Статті в журналах: "Nervous system Computer simulation"

1

Wensch, Jörg, and Ben Sommeijer. "Parallel simulation of axon growth in the nervous system." Parallel Computing 30, no. 2 (February 2004): 163–86. http://dx.doi.org/10.1016/j.parco.2003.04.006.

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2

Furness, JB, JC Bornstein, WAA Kunze, PP Bertrand, H. Kelly, and EA Thomas. "EXPERIMENTAL BASIS FOR REALISTIC LARGE-SCALE COMPUTER SIMULATION OF THE ENTERIC NERVOUS SYSTEM." Clinical and Experimental Pharmacology and Physiology 23, no. 9 (September 1996): 786–92. http://dx.doi.org/10.1111/j.1440-1681.1996.tb01180.x.

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3

MIFTAHOF, ROUSTEM, and N. R. AKHMADEEV. "COMPUTER SIMULATION OF COTRANSMISSION BY EXCITATORY AMINO ACIDS AND ACETYLCHOLINE IN THE ENTERIC NERVOUS SYSTEM." Journal of Mechanics in Medicine and Biology 07, no. 02 (June 2007): 229–46. http://dx.doi.org/10.1142/s0219519407002261.

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The role of cotransmission by α-amino-3-hydroxy-5-methyl-4-isoxalose propionic acid (AMPA), L-aspartate, N-methyl-D-aspartate (NMDA), and acetylcholine (ACh) as well as the coexpression of AMPA, NMDA, and nicotinic ACh (nACh) receptors on the electrophysiological activity of the primary sensory (AH) and motor (S) neurons of the enteric nervous system are numerically assessed. Results of computer simulations showed that AMPA and L-Asp alone can induce fast action potentials of short duration on AH and S neurons. Costimulation of nACh and AMPA receptors on the soma of the S neuron resulted in periodic spiking activity. A characteristic biphasic response was recorded from the AH neuron after coactivation of AMPA and NMDA receptors. Glutamate alone acting on NMDA receptors caused prolonged depolarization of the AH neuron and failed to depolarize the S neuron. Cojoint stimulation of the AMPA or nACh receptors was required to produce the effect of glutamate. The overall electrical response of neurons to the activation of NMDA receptors was long-term depolarization. Acetylcholine, AMPA, and glutamate acting alone or cojointly enhanced phasic contraction of the longitudinal smooth muscle. Treatment of neurons with AMPA, NMDA, and nACh receptor antagonists revealed intricate properties of the AH and S neurons. Application of MK-801, D-AP5, and CPP reduced the excitability of the AH neuron and totally abolished electrical activity in the S neuron. The information gained into the cotransmission by excitatory amino acids and acetylcholine in the enteric nervous system may be beneficial in the development of novel effective therapeutics to treat diseases associated with altered visceral nociception, i.e. irritable bowel syndrome.

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4

ZHANG, DINGGUO, and KUANYI ZHU. "COMPUTER SIMULATION STUDY ON CENTRAL PATTERN GENERATOR: FROM BIOLOGY TO ENGINEERING." International Journal of Neural Systems 16, no. 06 (December 2006): 405–22. http://dx.doi.org/10.1142/s0129065706000810.

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Central pattern generator (CPG) is a neuronal circuit in the nervous system that can generate oscillatory patterns for the rhythmic movements. Its simplified format, neural oscillator, is wildly adopted in engineering application. This paper explores the CPG from an integral view that combines biology and engineering together. Biological CPG and simplified CPG are both studied. Computer simulation reveals the mechanism of CPG. Some properties, such as effect of tonic input and sensory feedback, stable oscillation, robustness, entrainment etc., are further studied. The promising results provide foundation for the potential engineering application in future.

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5

Smith, Bram W., Steen Andreassen, Geoffrey M. Shaw, Per L. Jensen, Stephen E. Rees, and J. Geoffrey Chase. "Simulation of cardiovascular system diseases by including the autonomic nervous system into a minimal model." Computer Methods and Programs in Biomedicine 86, no. 2 (May 2007): 153–60. http://dx.doi.org/10.1016/j.cmpb.2007.02.001.

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6

Nebot, Angela, Francisco Mugica, François E. Cellier, and Montserrat Vallverdú. "Modeling and Simulation of the Central Nervous System Control with Generic Fuzzy Models." SIMULATION 79, no. 11 (November 2003): 648–69. http://dx.doi.org/10.1177/0037549703038883.

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7

Batmunkh, Munkhbaatar, Lkhagvaa Bayarchimeg, Aleksandr N. Bugay, and Oidov Lkhagva. "Monte Carlo track structure simulation in studies of biological effects induced by accelerated charged particles in the central nervous system." EPJ Web of Conferences 204 (2019): 04008. http://dx.doi.org/10.1051/epjconf/201920404008.

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Simulating the biological damage induced by charged particles trajectories (tracks) in the central nervous system (CNS) at different levels of its organization (molecular, cellular, and tissue) is a challenge of modern radiobiology studies. According to the recent experimental studies at particle accelerators, the most radiation-sensitive area of the CNS is the hippocampus. In this regards, the development of measurement-based Monte Carlo simulation of radiation-induced alterations in the hippocampus is of great interest to understand the radiobiological effects on the CNS. The present work investigates the influence of charged particles on the hippocampal cells of the rat brain using the Geant4 Monte Carlo radiation transport code. The applied computer simulation provides a method to simulate physics processes and chemical reactions in the developed model of the rat hippocampus, which contains different types of neural cells - pyramidal cells, mature and immature granular cells, mossy cells, and neural stem cells. The distribution of stochastic energy depositions has been obtained and analyzed in critical structures of the hippocampal neurons after irradiation with 600 MeV/u iron particles. The computed energy deposition in irradiated hippocampal neurons following a track of iron ion suggests that most of the energy is accumulated by granular cells. The obtained quantities at the level of molecular targets also assume that NMDA and GABA receptors belong to the most probable targets in the irradiated neural cells.

