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Academic literature on the topic 'Neurons Growth Computer simulation'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Neurons Growth Computer simulation"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Neurons Growth Computer simulation"

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Brightman, Frances A. "Computer simulation of a growth factor signal transduction pathway." Thesis, Oxford Brookes University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340868.

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Westwood, Chris. "Computer simulation of diffusional creep failure of engineering alloys." Thesis, University of Surrey, 2001. http://epubs.surrey.ac.uk/843127/.

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A simplified model with only 2 degrees of freedom is developed for cavity growth along a grain-boundary by surface and grain-boundary diffusion following a similar model for a row of grains used by Sun et al, (1996). A variational principle for the coupled diffusion problem is used to follow the cavity growth. The approximate solution can be reduced to the well-established equilibrium cavity growth model at the fast surface diffusion extreme. By comparing the 2 degree of freedom model with the full finite element solution by Pan et al, (1997), a 'Validity Map' is constructed in terms of the relative diffusivity and applied stress relative to the capillarity stress. It is found that the simplified model accurately describes the evolution process, in terms of overall cavity profile and propagation rate for engineering alloys subject to normal levels of applied stresses. The 2 degree of freedom model for a single cavity was then extended to allow the modelling of multiple cavities. These cavities can be either pre-existing or nucleated during the lifetime of the system. The relative rotation between the grains is also considered. The initial 2 degrees of freedom were increased to six, and a cavity element has been derived. The cavity elements are assembled together using the classical finite element approach. This allows the evolution of multiple cavities and their interactions to be modelled under different applied loads and material parameters. This simplified multiple cavity finite element model was compared with a model for cavity evolution based on a 'smeared-out' approach. It was shown that the 'smeared-out' model does not accurately predict the creep damage for realistic engineering materials and conditions and results in an under prediction of creep lifetime. Using the simplified finite element model the effect of surface diffusion on the evolution of the creep damage was investigated. The evolution of a large pre-existing 'crack-like' cavity was modelled and the effects of nucleation, surface diffusion and loading were also investigated. It was shown that in the majority of cases as the surface diffusion was increased the rupture time was also increased. The results from the large 'crack-like' cavity simulations showed that there was very little crack propagation through the material and the smaller cavities tended to grow independently of the large 'crack-like' cavity.
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Green-Petersen, Minna. "Diffusion-Limited Aggregation: a Model for Computer Simulation of Fractal Growth." Thesis, KTH, Teoretisk fysik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146038.

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Diffusion-limited aggregation (DLA) is a model for computer simulation of particle aggregation. It is known to generate aggregates with a characteristic appearance with many branches spreading in all directions and a specic fractal dimension. The aggregates resemble many created by processes in nature such as crystallization, 　uid 　ow and growth of bacteria colonies. In this thesis the DLA model and a few variations of it are investigated to illuminate its 　exibility. The variations investigated are a noise-reduced model, introducing surface tension and increasing the 　ux of particles. The fractal dimension is calculated in each case and compared to the results of similar experiments, both simulated and real.
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Li, Xuefei. "A computational study of bacterial growth in complex environments." HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1410.

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Preyer, Amanda Jervis. "Coupling and synchrony in neuronal networks electrophysiological experiments /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/24799.

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Thesis (Ph.D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008.
Committee Chair: Butera, Robert; Committee Member: Canavier, Carmen; Committee Member: DeWeerth, Stephen; Committee Member: Hasler, Paul; Committee Member: Lanterman, Aaron; Committee Member: Prinz, Astrid.
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Leung, Suet-ying, and 梁雪瑩. "A study of step kinetics by kinetic Monte Carlo simulation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31226322.

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Browne, David John. "Modelling columnar and equiaxed growth." Thesis, University of Oxford, 2002. http://ora.ox.ac.uk/objects/uuid:3d8ae26b-e0b4-4d54-801d-4951705d53aa.

