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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

HENTSCHEL, H. G. E., and ALAN FINE. "COMPLEX BIOLOGICAL GROWTH: NEURONAL MORPHOGENESIS." Fractals 03, no. 04 (December 1995): 905–14. http://dx.doi.org/10.1142/s0218348x95000795.

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It has been observed that neurons and certain other cell types have dendritic arbors which appear to be self-similar. This biological pattern formation is consistent with the concept that shape is controlled by the local submembrane concentration of a morphogen believed to be the calcium ion. Such diffusion-controlled growth of the cellular cytoskeleton has recently been shown to lead to dendritic instabilities. Linear stability analysis suggests that dendritic arboring will be greatly enhanced in the presence of excitable membranes provided the cell is large enough. Computer simulations of this class of models have established many similarities to the growth and form of real neurons including a dendritic morphology reminicent of neuronal arbors and the existence of growth cones observed during real neuronal development; bioelectrical activity; effects of changes in membrane conductivity on morphology; galvanotropism; and chemotropism.

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2

Adams, Robert D., Rebecca K. Willits, and Amy B. Harkins. "Computational modeling of neurons: intensity-duration relationship of extracellular electrical stimulation for changes in intracellular calcium." Journal of Neurophysiology 115, no. 1 (January 1, 2016): 602–16. http://dx.doi.org/10.1152/jn.00571.2015.

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In many instances of extensive nerve damage, the injured nerve never adequately heals, leaving lack of nerve function. Electrical stimulation (ES) has been shown to increase the rate and orient the direction of neurite growth, and is a promising therapy. However, the mechanism in which ES affects neuronal growth is not understood, making it difficult to compare existing ES protocols or to design and optimize new protocols. We hypothesize that ES acts by elevating intracellular calcium concentration ([Ca2+]i) via opening voltage-dependent Ca2+ channels (VDCCs). In this work, we have created a computer model to estimate the ES Ca2+ relationship. Using COMSOL Multiphysics, we modeled a small dorsal root ganglion (DRG) neuron that includes one Na+ channel, two K+ channels, and three VDCCs to estimate [Ca2+]i in the soma and growth cone. As expected, the results show that an ES that generates action potentials (APs) can efficiently raise the [Ca2+]i of neurons. More interestingly, our simulation results show that sub-AP ES can efficiently raise neuronal [Ca2+]i and that specific high-voltage ES can preferentially raise [Ca2+]i in the growth cone. The intensities and durations of ES on modeled growth cone calcium rise are consistent with directionality and orientation of growth cones experimentally shown by others. Finally, this model provides a basis to design experimental ES pulse parameters, including duration, intensity, pulse-train frequency, and pulse-train duration to efficiently raise [Ca2+]i in neuronal somas or growth cones.

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3

Lei, Yuchen, and Yinghui Li. "Construction and Simulation of the Market Risk Early-Warning Model Based on Deep Learning Methods." Scientific Programming 2022 (March 24, 2022): 1–8. http://dx.doi.org/10.1155/2022/4733220.

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To address the problem of low efficiency of existing forecasting models for market risk warning, a market risk early-warning model based on improved LSTM is suggested utilizing the whale optimization algorithm (WOA) to optimize the number of hidden layer neurons and time step parameters of long short-term memory. The proposed market risk early-warning model is validated by using 40 real estate companies as the research subjects and 20 relevant variables such as gross operating income, net profit asset growth rate, and total asset growth rate as indicators. The results demonstrate that the proposed model’s prediction accuracy for market risk is greater than 96% and that when compared to the standard CNN and LSTM models, the suggested model’s prediction accuracy for corporate finance from 2012 to 2019 is increased by 14% and 12%, respectively, and the prediction accuracy for corporate finance in 2020 is improved by 22% and 7%, respectively, which has certain practical application value and superiority.

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4

Cremonesi, Francesco, Georg Hager, Gerhard Wellein, and Felix Schürmann. "Analytic performance modeling and analysis of detailed neuron simulations." International Journal of High Performance Computing Applications 34, no. 4 (April 3, 2020): 428–49. http://dx.doi.org/10.1177/1094342020912528.