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8

Armstrong, J. Douglas, and Jano I. van Hemert. "Towards a virtual fly brain." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1896 (June 13, 2009): 2387–97. http://dx.doi.org/10.1098/rsta.2008.0308.

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Models of the brain that simulate sensory input, behavioural output and information processing in a biologically plausible manner pose significant challenges to both computer science and biology. Here we investigated strategies that could be used to create a model of the insect brain, specifically that of Drosophila melanogaster that is very widely used in laboratory research. The scale of the problem is an order of magnitude above the most complex of the current simulation projects, and it is further constrained by the relative sparsity of available electrophysiological recordings from the fly nervous system. However, fly brain research at the anatomical and behavioural levels offers some interesting opportunities that could be exploited to create a functional simulation. We propose to exploit these strengths of Drosophila central nervous system research to focus on a functional model that maps biologically plausible network architecture onto phenotypic data from neuronal inhibition and stimulation studies, leaving aside biophysical modelling of individual neuronal activity for future models until more data are available.

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9

Shorov, Andrey, and Igor Kotenko. "The Framework for Simulation of Bioinspired Security Mechanisms against Network Infrastructure Attacks." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/172583.

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The paper outlines a bioinspired approach named “network nervous system" and methods of simulation of infrastructure attacks and protection mechanisms based on this approach. The protection mechanisms based on this approach consist of distributed prosedures of information collection and processing, which coordinate the activities of the main devices of a computer network, identify attacks, and determine nessesary countermeasures. Attacks and protection mechanisms are specified as structural models using a set-theoretic approach. An environment for simulation of protection mechanisms based on the biological metaphor is considered; the experiments demonstrating the effectiveness of the protection mechanisms are described.

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10

Otten, E. "Multi-Joint Dynamics and the Development of Movement Control." Neural Plasticity 12, no. 2-3 (2005): 89–98. http://dx.doi.org/10.1155/np.2005.89.

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The movement control of articulated limbs in humans has been explained in terms of equilibrium points and moving equilibrium points or virtual trajectories. One hypothesis is that the nervous system controls multi-segment limbs by simply planning in terms of these equilibrium points and trajectories. The present paper describes a planar computer simulation of an articulated three-segment limb, controlled by pairs of muscles. The shape of the virtual trajectory is analyzed when the limb is required to make fast movements with endpoint movements along a straight line with bell-shaped velocity profiles. Apparently, the faster the movement, the more the virtual trajectory deviates from the real trajectory and becomes up to eight times longer. The complexity of the shape of the virtual trajectories and its length in these fast movements makes it unlikely that the nervous system plans using these trajectories. it seems simpler to set up the required bursts of muscle activation, coupled in the nervous system to the direction of movement, the s peed, and the place in workspace. Finally, it is argued that the two types of explanation do not contradict each other: when a relation is established in the nervous system between muscle activation and movements, equilibrium points and virtual trajectories are necessarily part of that relation.

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11

Le Rolle, Virginie, Alfredo I. Hernández, Pierre-Yves Richard, and Guy Carrault. "An Autonomic Nervous System Model Applied to the Analysis of Orthostatic Tests." Modelling and Simulation in Engineering 2008 (2008): 1–15. http://dx.doi.org/10.1155/2008/427926.

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One of the clinical examinations performed to evaluate the autonomic nervous system (ANS) activity is the tilt test, which consists in studying the cardiovascular response to the change of a patient's position from a supine to a head-up position. The analysis of heart rate variability signals during tilt tests has been shown to be useful for risk stratification and diagnosis on different pathologies. However, the interpretation of such signals is a difficult task. The application of physiological models to assist the interpretation of these data has already been proposed in the literature, but this requires, as a previous step, the identification of patient-specific model parameters. In this paper, a model-based approach is proposed to reproduce individual heart rate signals acquired during tilt tests. A new physiological model adapted to this problem and coupling the ANS, the cardiovascular system (CVS), and global ventricular mechanics is presented. Evolutionary algorithms are used for the identification of patient-specific parameters in order to reproduce heart rate signals obtained during tilt tests performed on eight healthy subjects and eight diabetic patients. The proposed approach is able to reproduce the main components of the observed heart rate signals and represents a first step toward a model-based interpretation of these signals.

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12

Okuno, Hirotsugu, and Tetsuya Yagi. "Bio-Inspired Real-Time Robot Vision for Collision Avoidance." Journal of Robotics and Mechatronics 20, no. 1 (February 20, 2008): 68–74. http://dx.doi.org/10.20965/jrm.2008.p0068.

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A mixed analog-digital integrated vision sensor was designed to detect an approaching object in real-time. To respond selectively to approaching stimuli, the sensor employed an algorithm inspired by the visual nervous system of a locust, which can avoid collisions robustly by using visual information. An electronic circuit model was designed to mimic the architecture of the locust nervous system. Computer simulations showed that the model provided appropriate responses for collision avoidance. We implemented the model with a compact hardware system consisting of a silicon retina and field-programmable gate array (FPGA) circuits; the system was confirmed to respond selectively to approaching stimuli that constituted a collision threat.

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13

Zhou, Wei, Ying Liu, Honglian Li, Zhaoyu Song, Ying Ma, and Yu Zhu. "Mass Spectrometry and Computer Simulation Predict the Interactions of AGPS and HNRNPK in Glioma." BioMed Research International 2021 (September 28, 2021): 1–14. http://dx.doi.org/10.1155/2021/6181936.