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A novel computer model of the evolution of columnar and equiaxed microstructure during alloy solidification has been developed. A control volume finite difference model of conduction heat transfer is applied to a two-dimensional domain bounded by a relatively cold mould. The initial condition is that of superheated liquid, and nucleation occurs either at the mould wall, leading to columnar dendritic growth, or within the bulk liquid, leading to the growth of equiaxed dendrites. The columnar front or the equiaxed grain boundaries are represented by computationally sharp interfaces, which separate liquid from partially solid alloy. Interpolation between discrete computational markers is employed to describe these interfaces, and a front-tracking technique is used to predict the evolution of the grain structure, via movement of the markers, across the fixed grid. The front velocity is determined via considerations of the kinetics of dendrite growth. The heat equation is fully coupled to the front-tracking algorithm by means of source terms which represent the evolution of latent heat due to the dendritic growth (advancing tips and thickening mushy zone). The model, applied to binary Al-Cu alloys, is computationally efficient. It predicts the variation of the extent of liquid undercooling ahead of the growing columnar front, and new metrics have been established to determine the likelihood of the formation of an equiaxed zone here. The employment of these metrics to establish the influence of heat extraction rate and alloy composition agrees with reports from the literature. The model does not distinguish between individual grains of the columnar zone, but it is shown that this is not an important limitation for most metal casting applications. Direct simulation of the nucleation and growth of multiple equiaxed crystals has been carried out, in which the nucleation and growth of individual grams can be observed via animation, and the influence of melt superheat and heat extraction rate on equiaxed solidification has been determined.
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Torabmostaedi, Hosein. "Computer simulation of processing controls on the formation and growth of nanoparticles in FSP." Thesis, Kingston University, 2014. http://eprints.kingston.ac.uk/28222/.

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In this study, the effect of nozzle geometries and processing controls during Flame Spray Pyrolysis (FSP) process were investigated theoretically on a pilot-scale reactor (production rates up to 5 kg h-1 zirconia) and lab scale reactor (production rates up to 74 g h-1 titania). The focus was on the controlled synthesis and continuous production of nanoparticles at high production rates as well as the study of particle formation and growth inside spray flames. The computational models developed in this study were validated by the measured data available from literature for particle dynamics in spray flames and used to process optimization and reactor design. Chapter one presents an overview of applications of nanoparticles and recent advances in synthesis of nanoparticles by Vapour-fed Flame Synthesis (VFS) and Flame Spray Pyrolysis (FSP). A general introduction on the formation and growth of nanoparticles in the gas-phase synthesis of nanoparticles is presented with emphasis on the mechanisms that control the particle morphology after the initial formation of the monomers. Finally, some existing models in the field are presented and compared. The mathematical models adopted in this study are fully described in chapter two. Several numerical models were developed to predict dynamic viscosity and surface tension of precursor solutions, the pumping pressure of precursor solution, the sauter mean diameter (SMD) of droplets during atomization and the particle growth inside the flame by coagulation and sintering. These models were coupled with the computational fluid dynamic (CFD) code to simulate FSP process. The models were validated by comparison with experimental data developed in this study and literatures. In chapter four, the effect of reactor geometries and processing parameters on the temperature and velocity profiles, droplet evaporation and particle growth were predicted using the validated computational models. The results show that the oxidant/dispersion gas gap size and the oxygen content of oxidant/dispersion gas have a big impact on the flame structure and ultimately the particle growth in the flame. In chapter five, investigation is performed to examine the effect of process parameters on the growth of zirconia particles at low, medium and high precursor concentrations. The results show that fine nanoparticles could be synthesized at low precursor concentration and medium production rates while further studies are needed at higher precursor concentrations and production rates. Therefore, in the sixth chapter, emphasis is placed on industrial production of nanoparticles at higher precursor concentrations. A process operation window for industrial-scale production of zirconia nanoparticles using medium and high precursor concentration was developed. The possible solutions to optimize pump performance and atomization quality at industrial scale production rates using high precursor concentration in FSP were investigated. The quantitative results given in this section can be used as a design guide for a prototype industrial FSP nanoparticle production line. Chapter seven extends the work above and investigates the possibility of quenching the FSP flames at industrial scale production rate by using different reactor configurations. The simulations show how choosing the right configuration and process parameters can affect the characteristics and collection of particles at above the burner. In chapter eight, the applicability of the developed approach for particle simulation of zirconia in the previous chapters is examined for flame spray synthesis of titania. In addition, a series of parametric studies was performed to assist better understanding and control over FSP synthesis of Ti02 nanoparticles. In the ninth chapter, a short summary is given along with recommendations for future investigations.
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Ma, Yingxiao. "Experimental investigation and computer simulation of grain growth and microstructur evolution in 2D polycristals /." Dortmund : Wulff, 2008. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=017070274&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Hawick, Kenneth Arthur. "Domain growth in alloys." Thesis, University of Edinburgh, 1991. http://hdl.handle.net/1842/10605.