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Big science initiatives are trying to reconstruct and model the brain by attempting to simulate brain tissue at larger scales and with increasingly more biological detail than previously thought possible. The exponential growth of parallel computer performance has been supporting these developments, and at the same time maintainers of neuroscientific simulation code have strived to optimally and efficiently exploit new hardware features. Current state-of-the-art software for the simulation of biological networks has so far been developed using performance engineering practices, but a thorough analysis and modeling of the computational and performance characteristics, especially in the case of morphologically detailed neuron simulations, is lacking. Other computational sciences have successfully used analytic performance engineering, which is based on “white-box,” that is, first-principles performance models, to gain insight on the computational properties of simulation kernels, aid developers in performance optimizations and eventually drive codesign efforts, but to our knowledge a model-based performance analysis of neuron simulations has not yet been conducted. We present a detailed study of the shared-memory performance of morphologically detailed neuron simulations based on the Execution-Cache-Memory performance model. We demonstrate that this model can deliver accurate predictions of the runtime of almost all the kernels that constitute the neuron models under investigation. The gained insight is used to identify the main governing mechanisms underlying performance bottlenecks in the simulation. The implications of this analysis on the optimization of neural simulation software and eventually codesign of future hardware architectures are discussed. In this sense, our work represents a valuable conceptual and quantitative contribution to understanding the performance properties of biological networks simulations.

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5

WANG, NING, MENG JOO ER, XIAN-YAO MENG, and XIANG LI. "AN ONLINE SELF-ORGANIZING SCHEME FOR PARSIMONIOUS AND ACCURATE FUZZY NEURAL NETWORKS." International Journal of Neural Systems 20, no. 05 (October 2010): 389–403. http://dx.doi.org/10.1142/s0129065710002486.

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In this paper, an online self-organizing scheme for Parsimonious and Accurate Fuzzy Neural Networks (PAFNN), and a novel structure learning algorithm incorporating a pruning strategy into novel growth criteria are presented. The proposed growing procedure without pruning not only simplifies the online learning process but also facilitates the formation of a more parsimonious fuzzy neural network. By virtue of optimal parameter identification, high performance and accuracy can be obtained. The learning phase of the PAFNN involves two stages, namely structure learning and parameter learning. In structure learning, the PAFNN starts with no hidden neurons and parsimoniously generates new hidden units according to the proposed growth criteria as learning proceeds. In parameter learning, parameters in premises and consequents of fuzzy rules, regardless of whether they are newly created or already in existence, are updated by the extended Kalman filter (EKF) method and the linear least squares (LLS) algorithm, respectively. This parameter adjustment paradigm enables optimization of parameters in each learning epoch so that high performance can be achieved. The effectiveness and superiority of the PAFNN paradigm are demonstrated by comparing the proposed method with state-of-the-art methods. Simulation results on various benchmark problems in the areas of function approximation, nonlinear dynamic system identification and chaotic time-series prediction demonstrate that the proposed PAFNN algorithm can achieve more parsimonious network structure, higher approximation accuracy and better generalization simultaneously.

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6

Garcia, Mikael, Cécile Leduc, Matthieu Lagardère, Amélie Argento, Jean-Baptiste Sibarita, and Olivier Thoumine. "Two-tiered coupling between flowing actin and immobilized N-cadherin/catenin complexes in neuronal growth cones." Proceedings of the National Academy of Sciences 112, no. 22 (May 18, 2015): 6997–7002. http://dx.doi.org/10.1073/pnas.1423455112.

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Neuronal growth cones move forward by dynamically connecting actin-based motility to substrate adhesion, but the mechanisms at the individual molecular level remain unclear. We cultured primary neurons on N-cadherin–coated micropatterned substrates, and imaged adhesion and cytoskeletal proteins at the ventral surface of growth cones using single particle tracking combined to photoactivated localization microscopy (sptPALM). We demonstrate transient interactions in the second time scale between flowing actin filaments and immobilized N-cadherin/catenin complexes, translating into a local reduction of the actin retrograde flow. Normal actin flow on micropatterns was rescued by expression of a dominant negative N-cadherin construct competing for the coupling between actin and endogenous N-cadherin. Fluorescence recovery after photobleaching (FRAP) experiments confirmed the differential kinetics of actin and N-cadherin, and further revealed a 20% actin population confined at N-cadherin micropatterns, contributing to local actin accumulation. Computer simulations with relevant kinetic parameters modeled N-cadherin and actin turnover well, validating this mechanism. Such a combination of short- and long-lived interactions between the motile actin network and spatially restricted adhesive complexes represents a two-tiered clutch mechanism likely to sustain dynamic environment sensing and provide the force necessary for growth cone migration.