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Ether lipids are overexpressed in malignant tumor and play an important role in tumor process. Glioma is the most common malignant central nervous system tumor, and the content of ether lipids is higher than that of normal tissues. Alkylglycerone phosphate synthase (AGPS) is a key enzyme in the synthesis of ether esters and plays a vital role in maintaining the morphology and pathogenic properties of tumor cells. The cell proliferation and the content of tumor-related lipid such as monoalkylglycerol ether (MAGe), lysophosphatidic acid ether (LPAe), lysophosphatidylcholine ether (LPCe), lysophosphatidylethanolamine ether (LPEe), phosphatidyl inositol (PI), phosphatidylcholine (PC), and phosphatidylserine (PS) were suppressed after AGPS silencing in U251, H4, and TJ905 cells; however, heterogeneous nuclear ribonucleoprotein K (HNRNPK) could reverse the above phenomenon such as cellar proliferation and ether lipid secretion. We found that HNRNPK was the target protein of AGPS by coimmunoprecipitation and mass spectrometry assay and verified by western blot assay in U251 cells. It confirmed that AGPS and HNRNPK are coexpressed in the cellular nucleus by a confocal laser microscope. The main protein-protein interaction mechanism between AGPS and HNRNPK is hydrogen bond, conjugation bond, hydrophobic bond, and electrostatic force by computer simulation prediction.

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14

Kułacz, Łukasz, and Adrian Kliks. "Brain-Inspired Data Transmission in Dense Wireless Network." Sensors 21, no. 2 (January 15, 2021): 576. http://dx.doi.org/10.3390/s21020576.

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In this paper, the authors investigate the innovative concept of a dense wireless network supported by additional functionalities inspired by the human nervous system. The nervous system controls the entire human body due to reliable and energetically effective signal transmission. Among the structure and modes of operation of such an ultra-dense network of neurons and glial cells, the authors selected the most worthwhile when planning a dense wireless network. These ideas were captured, modeled in the context of wireless data transmission. The performance of such an approach have been analyzed in two ways, first, the theoretic limits of such an approach has been derived based on the stochastic geometry, in particular—based on the percolation theory. Additionally, computer experiments have been carried out to verify the performance of the proposed transmission schemes in four simulation scenarios. Achieved results showed the prospective improvement of the reliability of the wireless networks while applying proposed bio-inspired solutions and keeping the transmission extremely simple.

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15

Zhang, Xiliang, Tang Zheng, and Yuki Todo. "The Mechanism of Orientation Detection Based on Artificial Visual System." Electronics 11, no. 1 (December 24, 2021): 54. http://dx.doi.org/10.3390/electronics11010054.

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As an important part of the nervous system, the human visual system can provide visual perception for humans. The research on it is of great significance to improve our understanding of biological vision and the human brain. Orientation detection, in which visual cortex neurons respond only to linear stimuli in specific orientations, is an important driving force in computer vision and biological vision. However, the principle of orientation detection is still unknown. This paper proposes an orientation detection mechanism based on dendrite calculation of local orientation detection neurons. We hypothesized the existence of orientation detection neurons that only respond to specific orientations and designed eight neurons that can detect local orientation information. These neurons interact with each other based on the nonlinearity of dendrite generation. Then, local orientation detection neurons are used to extract local orientation information, and global orientation information is deduced from local orientation information. The effectiveness of the mechanism is verified by computer simulation, which shows that the machine can perform orientation detection well in all experiments, regardless of the size, shape, and position of objects. This is consistent with most known physiological experiments.

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16

GÁL, V., J. HÁMORI, T. ROSKA, D. BÁLYA, ZS BOROSTYÁNKŐI, M. BRENDEL, K. LOTZ, et al. "RECEPTIVE FIELD ATLAS AND RELATED CNN MODELS." International Journal of Bifurcation and Chaos 14, no. 02 (February 2004): 551–84. http://dx.doi.org/10.1142/s0218127404009545.

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In this paper we demonstrate the potential of the cellular nonlinear/neural network paradigm (CNN) that of the analogic cellular computer architecture (called CNN Universal Machine — CNN-UM) in modeling different parts and aspects of the nervous system. The structure of the living sensory systems and the CNN share a lot of features in common: local interconnections ("receptive field architecture"), nonlinear and delayed synapses for the processing tasks, the potentiality of feedback and using the advantages of both the analog and logic signal-processing mode. The results of more than ten years of cooperative work of many engineers and neurobiologists have been collected in an atlas: what we present here is a kind of selection from these studies emphasizing the flexibility of the CNN computing: visual, tactile and auditory modalities are concerned.

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17

Stevens, Scott A., and William D. Lakin. "A mathematical model of the systemic circulatory system with logistically defined nervous system regulatory mechanisms." Mathematical and Computer Modelling of Dynamical Systems 12, no. 6 (December 2006): 555–76. http://dx.doi.org/10.1080/13873950500064343.

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18

Matsumura, Michikazu. "Computer simulation analysis of a perceptron-type neural network model based on anatomical, excitatory and inhibitory connections in the central nervous system." Neuroscience Research Supplements 7 (January 1988): S73. http://dx.doi.org/10.1016/0921-8696(88)90157-0.

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19

Alkabaa, Abdulaziz S., Osman Taylan, Mustafa Tahsin Yilmaz, Ehsan Nazemi, and El Mostafa Kalmoun. "An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach." Mathematics 10, no. 4 (February 16, 2022): 612. http://dx.doi.org/10.3390/math10040612.