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This thesis describes Monte-Carlo computer simulations of binary alloys, with comparisons between small angle neutron scattering (SANS) data, and numerically integrated solutions to the Cahn-Hilliard-Cook (CHC) equation. Elementary theories for droplet growth are also compared with computer simulated data. Monte-Carlo dynamical algorithms are investigated in detail, with special regard for universal dynamical times. The computer simulated systems are Fourier transformed to yield partial structure functions which are compared with SANS data for the binary Iron-Chromium system. A relation between real time and simulation time is found. Cluster statistics are measured in the simulated systems, and compared to droplet formation in the Copper-Cobalt system. Some scattering data for the complex steel PE16 is also discussed. The characterisation of domain size and its growth with time are investigated, and scaling laws fitted to real and simulated data. The simple scaling law of Lifshitz and Slyozov is found to be inadequate, and corrections such as those suggested by Huse, are necessary. Scaling behaviour is studied for the low-concentration nucleation regime and the high-concentration spinodal-decomposition regime. The need for multi-scaling is also considered. The effect of noise and fluctuations in the simulations is considered in the MonteCarlo model, a cellular-automaton (CA) model and in the Cahn-Billiard-Cook equation. The Cook noise term in the CHC equation is found to be important for correct growth scaling properties.
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Books on the topic "Neurons Growth Computer simulation"

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Howard, Alan. A computer simulation of cyanobacterial growth. Reading: University of Reading Department of Geography, 1994.

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Andrew, Coward L., ed. A system architecture approach to the brain: From neurons to consciousness. Hauppauge, NY: Nova Science, 2005.

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Friesen, W. Otto. NeuroDynamix: Computer models for neurophysiology. New York: Oxford University Press, 1994.

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Agent-based computer simulation of dichotomous economic growth. Boston: Kluwer Academic, 2000.

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McCain, Roger A. Agent-Based Computer Simulation of Dichotomous Economic Growth. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4613-9.

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1973-, Friesen Jonathon A., ed. NeuroDynamix: Computer-based neuronal models for neurophysiology. Oxford: Oxford University Press, 1994.

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Pannangpetch, K. Introduction to simulation of crop growth on microcomputer. Khon Kaen, Thailand: Dept. of Agronomy, Faculty of Agriculture, Khon Kaen University, 1992.

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David, McCormick, and Shepherd Gordon M. 1933-, eds. Electrophysiology of the neuron: An interactive tutorial. New York: Oxford University Press, 1994.

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Huguenard, John. Electrophysiology of the neuron: An interactive tutorial. New York: Oxford University Press, 1994.

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Kaandorp, Jaap A. Modelling growth forms of biological objects using fractals. Meppel, the Netherlands: Printed by Krips Repro, 1992.

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Book chapters on the topic "Neurons Growth Computer simulation"

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Meakin, Paul. "Computer Simulation of Growth and Aggregation Processes." In On Growth and Form, 111–35. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-5165-5_7.

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Werlang, Pablo, Michel Q. Fagundes, Diana F. Adamatti, Karina S. Machado, Andrea von Groll, Pedro E. A. da Silva, and Adriano V. Werhli. "Multi-Agent-Based Simulation of Mycobacterium Tuberculosis Growth." In Lecture Notes in Computer Science, 131–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54783-6_9.

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Ofengeim, Dmitry K., and Alexander I. Zhmakin. "Industrial Challenges for Numerical Simulation of Crystal Growth." In Lecture Notes in Computer Science, 3–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44860-8_1.

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Nikolic, Zoran S., Ivona Mitrovic, and Vojislav V. Mitic. "Computer Simulation of Neck Growth During Sintering Process." In Advanced Science and Technology of Sintering, 61–66. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4419-8666-5_6.

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Düchting, W. "Simulation of 3-D Tumor Growth and Radiation Therapy." In CAR ’87 Computer Assisted Radiology, 335–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-95530-3_52.

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Huang, Jiarong, Guangqin Gao, and Fang Guo. "Forest Growth Simulation Based on Artificial Neural Network." In Recent Advances in Computer Science and Information Engineering, 657–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25781-0_96.

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Droll, P., M. El Ganaoui, L. Kadinski, M. Kurz, A. Lamazouade, O. Louchart, D. Morvan, et al. "High Performance Computer Codes and their Application to Optimize Crystal Growth Processes." In Numerical Flow Simulation I, 115–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-540-44437-4_6.

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Zhao, Peng-fei, Tian-en Chen, Wei Wang, and Fang-yi Chen. "Research on Plant Growth Simulation Method Based on ARToolkit." In Computer and Computing Technologies in Agriculture X, 189–96. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06155-5_18.