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7

Dai, Chuankai, Xiaoming Liu, Rongyu Tang, Jiping He, and Tatsuo Arai. "A Review on Microfluidic Platforms Applied to Nerve Regeneration." Applied Sciences 12, no. 7 (March 30, 2022): 3534. http://dx.doi.org/10.3390/app12073534.

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In recent decades, microfluidics have significantly advanced nerve regeneration research. Microfluidic devices can provide an accurate simulation of in vivo microenvironment for different research purposes such as analyzing myelin growth inhibitory factors, screening drugs, assessing nerve growth factors, and exploring mechanisms of neural injury and regeneration. The microfluidic platform offers technical supports for nerve regeneration that enable precise spatio-temporal control of cells, such as neuron isolation, single-cell manipulation, neural patterning, and axon guidance. In this paper, we review the development and recent advances of microfluidic platforms for nerve regeneration research.

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8

Waldherr, L., V. Handl, T. Abrahamsson, T. Arbring Sjöström, M. Seitanidou, S. Erschen, S. Honeder, et al. "P10.03.B Insights into the development of tunable brain implants for local chemotherapy." Neuro-Oncology 24, Supplement_2 (September 1, 2022): ii48—ii49. http://dx.doi.org/10.1093/neuonc/noac174.168.

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Abstract Background Glioblastomas (GBMs) remain an unmastered medical challenge. Poor delivery and systemic toxicity of many chemotherapeutic agents limit their therapeutic success in GBM treatment. Bioelectronic implants for local chemo drug delivery can optimize drug concentrations at the tumor site, duration of treatment and tumor suppression, while systemic effects remain at an acceptable low level. We present miniature bioelectronic devices for drug delivery able to administer chemotherapeutics via electric control with high spatiotemporal precision.1 The drug delivery is based on the electro migration of drug molecules in an ion selective matrix towards a target of choice. These bioelectronic devices, called chemotherapeutic ion pumps (chemoIPs), can be used for triggered drug release of chemotherapeutics that are usually shielded by the blood brain barrier. Material and Methods The performance of chemoIPs is studied in different brain tumor models with increasing complexity (cell culture and different in vivo models). With chemoIPs it is possible to constantly administer drugs with highest precision (delivery rates at pmol*min-1 precision) towards cell culture spheroids, ex ovo-grown tumors and in vivo brain tumors Results The treatment efficiency was analyzed via flow cytometry quantifying apoptosis and cell cycle arrest, as well as immune-histochemical analysis for apoptosis. ChemoIP treatment is able to trigger the disintegration of targeted tumor spheroids, and is able to inhibit the tumor growth of ex ovo-grown glioblastomas significantly. Furthermore, the proteomes of neurons and glioblastoma cells were recorded via proteomics, which showed that only GBM cells are harmed by the chemotherapeutic treatment, but not neurons. In parallel, can follow the pharmacokinetics of the chemoIP-mediated drug administration via drug quantification using mass spectrometry and compare it to computer simulations in different tumor models. Conclusion The here exemplified electrically-driven drug delivery via chemoIPs is a drug administration method that can serve as basis for further implant development, which has the potential to increase the efficacy of chemotherapy due to highly-targeted and locally-controlled drug delivery.

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9

Vidal de Caralho, Luís Alfred, Nívea de Carvalho Ferreira, and Adriana Fiszman. "A Theoretical Model for Autism." Journal of Theoretical Medicine 3, no. 4 (2001): 271–86. http://dx.doi.org/10.1080/10273660108833080.

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Autism is a mental disorder characterized by deficits in socialization, communication, and imagination. Along with the deficits, autistic children may show savant skills (“islets of ability”) of unknown origin that puzzles their families and the psychologists. Comorbidity with epilepsy and mental retardation has brought the researchers' attention to neurobiological and cognitive theories of the syndrome. The present article proposes a neurobiological model for the autism based on the fundamental biological process of neuronal competition. A neural network capable of defining neural maps—synaptic projections preserving neighborhoods between two neural tissues—simulates the process of neurodevelopment. Experiments were performed reducing the level of neural growth factor released by the neurons, leading to ill-developed maps and suggesting the cause of the aberrant neurogenesis present in autism. The computer simulations hint that brain regions responsible for the formation of higher level representations are impaired in autistic patients. The lack of this integrated representation of the world would result in the peculiar cognitive deficits of socialization, communication, and imagination and could also explain some “islets of abilities”, like excellent memory for raw data and stimuli discrimination. The neuronal model is based on plausible biological findings and on recently developed cognitive theories of autism. Close relations are established between the computational properties of the neural network model and the cognitive theory of autism denominated “weak central coherence”, bringing some insight to the understanding of the disorder.