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The main required organ of the biological system is the Central Nervous System (CNS), which can influence the other basic organs in the human body. The basic elements of this important organ are neurons, synapses, and glias (such as astrocytes, which are the highest percentage of glias in the human brain). Investigating, modeling, simulation, and hardware implementation (realization) of different parts of the CNS are important in case of achieving a comprehensive neuronal system that is capable of emulating all aspects of the real nervous system. This paper uses a basic neuron model called the Izhikevich neuronal model to achieve a high copy of the primary nervous block, which is capable of regenerating the behaviors of the human brain. The proposed approach can regenerate all aspects of the Izhikevich neuron in high similarity degree and performances. The new model is based on Look-Up Table (LUT) modeling of the mathematical neuromorphic systems, which can be realized in a high degree of correlation with the original model. The proposed procedure is considered in three cases: 100 points LUT modeling, 1000 points LUT modeling, and 10,000 points LUT modeling. Indeed, by removing the high-cost functions in the original model, the presented model can be implemented in a low-error, high-speed, and low-area resources state in comparison with the original system. To test and validate the proposed final hardware, a digital FPGA board (Xilinx Virtex-II FPGA board) is used. Digital hardware synthesis illustrates that our presented approach can follow the Izhikevich neuron in a high-speed state (more than the original model), increase efficiency, and also reduce overhead costs. Implementation results show the overall saving of 84.30% in FPGA and also the higher frequency of the proposed model of about 264 MHz, which is significantly higher than the original model, 28 MHz.

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20

Linder, R., D. Mörschner, S. J. Pöppl, and A. Moser. "Computer-Aided Diagnosis of Multiple Sclerosis." Computational and Mathematical Methods in Medicine 10, no. 1 (2009): 39–47. http://dx.doi.org/10.1080/17486700802070724.

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The study aims to develop a computer-assisted decision support based on cerebrospinal fluid (CSF) and blood findings to improve their value and ease the diagnostic procedure of chronic inflammatory diseases (CIDs) of central nervous system (CNS). Data were collected from patients suffering from multiple sclerosis (MS,n = 73), from another CID of the CNS (n = 22), or a psychiatric disease (control group, CTRL,n = 12). Univariate and multivariate analyses were performed using multiple logistic regression and artificial neural networks. Differentiating between MS and CID, no parameter could be disclosed that could provide a meaningful decision support. However, multivariate analysis obtained a statistically significant classification (sensitivity = 84.9%, specificity = 54.5%,p < 0.001). On the contrary, multivariate analysis based on the differentiation between MS vs. CTRL, gave good results (sensitivity = 95.9%, specificity = 83.3%,p < 0.001). It became evident from standard laboratory findings that there is a significant potential for computer-aided decision support.

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21

Ma, Guilei, Menghua Man, Yongqiang Zhang, and Shanghe Liu. "A Fast Homeostatic Inhibitory Plasticity Rule Circuit with a Memristive Synapse." Electronics 12, no. 3 (January 17, 2023): 490. http://dx.doi.org/10.3390/electronics12030490.

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Learning from the robust mechanism of the biological nervous system is critical for creating reliable neuromorphic hardware. The homeostatic inhibition plasticity rule is a robust biological mechanism to balance Hebbian plasticity and resist external environmental disturbances and local damage. It plays an essential role in maintaining the homeostatic sparse firing patterns of the nervous system. This paper imitates this mechanism and provides a fast homeostatic inhibitory plasticity rule circuit with a memristive synapse. Firstly, the design method and principle of the circuit are demonstrated. Secondly, the function of the circuit was verified in PSpice© using a commercial Knowm memristor as a synapse. The PSpice© simulation results show that the circuit can achieve a weight update curve similar to the biological homeostatic inhibitory plasticity rule, and the time scale of the circuit is improved by a factor of 1000 compared to that of the biological nervous system. Furthermore, the circuit has wide applicability due to the tunable qualities of the homeostatic learning window, scaling factor, and homeostatic factor. This study provides new opportunities for building fast and reliable neuromorphic hardware.

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22

Gierdziewicz, Maciej. "Relations between Geometric Parameters and Numerical Simulation Accuracy in Modeling Signal Transmission in the Presynaptic Bouton." Applied Sciences 11, no. 6 (March 22, 2021): 2811. http://dx.doi.org/10.3390/app11062811.

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In order to examine nerve impulses by means of simulation methodology, the models of all parts of nervous system, well suited for numerical modeling, are needed. In this paper the problem of setting up such a model, namely, that of a presynaptic bouton, is addressed. Simulation of the neurotransmitter flow inside the presynaptic bouton is performed. The transport is modeled with a partial differential equation with an additional nonlinear term. Two ways of modeling the bouton are applied. One of them let reflect a complex shape of the bouton and of some inner organelles. The influence of the generated mesh quality on the accuracy of numerical simulations is studied by comparing the released amount of neurotransmitter. The only mesh that produced diminished output was the worst one. The conclusion is that even slightly optimized tetrahedral mesh is suitable for calculations.

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23

Radwan, Awwad, та Fars Alanazi. "Combined Modeling Study of the Binding Characteristics of Natural Compounds, Derived from Psoralea Fruits, to β-Amyloid Peptide Monomer". International Journal of Molecular Sciences 23, № 7 (24 березня 2022): 3546. http://dx.doi.org/10.3390/ijms23073546.