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Degenhardt, A., P. Droll, M. Ganaoui, L. Kadinski, M. Kurz, A. Lamazouade, D. Morvan, et al. "High Performance Computer Codes and their Application to Optimize Crystal Growth Processes, II." In Numerical Flow Simulation II, 69–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-540-44567-8_5.

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Papáček, Štěpán, Ctirad Matonoha, and Karel Petera. "Modeling and Simulation of Microalgae Growth in a Couette-Taylor Bioreactor." In Lecture Notes in Computer Science, 174–87. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97136-0_13.

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Conference papers on the topic "Neurons Growth Computer simulation"

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Allen, Kathleen B., and Bradley E. Layton. "Mechanical Neural Growth Models." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-79445.

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Critical to being able to control the growth patterns of cell-based sensors is being able to understand how the cytoskeleton of the cell maintains its structure and integrity both under mechanical load and in a load-free environment. Our approach to a better understanding of cell growth is to use a computer simulation that incorporates the primary structures, microtubules, necessary for growth along with their observed behaviors and experimentally determined mechanical properties. Microtubules are the main compressive structural support elements for the axon of a neuron and are created via polymerization of α-β tubulin dimers. Our de novo simulation explores the mechanics of the forces between microtubules and the membrane. We hypothesize that axonal growth is most influenced by the location and direction of the force exerted by the microtubule on the membrane, and furthermore that the interplay of forces between microtubules and the inner surface of the cell membrane dictates the polar structure of axons. The simulation will be used to understand cytoskeletal mechanics for the purpose of engineering cells to be used as sensors.
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Allen, Kathleen B., and Bradley Layton. "A Mechanical Model for Cytoskeleton and Membrane Interactions in Neuronal Growth Cones." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42008.

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Revealing the molecular events of neuronal growth is critical to obtaining a deeper understanding of nervous system development, neural injury response, and neural tissue engineering. Central to this is the need to understand the mechanical interactions among the cytoskeleton and the cell membrane, and how these interactions affect the overall growth mechanics of neurons. Using ANSYS, the force produced by a cytoskeletal protein acting against a deformable membrane was modeled, and the deformation, stress, and strain were computed for the membrane. Parameters to represent the flexural rigidities of the well-studied actin and tubulin cytoskeletal proteins as well as the mechanical properties of neuronal growth cones were used in the simulations. Our model predicts that while a single actin filament is able to produce a force sufficient to cause membrane deformation and thus growth, it is also possible that the actin filament may cause the membrane to rupture, if a dilatational strain of more than 3–4% occurs. Additionally, neurotoxins or pharmaceuticals that alter the mechanical properties of either the cell membrane or cytoskeletal proteins could disrupt the balance of forces required for neurons to not only push out and grow correctly, but also to sustain their shapes as high-aspect-ratio structures once growth is complete. Understanding how cytoskeletal elements have coevolved mechanically with their respective cell membranes will yield insights into the events that gave rise to the sequences and quaternary structures of the major cytoskeletal elements.
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Nakagawa, K., T. Takaki, Y. Morita, and E. Nakamachi. "2D Phase-Field Analyses of Axonal Extension of Nerve Cell." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64281.

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In this study, we aimed to develop a computer-aided simulation technique to predict the axonal extension in the neuronal network evolution processes for design new scaffolds to activate the nerve cell and promote the nerve regeneration. We developed a mathematical model of axonal extension by using phase-field method and evaluated the validity of the mathematical model by comparison with the experiments. In the previous experimental studies, the peripheral nerve scaffold has been introduced to guide the axonal extension. Damaged part of nerve was replaced by the artificial tube as the scaffold to induce the axonal growth through the artificial tube and regenerate the nerve network. However, the scaffold made of biodegradable materials has a problem that it is degraded and absorbed before the nerve regenerate, and then the nerve cannot regenerate. Therefore, there is a need for the design and development of a scaffold for nerve regeneration to promote nerve regeneration. For that purpose, it is necessary to understand the difference between the axonal extensions by the surrounding environment, such as the shape or materials of the scaffold for nerve regeneration. In particular, the numerical technique to analyze the remodeling process of the nerve in the scaffold is strongly required to be established because the in-vivo experimental observation technology at the micro scale, bioethical issues in the animal experiment and requires time and money are also remained as unresolved problems. In this study, we developed a new simulation code which employed the phase-field method to predict the two-dimensional dendritic and axonal growth processes of nerve cells on cultivation scaffolds. We curried out the phase-field analyses to make clear how the parameters of Kobayashi–Warren–Carter (KWC) phase-field model affected on the morphologic growths of dendrite and axon. Simultaneously, we had observed the axonal extension process by using the PC-12D cells with nerve growth factor (NGF) on two-dimensional cultivation dish. Based on these axonal extension observation results, we approximated the morphological changes and establish the phenomenological model for phase-field analysis. Finally, we confirmed the validity of our newly developed phase-field simulation scheme in two dimensions by comparison with the experiments.
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Kilinc, Deniz, and Alper Demir. "Simulation of noise in neurons and neuronal circuits." In 2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, 2015. http://dx.doi.org/10.1109/iccad.2015.7372623.