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10

Sato, Daisuke, Gonzalo Hernández-Hernández, Collin Matsumoto, Sendoa Tajada, Claudia M. Moreno, Rose E. Dixon, Samantha O’Dwyer, et al. "A stochastic model of ion channel cluster formation in the plasma membrane." Journal of General Physiology 151, no. 9 (August 1, 2019): 1116–34. http://dx.doi.org/10.1085/jgp.201912327.

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Ion channels are often found arranged into dense clusters in the plasma membranes of excitable cells, but the mechanisms underlying the formation and maintenance of these functional aggregates are unknown. Here, we tested the hypothesis that channel clustering is the consequence of a stochastic self-assembly process and propose a model by which channel clusters are formed and regulated in size. Our hypothesis is based on statistical analyses of the size distributions of the channel clusters we measured in neurons, ventricular myocytes, arterial smooth muscle, and heterologous cells, which in all cases were described by exponential functions, indicative of a Poisson process (i.e., clusters form in a continuous, independent, and memory-less fashion). We were able to reproduce the observed cluster distributions of five different types of channels in the membrane of excitable and tsA-201 cells in simulations using a computer model in which channels are “delivered” to the membrane at randomly assigned locations. The model’s three parameters represent channel cluster nucleation, growth, and removal probabilities, the values of which were estimated based on our experimental measurements. We also determined the time course of cluster formation and membrane dwell time for CaV1.2 and TRPV4 channels expressed in tsA-201 cells to constrain our model. In addition, we elaborated a more complex version of our model that incorporated a self-regulating feedback mechanism to shape channel cluster formation. The strong inference we make from our results is that CaV1.2, CaV1.3, BK, and TRPV4 proteins are all randomly inserted into the plasma membranes of excitable cells and that they form homogeneous clusters that increase in size until they reach a steady state. Further, it appears likely that cluster size for a diverse set of membrane-bound proteins and a wide range of cell types is regulated by a common feedback mechanism.

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11

Ferber, F. "Computer Simulation of Crack Growth." Materials Science Forum 123-125 (January 1993): 495–504. http://dx.doi.org/10.4028/www.scientific.net/msf.123-125.495.

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12

BOSSCHER, HEMMO, and WOLFGANG SCHLAGER. "Computer simulation of reef growth." Sedimentology 39, no. 3 (June 1992): 503–12. http://dx.doi.org/10.1111/j.1365-3091.1992.tb02130.x.

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13

Xiao, Rong-Fu. "Computer simulation of surface growth." Journal of Crystal Growth 174, no. 1-4 (April 1997): 531–38. http://dx.doi.org/10.1016/s0022-0248(97)00052-3.

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14

Frost, Harold J., and Carl V. Thompson. "Computer simulation of grain growth." Current Opinion in Solid State and Materials Science 1, no. 3 (June 1996): 361–68. http://dx.doi.org/10.1016/s1359-0286(96)80026-x.

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15

Müller-Pfeiffer, S., and H. J. Anklam. "Computer simulation of hillock growth." Vacuum 42, no. 1-2 (January 1991): 113–16. http://dx.doi.org/10.1016/0042-207x(91)90090-6.

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16

Buanes, Trygve, Bjørn Kvamme, and Atle Svandal. "Computer simulation of hydrate growth." Journal of Crystal Growth 287, no. 2 (January 2006): 491–94. http://dx.doi.org/10.1016/j.jcrysgro.2005.11.074.

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17

Düchting, Werner. "Tumor growth simulation." Computers & Graphics 14, no. 3-4 (January 1990): 505–8. http://dx.doi.org/10.1016/0097-8493(90)90073-7.

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18

Morón, C., M. Mora, and A. Garcı́a. "Computer simulation of grain growth kinetics." Journal of Magnetism and Magnetic Materials 215-216 (June 2000): 153–55. http://dx.doi.org/10.1016/s0304-8853(00)00100-1.