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A dysfunctional protein aggregation in the nervous system can lead to several neurodegenerative disorders that result in intracellular inclusions or extracellular aggregates. An early critical event within the pathogenesis of Alzheimer’s disease is the accumulation of amyloid beta peptide within the brain. Natural compounds isolated from Psoralea Fructus (PF) have significant anti-Alzheimer effects as strong inhibitors of Aβ42 aggregation. Computer simulations provide a powerful means of linking experimental findings to nanoscale molecular events. As part of this research four prenylated compounds, the active ingredients of Psoralea Fructus (PF), were studied as Aβ42 accumulation inhibitors using molecular simulations modeling. In order to resolve the binding modes of the ligands and identify the main interactions of Aβ42 residues, we performed a 100 ns molecular dynamics simulation and binding free energy calculations starting from the model of the compounds obtained from the docking study. This study was able to pinpoint the key amino acid residues in the Aβ42 active site and provide useful information that could benefit the development of new Aβ42 accumulation inhibitors.

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Zdarova Karasova, Jana, Martin Mzik, Tomas Kucera, Zbynek Vecera, Jiri Kassa, and Vit Sestak. "Interaction of Cucurbit[7]uril with Oxime K027, Atropine, and Paraoxon: Risky or Advantageous Delivery System?" International Journal of Molecular Sciences 21, no. 21 (October 23, 2020): 7883. http://dx.doi.org/10.3390/ijms21217883.

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Antidotes against organophosphates often possess physicochemical properties that mitigate their passage across the blood–brain barrier. Cucurbit[7]urils may be successfully used as a drug delivery system for bisquaternary oximes and improve central nervous system targeting. The main aim of these studies was to elucidate the relationship between cucurbit[7]uril, oxime K027, atropine, and paraoxon to define potential risks or advantages of this delivery system in a complex in vivo system. For this reason, in silico (molecular docking combined with umbrella sampling simulation) and in vivo (UHPLC—pharmacokinetics, toxicokinetics; acetylcholinesterase reactivation and functional observatory battery) methods were used. Based on our results, cucurbit[7]urils affect multiple factors in organophosphates poisoning and its therapy by (i) scavenging paraoxon and preventing free fraction of this toxin from entering the brain, (ii) enhancing the availability of atropine in the central nervous system and by (iii) increasing oxime passage into the brain. In conclusion, using cucurbit[7]urils with oximes might positively impact the overall treatment effectiveness and the benefits can outweigh the potential risks.

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25

Hu, Ningning, Aihui Wang, and Yuanhang Wu. "Robust adaptive PD-like control of lower limb rehabilitation robot based on human movement data." PeerJ Computer Science 7 (February 24, 2021): e394. http://dx.doi.org/10.7717/peerj-cs.394.

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The combination of biomedical engineering and robotics engineering brings hope of rehabilitation to patients with lower limb movement disorders caused by diseases of the central nervous system. For the comfort during passive training, anti-interference and the convergence speed of tracking the desired trajectory, this paper analyzes human body movement mechanism and proposes a robust adaptive PD-like control of the lower limb exoskeleton robot based on healthy human gait data. In the case of bounded error perturbation, MATLAB simulation verifies that the proposed method can ensure the global stability by introducing an S-curve function to make the design robust adaptive PD-like control. This control strategy allows the lower limb rehabilitation robot to track the human gait trajectory obtained through the motion capture system more quickly, and avoids excessive initial output torque. Finally, the angle similarity function is used to objectively evaluate the human body for wearing the robot comfortably.

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26

Prokopowicz, Piotr, Dariusz Mikołajewski, Krzysztof Tyburek, and Piotr Kotlarz. "Fuzzy-based Description of Computational Complexity of Central Nervous Systems." Journal of Telecommunications and Information Technology 3 (September 30, 2020): 57–66. http://dx.doi.org/10.26636/jtit.2020.145620.

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Computational intelligence algorithms are currently capable of dealing with simple cognitive processes, but still remain ineﬃcient compared with the human brain’s ability to learn from few exemplars or to analyze problems that have not been deﬁned in an explicit manner. Generalization and decision-making processes typically require an uncertainty model that is applied to the decision options while relying on the probability approach. Thus, models of such cognitive functions usually interact with reinforcement-based learning to simplify complex problems. Decision-makers are needed to choose from the decision options that are available, in order to ensure that the decision-makers’ choices are rational. They maximize the subjective overall utility expected, given by the outcomes in diﬀerent states and weighted with subjective beliefs about the occurrence of those states. Beliefs are captured by probabilities and new information is incorporated using the Bayes’ law. Fuzzy-based models described in this paper propose a diﬀerent – they may serve as a point of departure for a family of novel methods enabling more effective and neurobiologically reliable brain simulation that is based on fuzzy logic techniques and that turns out to be useful in both basic and applied sciences. The approach presented provides a valuable insight into understanding the aforementioned processes, doing that in a descriptive, fuzzy-based manner, without presenting a complex analysis

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27

Wang, Aihui, Ningning Hu, Jun Yu, Junlan Lu, Yifei Ge, and Yan Wang. "Human-Like Robust Adaptive PD Based Human Gait Tracking for Exoskeleton Robot." Journal of Robotics and Mechatronics 33, no. 1 (February 20, 2021): 88–96. http://dx.doi.org/10.20965/jrm.2021.p0088.

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For patients with dyskinesias caused by central nervous system diseases such as stroke, in the early stage of rehabilitation training, lower limb rehabilitation robots are used to provide passive rehabilitation training. This paper proposed a human-like robust adaptive PD control strategy of the exoskeleton robot based on healthy human gait data. When the error disturbance is bounded, a human-like robust adaptive PD control strategy is designed, which not only enables the rehabilitation exoskeleton robot to quickly track the human gait trajectory obtained through the 3D NOKOV motion capture system, but also can well identify the structural parameters of the system and avoid excessively initial output torque for the robot. MATLAB simulation verifies that the proposed method has a better performance to realize tracking the experimental trajectory of human movement and anti-interference ability under the condition of ensuring global stability for a lower limb rehabilitation exoskeleton robot.