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Peng, Yueping, and Jue Wang. "Synchrony of Two Uncoupled Neurons under the Chaos Signal Stimulation." In 2010 Second International Conference on Computer Modeling and Simulation (ICCMS). IEEE, 2010. http://dx.doi.org/10.1109/iccms.2010.231.

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Karim, Muhammad-Amri Abdul, Khalid Al-Kofahi, Badrinath Roysam, Natalie Dowell-Mesfin, Rifat J. Hussain, William Shain, and James N. Turner. "Computer Vision Algorithms for Quantifying the Growth and Behavior of Neurons Cultured on Nanofabricated Surfaces." In 2003 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW). IEEE, 2003. http://dx.doi.org/10.1109/cvprw.2003.10016.

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Xin Li and Lichun Wu. "Computer simulation for DLA model of fractal growth." In 2011 International Conference on Computer Science and Service System (CSSS). IEEE, 2011. http://dx.doi.org/10.1109/csss.2011.5974491.

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Kaneko, Yutaka. "Computer simulation of crystal growth and void formation." In Third tohwa university international conference on statistical physics. AIP, 2000. http://dx.doi.org/10.1063/1.1291624.

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Paltanea, Marius, Sabin Tabirca, Ernesc Scheiber, and Mark Tangney. "Logarithmic Growth in Biological Processes." In 2010 12th International Conference on Computer Modelling and Simulation. IEEE, 2010. http://dx.doi.org/10.1109/uksim.2010.29.

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ASCENCIO, SABÁS FLORES, HÉCTOR PÉREZ MEANA, and MARIKO NAKANO MIYATAKE. "TWO AND THREE DIMENSIONAL COMPUTER SIMULATION OF CANCER GROWTH." In Conference on Fractals 2002. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812777720_0002.

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Reports on the topic "Neurons Growth Computer simulation"

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Wilson, Douglas J. Computer Simulation of Sand Ripple Growth and Migration. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada627705.

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Wilson, Douglas J. Computer Simulation of Sand Ripple Growth and Migration. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada626967.

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FOILES, STEPHEN M., and JEFFREY J. HOYT. Computer Simulation of Bubble Growth in Metals Due to He. Office of Scientific and Technical Information (OSTI), March 2001. http://dx.doi.org/10.2172/780304.

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Lebedev, V. Computer simulation of the emittance growth due to noise in large hadron colliders. Office of Scientific and Technical Information (OSTI), March 1993. http://dx.doi.org/10.2172/64338.

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Zimmerman, Jonathan A. Computer simulation of boundary effects on bubble growth in metals due to He. Office of Scientific and Technical Information (OSTI), March 2003. http://dx.doi.org/10.2172/918314.

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Hughes, Garrett A., Paul E. Sendak, and Paul E. Sendak. Key algorithms used in GR02: A computer simulation model for predicting tree and stand growth. Broomall, PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experimental Station, 1985. http://dx.doi.org/10.2737/ne-rp-570.

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Hughes, Garrett A., Paul E. Sendak, and Paul E. Sendak. Key algorithms used in GR02: A computer simulation model for predicting tree and stand growth. Broomall, PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experimental Station, 1985. http://dx.doi.org/10.2737/ne-rp-570.

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Markova, Oksana, Serhiy Semerikov, and Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, May 2018. http://dx.doi.org/10.31812/0564/2250.