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19

Rodriguez-Navarro, A., and J. M. Garcia-Ruiz. "Computer simulation of competitive crystal growth." Computer Physics Communications 121-122 (September 1999): 727. http://dx.doi.org/10.1016/s0010-4655(06)70130-8.

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20

Mattheck, C. "Computer simulation of adaptive biological growth." Journal of Biomechanics 25, no. 7 (July 1992): 780. http://dx.doi.org/10.1016/0021-9290(92)90513-z.

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21

Nagai, T., S. Ohta, and Kenji Kawasaki. "Computer Simulation of Cellular Pattern Growth." Materials Science Forum 94-96 (January 1992): 313–18. http://dx.doi.org/10.4028/www.scientific.net/msf.94-96.313.

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22

Yang, Wei, Long-Qing Chen, and Gary L. Messing. "Computer simulation of anisotropic grain growth." Materials Science and Engineering: A 195 (June 1995): 179–87. http://dx.doi.org/10.1016/0921-5093(94)06517-9.

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23

Srolovitz, D. J., G. S. Grest, and M. P. Anderson. "Computer simulation of grain growth—V. Abnormal grain growth." Acta Metallurgica 33, no. 12 (December 1985): 2233–47. http://dx.doi.org/10.1016/0001-6160(85)90185-3.

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24

Claverol, Enric T., Andrew D. Brown, and John E. Chad. "Discrete simulation of large aggregates of neurons." Neurocomputing 47, no. 1-4 (August 2002): 277–97. http://dx.doi.org/10.1016/s0925-2312(01)00629-4.

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25

McGrath, Paul, Robert Kucera, and Wayne Smith. "COMPUTER SIMULATION OF INTRODUCTORY NEUROPHYSIOLOGY." Advances in Physiology Education 27, no. 3 (September 2003): 120–29. http://dx.doi.org/10.1152/advan.00055.2002.

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A computer-assisted learning (CAL) package, NeuroLab, developed for use by first-year university students undertaking professional programs in the health area, is described and evaluated. NeuroLab is a simulation of a laboratory, in which students are able to impale neurons to measure resting membrane potentials and subsequently undertake experiments including measuring resting membrane potentials, determining threshold potentials, measuring refractory periods, and examining effects on membrane potential through altering the membrane permeability to sodium and potassium ions. Students find the package to be a worthwhile learning experience, with 81 ± 2.2% reporting the package increased their understanding of neuron function, and 78 ± 2.5% expressing a desire for more CAL packages. Exposure to the package resulted in significantly higher mean scores in a multiple-choice question test on measuring neuron membrane potentials compared with those who were not exposed (mean scores out of 4 of 2.42 and 2.02, respectively, P < 0.001).

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26

Adeeb, Samer, and Walter Herzog. "Simulation of biological growth." Computer Methods in Biomechanics and Biomedical Engineering 12, no. 6 (December 2009): 617–26. http://dx.doi.org/10.1080/10255840902802909.

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27

ABBRUZZESE, G., K. LÜCKE, and H. EICHELKRAUT. "Computer simulation of texture-controlled grain growth." Transactions of the Iron and Steel Institute of Japan 28, no. 10 (1988): 818–25. http://dx.doi.org/10.2355/isijinternational1966.28.818.

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28

Zheng Xiao-Ping, Zhang Pei-Feng, Liu Jun, He De-Yan, and Ma Jian-Tai. "Computer simulation of thin-film epitaxy growth." Acta Physica Sinica 53, no. 8 (2004): 2687. http://dx.doi.org/10.7498/aps.53.2687.

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29

Takahashi, Akiyuki, Masahiro Arita, and Masanori Kikuchi. "Computer Simulation of Irradiation Growth in Zirconium." Advanced Materials Research 33-37 (March 2008): 889–94. http://dx.doi.org/10.4028/www.scientific.net/amr.33-37.889.

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This paper describes the computer simulation of irradiation growths induced by neutron irradiations in zirconium using a combination of Molecular Dynamics (MD) and Kinetic Monte Carlo (KMC) methods. First, we performed the MD simulation of the displacement cascade on a defect cluster to study the interaction between the defect cluster and the displacement cascade. The MD simulations provide a lot of information on the amount of the defect production and the subsequent morphological change in the defect cluster. The results are used to make simple models that describe the nature of the displacement cascade overlap on the defect clusters. The models are then implemented into the KMC simulation code to extend the length- and time-scale of the simulation, which allows us to evaluate directly the defect cluster accumulations during a long-term irradiation. The irradiation growth strain resulting from the defect cluster accumulations is simply evaluated, and compared to an available experimental data. The comparison suggests that the displacement cascade overlap plays an important role on the irradiation growth, and, consequently, the KMC method with the simple models must be appropriate for the simulations of the irradiation growth.