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Rizzo, Albert A., Todd Bowerly, J. Galen Buckwalter, Dean Klimchuk, Roman Mitura, and Thomas D. Parsons. "A Virtual Reality Scenario for All Seasons:The Virtual Classroom." CNS Spectrums 11, no. 1 (October 2009): 35–44. http://dx.doi.org/10.1017/s1092852900024196.

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ABSTRACTTreatment and rehabilitation of the cognitive, psychological, and motor sequelae of central nervous system dysfunction often relies on assessment instruments to inform diagnosis and to track changes in clinical status. Typically, these assessments employ paper-and-pencil psychometrics, hands-on analog/computer tests, and rating of behavior within the context of real-world functional environments. Virtual reality offers the option to produce and distribute identical “standard” simulation environments in which performance can be measured and rehabilitated. Within such digital scenarios, normative data can be accumulated for performance comparisons needed for assessment/diagnosis and for treatment/rehabilitation purposes. In this manner, reusable archetypic virtual environments constructed for one purpose can also be applied for applications addressing other clinical targets. This article will provide a review of such a retooling approach using a virtual classroom simulation that was originally developed as a controlled stimulus environment in which attention processes could be systematically assessed in children with attention-deficit/hyperactivity disorder. This system is now being applied to other clinical targets including the development of tests that address other cognitive functions, eye movement under distraction conditions, social anxiety disorder, and the creation of an earthquake safety training application for children with developmental and learning disabilities.

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29

Madwed, J. B., P. Albrecht, R. G. Mark, and R. J. Cohen. "Low-frequency oscillations in arterial pressure and heart rate: a simple computer model." American Journal of Physiology-Heart and Circulatory Physiology 256, no. 6 (June 1, 1989): H1573—H1579. http://dx.doi.org/10.1152/ajpheart.1989.256.6.h1573.

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We have previously reported that low-frequency oscillations in arterial blood pressure (ABP) and heart rate (HR) occur when conscious dogs experience severe blood loss. These low-frequency oscillations are generated by enhancement of the sympathetic nervous system and inhibition of the parasympathetic nervous system. We have developed a simple computer model to elucidate those properties critical to the generation of these oscillations. Our model incorporates several important features: 1) arterial baroreceptor feedback loops, which relate ABP to targeted HR and total peripheral resistance (TPR) values; 2) two effector outputs, HR and TPR, which are controlled by the outputs of vagal, beta-adrenergic, and alpha-adrenergic effector mechanisms; 3) a fixed beat-to-beat stroke volume; and 4) a wind-kessel model, which represents the peripheral circulation. Each effector mechanism is modeled as a low-pass filter in series with a delay. The vagal effector mechanism slows the HR after a 100-ms delay and reaches maximal HR at that time. The beta-adrenergic effector mechanism speeds HR after a 2.5-s delay and then increases to maximal HR 7.5 s later. The alpha-adrenergic effector mechanism begins vasoconstriction after a 5-s delay and then reaches maximal contraction 15 s later. Computer simulations of inhibition of the vagal effector mechanism and activation of the adrenergic effector mechanisms elicit low-frequency oscillations in ABP and HR. These oscillations are similar to those observed experimentally in the dog during hemorrhage. We conclude that the slow temporal response of the alpha-adrenergic effector mechanism controlling TPR is the critical element in predicting the observed low-frequency oscillations in ABP and HR.

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30

IGLESIAS, JAVIER, and ALESSANDRO E. P. VILLA. "EMERGENCE OF PREFERRED FIRING SEQUENCES IN LARGE SPIKING NEURAL NETWORKS DURING SIMULATED NEURONAL DEVELOPMENT." International Journal of Neural Systems 18, no. 04 (August 2008): 267–77. http://dx.doi.org/10.1142/s0129065708001580.

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Two main processes concurrently refine the nervous system over the course of development: cell death and selective synaptic pruning. We simulated large spiking neural networks (100 × 100 neurons "at birth") characterized by an early developmental phase with cell death due to excessive firing rate, followed by the onset of spike timing dependent synaptic plasticity (STDP), driven by spatiotemporal patterns of stimulation. The cell death affected the inhibitory units more than the excitatory units during the early developmental phase. The network activity showed the appearance of recurrent spatiotemporal firing patterns along the STDP phase, thus suggesting the emergence of cell assemblies from the initially randomly connected networks. Some of these patterns were detected throughout the simulation despite the activity-driven network modifications while others disappeared.

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31

Bem, Tiaza, Yves Le Feuvre, John Simmers, and Pierre Meyrand. "Electrical Coupling Can Prevent Expression of Adult-Like Properties in an Embryonic Neural Circuit." Journal of Neurophysiology 87, no. 1 (January 1, 2002): 538–47. http://dx.doi.org/10.1152/jn.00372.2001.

