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44868-3_11.

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Sterling, Peter, Ethan Cohen, Robert G. Smith, and Yoshihiko Tsukamoto. "Retinal circuits for daylight: why ballplayers don’t wear shades." In Analysis and Modeling of Neural Systems, 141–62. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-4010-6_15.

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Nagy, Frédéric, Thierry Bal, and Patrick Cardi. "Dynamic Re-wiring of CPG Circuits in a Simple Nervous System." In Analysis and Modeling of Neural Systems, 339–51. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-4010-6_35.

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Rybak, Ilya A., Dmitry G. Ivashko, Boris I. Prilutsky, M. Anthony Lewis, and John K. Chapin. "Modeling Neural Control of Locomotion: Integration of Reflex Circuits with CPG." In Artificial Neural Networks — ICANN 2002, 99–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_17.

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Arle, J. E., and D. O. Kim. "A Modeling Study of Single Neurons and Neural Circuits of the Ventral and Dorsal Cochlear Nucleus." In Analysis and Modeling of Neural Systems, 289–95. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-4010-6_29.

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Conference papers on the topic "Modeling Neural Glial Circuits"

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Joshi, Jonathan, Alice C. Parker, and Ko-Chung Tseng. "An in-silico glial microdomain to invoke excitability in cortical neural networks." In 2011 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2011. http://dx.doi.org/10.1109/iscas.2011.5937657.

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Hasani, Ramin M., Dieter Haerle, Christian F. Baumgartner, Alessio R. Lomuscio, and Radu Grosu. "Compositional neural-network modeling of complex analog circuits." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966126.

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Xiong, Jie, Alan S. Yang, Maxim Raginsky, and Elyse Rosenbaum. "Neural Networks for Transient Modeling of Circuits : Invited Paper." In 2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD). IEEE, 2021. http://dx.doi.org/10.1109/mlcad52597.2021.9531153.

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Min Fang, Jin He, Jian Zhang, Lining Zhang, Mansun Chan, and Chenyue Ma. "Modeling nanoscale MOSFETs by a neural network approach." In 2008 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC). IEEE, 2008. http://dx.doi.org/10.1109/edssc.2008.4760660.

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Pochmara, J. "Modeling Power Amplifier Nonlinearities with Artifical Neural Network." In 2007 14th International Conference on Mixed Design of Integrated Circuits and Systems. IEEE, 2007. http://dx.doi.org/10.1109/mixdes.2007.4286202.

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Hasani, Ramin M., Dieter Haerle, and Radu Grosu. "Efficient modeling of complex Analog integrated circuits using neural networks." In 2016 12th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME). IEEE, 2016. http://dx.doi.org/10.1109/prime.2016.7519486.

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Lei Zhang, Kui Bo, and Q. J. Zhang. "Application of neural networks for linear/nonlinear microwave modeling." In 2007 Joint 50th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) and the IEEE Northeast Workshop on Circuits and Systems (NEWCAS 2007). IEEE, 2007. http://dx.doi.org/10.1109/mwscas.2007.4488602.

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Yin Shirong. "Application of BP neural network in analog circuits diagnosis." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5623266.

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Ntoune Ntoune, Roger Sandrin, Mohammed Bahoura, and Chan-Wang Park. "FPGA-implementation of pipelined neural network for power amplifier modeling." In 2012 IEEE 10th International New Circuits and Systems Conference (NEWCAS). IEEE, 2012. http://dx.doi.org/10.1109/newcas.2012.6328968.

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Dooley, John, Bill O'Brien, and Thomas Brazil. "Prior Knowledge Input Neural Network for Microwave Power Amplifier Modeling." In 2006 International Workshop on Integrated Nonlinear Microwave and Millimeter-Wave Circuits. IEEE, 2006. http://dx.doi.org/10.1109/inmmic.2006.283535.

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