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30

Tarasevich, Yu Yu. "Computer simulation of crystal growth from solution." Technical Physics 46, no. 5 (May 2001): 627–29. http://dx.doi.org/10.1134/1.1372959.

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31

Martin, D. G. "Computer simulation of recrystallisation and grain growth." Materials Science and Technology 10, no. 10 (October 1994): 855–61. http://dx.doi.org/10.1179/mst.1994.10.10.855.

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32

Frieboes, Hermann B., John S. Lowengrub, S. Wise, X. Zheng, Paul Macklin, Elaine L. Bearer, and Vittorio Cristini. "Computer simulation of glioma growth and morphology." NeuroImage 37 (January 2007): S59—S70. http://dx.doi.org/10.1016/j.neuroimage.2007.03.008.

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33

Ojo, Sonia A., Lee Whitmore, Ben Slater, and C. Richard A. Catlow. "Understanding nucleation and growth using computer simulation." Solid State Sciences 3, no. 7 (October 2001): 821–26. http://dx.doi.org/10.1016/s1293-2558(01)01201-8.

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34

Cruikshank, K. M., K. E. Neavel, and Guo Zho Zhao. "Computer simulation of growth of duplex structures." Tectonophysics 164, no. 1 (July 1989): 1–12. http://dx.doi.org/10.1016/0040-1951(89)90229-1.

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35

Malki, B., and B. Baroux. "Computer simulation of the corrosion pit growth." Corrosion Science 47, no. 1 (January 2005): 171–82. http://dx.doi.org/10.1016/j.corsci.2004.05.004.

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36

Marner, Brigitte, and Wolfgang Schmickler. "A computer simulation of two-dimensional growth." Journal of Electroanalytical Chemistry and Interfacial Electrochemistry 214, no. 1-2 (December 1986): 589–96. http://dx.doi.org/10.1016/0022-0728(86)80126-7.

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37

Maksymowicz, Andrzej Z., Maria Magdoń, and Jeremy S. S. Whiting. "Computer simulation of dynamics of surface growth." Computer Physics Communications 97, no. 1-2 (August 1996): 101–5. http://dx.doi.org/10.1016/0010-4655(96)00024-0.

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38

Kim, Theodore, Jason Sewall, Avneesh Sud, and Ming C. Lin. "Fast Simulation of Laplacian Growth." IEEE Computer Graphics and Applications 27, no. 2 (March 2007): 68–76. http://dx.doi.org/10.1109/mcg.2007.33.

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39

Ramanathan, Kiruthika, and Sheng-Uei Guan. "Multiorder Neurons for Evolutionary Higher-Order Clustering and Growth." Neural Computation 19, no. 12 (December 2007): 3369–91. http://dx.doi.org/10.1162/neco.2007.19.12.3369.

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This letter proposes to use multiorder neurons for clustering irregularly shaped data arrangements. Multiorder neurons are an evolutionary extension of the use of higher-order neurons in clustering. Higher-order neurons parametrically model complex neuron shapes by replacing the classic synaptic weight by higher-order tensors. The multiorder neuron goes one step further and eliminates two problems associated with higher-order neurons. First, it uses evolutionary algorithms to select the best neuron order for a given problem. Second, it obtains more information about the underlying data distribution by identifying the correct order for a given cluster of patterns. Empirically we observed that when the correlation of clusters found with ground truth information is used in measuring clustering accuracy, the proposed evolutionary multiorder neurons method can be shown to outperform other related clustering methods. The simulation results from the Iris, Wine, and Glass data sets show significant improvement when compared to the results obtained using self-organizing maps and higher-order neurons. The letter also proposes an intuitive model by which multiorder neurons can be grown, thereby determining the number of clusters in data.

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40

Bahmer, Andreas, and Gerald Langner. "A simulation of chopper neurons in the cochlear nucleus with wideband input from onset neurons." Biological Cybernetics 100, no. 1 (November 18, 2008): 21–33. http://dx.doi.org/10.1007/s00422-008-0276-3.