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Electrical coupling is widespread in developing nervous systems and plays a major role in circuit formation and patterning of activity. In most reported cases, such coupling between rhythmogenic neurons tends to synchronize and enhance their oscillatory behavior, thereby producing monophasic rhythmic output. However, in many adult networks, such as those responsible for rhythmic motor behavior, oscillatory neurons are linked by synaptic inhibition to produce rhythmic output with multiple phases. The question then arises whether such networks are still able to generate multiphasic output in the early stage of development when electrical coupling is abundant. A suitable model for addressing this issue is the lobster stomatogastric nervous system (STNS). In the adult animal, the STNS consists of three discrete neural networks that are comprised of oscillatory neurons interconnected by reciprocal inhibition. These networks generate three distinct rhythmic motor patterns with large amplitude neuronal oscillations. By contrast, in the embryo the same neuronal population expresses a single multiphasic rhythm with small-amplitude oscillations. Recent findings have revealed that adult-like network properties are already present early in the embryonic system but are masked by an as yet unknown mechanism. Here we use computer simulation to test whether extensive electrical coupling may be involved in masking adult-like properties in the embryonic STNS. Our basic model consists of three different adult-like STNS networks that are built of relaxation oscillators interconnected by reciprocal synaptic inhibition. Individual model cells generate slow membrane potential oscillations without action potentials. The introduction of widespread electrical coupling between members of these networks dampens oscillation amplitudes and, at moderate coupling strengths, may coordinate neuronal activity into a single rhythm with different phases, which is strongly reminiscent of embryonic STNS output. With a further increase in coupling strength, the system reaches one of two final states depending on the relative contribution of inhibition and inherent oscillatory properties within the networks: either fully synchronized and dampened oscillations, or a complete collapse of activity. Our simulations indicate that, beginning from either of these two states, the emergence of distinct adult networks during maturation may arise from a developmental decrease in electrical coupling that unmasks preexisting adult-like network properties.

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32

Rattay, Frank, and Hans Motz. "Simulation of the Response of a Multichannel Nerve Array to Pulse Shapes Produced by Single-Channel Electrostimulation." Perception 16, no. 6 (December 1987): 769–76. http://dx.doi.org/10.1068/p160769.

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Results of experiments on single-channel electrostimulation of the cochlea which throw light on the performance of the central auditory nervous system (CANS) have recently been reported by Dobie and Dillier. Trains of pulses with different rise times could be distinguished by subjects with cochlea implants, even though time differences involved were very small. It was suggested by the authors that the information is carried to the CANS by an array of nerve fibres with characteristic time differences. In the present paper, simulations produced by means of a nerve model are reported and used to compute the patterns of action potentials evoked on the nerve array by different pulse trains. The changes in the patterns of the nerve responses resulting from the shape variations which have to be perceived by the CANS are examined.

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33

Taylan, Osman, Mona Abusurrah, Ehsan Eftekhari-Zadeh, Ehsan Nazemi, Farheen Bano, and Ali Roshani. "Controlling Effects of Astrocyte on Neuron Behavior in Tripartite Synapse Using VHDL–AMS." Mathematics 9, no. 21 (October 25, 2021): 2700. http://dx.doi.org/10.3390/math9212700.

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Astrocyte cells form the largest cell population in the brain and can influence neuron behavior. These cells provide appropriate feedback control in regulating neuronal activities in the Central Nervous System (CNS). This paper presents a set of equations as a model to describe the interactions between neurons and astrocyte. A VHDL–AMS-based tripartite synapse model that includes a pre-synaptic neuron, the synaptic terminal, a post-synaptic neuron, and an astrocyte cell is presented. In this model, the astrocyte acts as a controller module for neurons and can regulates the spiking activity of them. Simulation results show that by regulating the coupling coefficients of astrocytes, spiking frequency of neurons can be reduced and the activity of neuronal cells is modulated.

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Amir, Shahizat, N. S. Mohamed, Siti Aishah Hashim Ali, and Shahrul Amir. "Simulation Model of Multiple Cluster Fractals Cultured in Nanocomposite Polymer Electrolyte Film." Materials Science Forum 864 (August 2016): 163–68. http://dx.doi.org/10.4028/www.scientific.net/msf.864.163.

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This work contributes to the melioration of the modeling and simulation of laboratory cultured fractals using poly (vinylidene fluoride-co-hexafluoropropylene)/poly (ethyl methacrylate)-ammonium trifluorome-thanesulfonate nanocomposite polymer electrolyte films as the media of growth. Main focus is given to fractals and fractal growth models particularly DLA (Diffusion Limited Aggregation). The DLA cluster formed through DLA is formed by particles moving in Brownian motion (diffusion) which meet and stick together randomly (aggregation) to form the cluster. The simulation of multiple cluster fractals is done using DLA methods incorporating different parameters such as its sticking coefficient, lattice geometry and number of particles. To compare the simulation with the real patterns obtained, one vital aspect would be the calculation of their fractal dimension values. The computer program developed is able to calculate the fractal dimension value of each of the simulated fractal patterns. Suitable fractal dimension calculation method is employed according to its usefulness and efficiency. Fractal growth modeling and simulation such as done here can contribute to the understanding of other related studies concerning fractal growth found in areas including medical (nervous systems, cancer growth and more).

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Ye, Jiazhen, Yuki Todo, Zheng Tang, Bin Li, and Yu Zhang. "Artificial Visual System for Orientation Detection." Electronics 11, no. 4 (February 13, 2022): 568. http://dx.doi.org/10.3390/electronics11040568.

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The human visual system is one of the most important components of the nervous system, responsible for visual perception. The research on orientation detection, in which neurons of the visual cortex respond only to a line stimulus in a particular orientation, is an important driving force of computer vision and biological vision. However, the principle underlying orientation detection remains a mystery. In order to solve this mystery, we first propose a completely new mechanism that explains planar orientation detection in a quantitative manner. First, we assume that there are planar orientation-detective neurons which respond only to a particular planar orientation locally and that these neurons detect local planar orientation information based on nonlinear interactions that take place on the dendrites. Then, we propose an implementation of these local planar orientation-detective neurons based on their dendritic computations, use them to extract the local planar orientation information, and infer the global planar orientation information from the local planar orientation information. Furthermore, based on this mechanism, we propose an artificial visual system (AVS) for planar orientation detection and other visual information processing. In order to prove the effectiveness of our mechanism and the AVS, we conducted a series of experiments on rectangular images which included rectangles of various sizes, shapes and positions. Computer simulations show that the mechanism can perfectly perform planar orientation detection regardless of their sizes, shapes and positions in all experiments. Furthermore, we compared the performance of both AVS and a traditional convolution neural network (CNN) on planar orientation detection and found that AVS completely outperformed CNN in planar orientation detection in terms of identification accuracy, noise resistance, computation and learning cost, hardware implementation and reasonability.