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The role of neural network modeling in the learning сontent of special course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities – future IT-specialists and directed to breaking the gap between theoretic computer science and it’s applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic “Neural network and pattern recognition” of the special course “Foundations of Mathematic Informatics” are shown. The program code was presented in a CofeeScript language, which implements the basic components of artificial neural network: neurons, synaptic connections, functions of activations (tangential, sigmoid, stepped) and their derivatives, methods of calculating the network`s weights, etc. The features of the Kolmogorov–Arnold representation theorem application were discussed for determination the architecture of multilayer neural networks. The implementation of the disjunctive logical element and approximation of an arbitrary function using a three-layer neural network were given as an examples. According to the simulation results, a conclusion was made as for the limits of the use of constructed networks, in which they retain their adequacy. The framework topics of individual research of the artificial neural networks is proposed.
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Shani, Uri, Lynn Dudley, Alon Ben-Gal, Menachem Moshelion, and Yajun Wu. Root Conductance, Root-soil Interface Water Potential, Water and Ion Channel Function, and Tissue Expression Profile as Affected by Environmental Conditions. United States Department of Agriculture, October 2007. http://dx.doi.org/10.32747/2007.7592119.bard.

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Constraints on water resources and the environment necessitate more efficient use of water. The key to efficient management is an understanding of the physical and physiological processes occurring in the soil-root hydraulic continuum.While both soil and plant leaf water potentials are well understood, modeled and measured, the root-soil interface where actual uptake processes occur has not been sufficiently studied. The water potential at the root-soil interface (yᵣₒₒₜ), determined by environmental conditions and by soil and plant hydraulic properties, serves as a boundary value in soil and plant uptake equations. In this work, we propose to 1) refine and implement a method for measuring yᵣₒₒₜ; 2) measure yᵣₒₒₜ, water uptake and root hydraulic conductivity for wild type tomato and Arabidopsis under varied q, K⁺, Na⁺ and Cl⁻ levels in the root zone; 3) verify the role of MIPs and ion channels response to q, K⁺ and Na⁺ levels in Arabidopsis and tomato; 4) study the relationships between yᵣₒₒₜ and root hydraulic conductivity for various crops representing important botanical and agricultural species, under conditions of varying soil types, water contents and salinity; and 5) integrate the above to water uptake term(s) to be implemented in models. We have made significant progress toward establishing the efficacy of the emittensiometer and on the molecular biology studies. We have added an additional method for measuring ψᵣₒₒₜ. High-frequency water application through the water source while the plant emerges and becomes established encourages roots to develop towards and into the water source itself. The yᵣₒₒₜ and yₛₒᵢₗ values reflected wetting and drying processes in the rhizosphere and in the bulk soil. Thus, yᵣₒₒₜ can be manipulated by changing irrigation level and frequency. An important and surprising finding resulting from the current research is the obtained yᵣₒₒₜ value. The yᵣₒₒₜ measured using the three different methods: emittensiometer, micro-tensiometer and MRI imaging in both sunflower, tomato and corn plants fell in the same range and were higher by one to three orders of magnitude from the values of -600 to -15,000 cm suggested in the literature. We have added additional information on the regulation of aquaporins and transporters at the transcript and protein levels, particularly under stress. Our preliminary results show that overexpression of one aquaporin gene in tomato dramatically increases its transpiration level (unpublished results). Based on this information, we started screening mutants for other aquaporin genes. During the feasibility testing year, we identified homozygous mutants for eight aquaporin genes, including six mutants for five of the PIP2 genes. Including the homozygous mutants directly available at the ABRC seed stock center, we now have mutants for 11 of the 19 aquaporin genes of interest. Currently, we are screening mutants for other aquaporin genes and ion transporter genes. Understanding plant water uptake under stress is essential for the further advancement of molecular plant stress tolerance work as well as for efficient use of water in agriculture. Virtually all of Israel’s agriculture and about 40% of US agriculture is made possible by irrigation. Both countries face increasing risk of water shortages as urban requirements grow. Both countries will have to find methods of protecting the soil resource while conserving water resources—goals that appear to be in direct conflict. The climate-plant-soil-water system is nonlinear with many feedback mechanisms. Conceptual plant uptake and growth models and mechanism-based computer-simulation models will be valuable tools in developing irrigation regimes and methods that maximize the efficiency of agricultural water. This proposal will contribute to the development of these models by providing critical information on water extraction by the plant that will result in improved predictions of both water requirements and crop yields. Plant water use and plant response to environmental conditions cannot possibly be understood by using the tools and language of a single scientific discipline. This proposal links the disciplines of soil physics and soil physical chemistry with plant physiology and molecular biology in order to correctly treat and understand the soil-plant interface in terms of integrated comprehension. Results from the project will contribute to a mechanistic understanding of the SPAC and will inspire continued multidisciplinary research.
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