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41

Bahmer, Andreas, and Gerald Langner. "Oscillating neurons in the cochlear nucleus: II. Simulation results." Biological Cybernetics 95, no. 4 (July 18, 2006): 381–92. http://dx.doi.org/10.1007/s00422-006-0091-7.

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42

Boichuk, M. V., and N. M. Shmurygina. "Simulation of Diversified Economic Growth." Cybernetics and Systems Analysis 41, no. 5 (September 2005): 759–66. http://dx.doi.org/10.1007/s10559-006-0012-8.

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43

De Schutter, E. "Computer software for development and simulation of compartmental models of neurons." Computers in Biology and Medicine 19, no. 2 (January 1989): 71–81. http://dx.doi.org/10.1016/0010-4825(89)90001-2.

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44

Oliveri, Hadrien, and Alain Goriely. "Mathematical models of neuronal growth." Biomechanics and Modeling in Mechanobiology 21, no. 1 (January 7, 2022): 89–118. http://dx.doi.org/10.1007/s10237-021-01539-0.

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AbstractThe establishment of a functioning neuronal network is a crucial step in neural development. During this process, neurons extend neurites—axons and dendrites—to meet other neurons and interconnect. Therefore, these neurites need to migrate, grow, branch and find the correct path to their target by processing sensory cues from their environment. These processes rely on many coupled biophysical effects including elasticity, viscosity, growth, active forces, chemical signaling, adhesion and cellular transport. Mathematical models offer a direct way to test hypotheses and understand the underlying mechanisms responsible for neuron development. Here, we critically review the main models of neurite growth and morphogenesis from a mathematical viewpoint. We present different models for growth, guidance and morphogenesis, with a particular emphasis on mechanics and mechanisms, and on simple mathematical models that can be partially treated analytically.

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45

Hisada, Yasuhiro, Osami Saito, Koshi Mitachi, Tatau Nishinaga, and Takuo Sugano. "Computer Simulation of Si Crystal Growth in Space." IEEJ Transactions on Fundamentals and Materials 111, no. 1 (1991): 49–56. http://dx.doi.org/10.1541/ieejfms1990.111.1_49.

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46

MATSUBARA, Hideaki. "Computer Simulation Studies on Sintering and Grain Growth." Journal of the Ceramic Society of Japan 113, no. 1316 (2005): 263–68. http://dx.doi.org/10.2109/jcersj.113.263.

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47

Ohnuma, Ikuo. "Computer Simulation of Grain Growth in Polycrystalline Structures." Materia Japan 38, no. 8 (1999): 610–14. http://dx.doi.org/10.2320/materia.38.610.

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48

Belova, O. N., L. V. Stepanova, and D. V. Chapliy. "COMPUTER SIMULATION OF CRACK GROWTH. MOLECULAR DYNAMICS METHOD." Vestnik of Samara University. Natural Science Series 26, no. 4 (August 17, 2021): 44–55. http://dx.doi.org/10.18287/2541-7525-2020-26-4-44-55.

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The aim of the study is to determine the stress intensity factors using molecular dynamics (MD) method. In the course of the study, a computer simulation of the propagation of a central crack in a copper plate was carried out. The simulation was performed in the LAMMPS (Large-scale Atomic / Molecular Massively Parallel Simulator) software package. A comprehensive study of the influence of geometric characteristics (model dimensions, crack length), temperature, strain rate and loading mixing parameter on the plate strength, crack growth and direction was carried out. The article proposes a method for determining the coefficients of the asymptotic expansion of M. Williams stress fields. The analysis of the influence of the choice of points on the calculation of the coefficients and the comparison of the results obtained with the analytical solution are carried out.

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49

Satoh, Tomotoshi, and Hironori Sakai. "Computer Simulation of Two-Dimensional Normal Grain Growth." Journal of the Japan Institute of Metals 55, no. 7 (1991): 739–47. http://dx.doi.org/10.2320/jinstmet1952.55.7_739.

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50

Jeppesen, Claus, Ole G. Mouritsen, and Henrik Flyvbjerg. "Computer Simulation of Vortex Formation During Domain Growth." Physica Scripta T33 (January 1, 1990): 180–84. http://dx.doi.org/10.1088/0031-8949/1990/t33/034.

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