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36

Mang, Andreas, Spyridon Bakas, Shashank Subramanian, Christos Davatzikos, and George Biros. "Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology." Annual Review of Biomedical Engineering 22, no. 1 (June 4, 2020): 309–41. http://dx.doi.org/10.1146/annurev-bioeng-062117-121105.

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Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved ( a) detection/segmentation of tumor subregions and ( b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of ( a) biophysical growth modeling and simulation,( b) inverse problems for model calibration, ( c) these models' integration with imaging workflows, and ( d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.

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Jaramillo, Santiago, and Barak A. Pearlmutter. "Optimal Coding Predicts Attentional Modulation of Activity in Neural Systems." Neural Computation 19, no. 5 (May 2007): 1295–312. http://dx.doi.org/10.1162/neco.2007.19.5.1295.

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Neuronal activity in response to a fixed stimulus has been shown to change as a function of attentional state, implying that the neural code also changes with attention. We propose an information-theoretic account of such modulation: that the nervous system adapts to optimally encode sensory stimuli while taking into account the changing relevance of different features. We show using computer simulation that such modulation emerges in a coding system informed about the uneven relevance of the input features. We present a simple feedforward model that learns a covert attention mechanism, given input patterns and coding fidelity requirements. After optimization, the system gains the ability to reorganize its computational resources (and coding strategy) depending on the incoming attentional signal, without the need of multiplicative interaction or explicit gating mechanisms between units. The modulation of activity for different attentional states matches that observed in a variety of selective attention experiments. This model predicts that the shape of the attentional modulation function can be strongly stimulus dependent. The general principle presented here accounts for attentional modulation of neural activity without relying on special-purpose architectural mechanisms dedicated to attention. This principle applies to different attentional goals, and its implications are relevant for all modalities in which attentional phenomena are observed.

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Aoyama, H., H. Shirato, Y. Watanabe, T. Nishioka, S. Hashimoto, K. Tsuchiya, K. Fujita, K. Kagei, and K. Miyasaka. "Development of an interactive magnetic resonance imaging (MRI)/computed tomography (CT) simulation system and its impact on gross tumor volume (GTV) delineation of central nervous system (CNS) tumors." International Journal of Radiation Oncology*Biology*Physics 48, no. 3 (January 2000): 298–99. http://dx.doi.org/10.1016/s0360-3016(00)80399-5.

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39

Kumar, Chundi Vinay, Rayapadi G. Swetha, Anand Anbarasu, and Sudha Ramaiah. "Computational Analysis Reveals the Association of Threonine 118 Methionine Mutation in PMP22 Resulting in CMT-1A." Advances in Bioinformatics 2014 (October 20, 2014): 1–10. http://dx.doi.org/10.1155/2014/502618.

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The T118M mutation in PMP22 gene is associated with Charcot Marie Tooth, type 1A (CMT1A). CMT1A is a form of Charcot-Marie-Tooth disease, the most common inherited disorder of the peripheral nervous system. Mutations in CMT related disorder are seen to increase the stability of the protein resulting in the diseased state. We performed SNP analysis for all the nsSNPs of PMP22 protein and carried out molecular dynamics simulation for T118M mutation to compare the stability difference between the wild type protein structure and the mutant protein structure. The mutation T118M resulted in the overall increase in the stability of the mutant protein. The superimposed structure shows marked structural variation between the wild type and the mutant protein structures.

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40

Donhouede, Blaise. "Nervous coding of the acoustic message — A computational model of the first stages of the peripheral auditory system." Speech Communication 4, no. 4 (December 1985): 352. http://dx.doi.org/10.1016/0167-6393(85)90064-0.

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41

Peng, Hua, Jing Li, Huosheng Hu, Keli Hu, Chao Tang, and Yulong Ding. "Creating a Computable Cognitive Model of Visual Aesthetics for Automatic Aesthetics Evaluation of Robotic Dance Poses." Symmetry 12, no. 1 (December 20, 2019): 23. http://dx.doi.org/10.3390/sym12010023.

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Inspired by human dancers who can evaluate the aesthetics of their own dance poses through mirror observation, this paper presents a corresponding mechanism for robots to improve their cognitive and autonomous abilities. Essentially, the proposed mechanism is a brain-like intelligent system that is symmetrical to the visual cognitive nervous system of the human brain. Specifically, a computable cognitive model of visual aesthetics is developed using the two important aesthetic cognitive neural models of the human brain, which is then applied in the automatic aesthetics evaluation of robotic dance poses. Three kinds of features (color, shape and orientation) are extracted in a manner similar to the visual feature elements extracted by human brains. After applying machine learning methods in different feature combinations, machine aesthetics models are built for automatic evaluation of robotic dance poses. The simulation results show that our approach can process visual information effectively by cognitive computation, and achieved a very good evaluation performance of automatic aesthetics.

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42

Xu, Li, Qi Gao, and Nasser Yousefi. "Brain tumor diagnosis based on discrete wavelet transform, gray-level co-occurrence matrix, and optimal deep belief network." SIMULATION 96, no. 11 (August 21, 2020): 867–79. http://dx.doi.org/10.1177/0037549720948595.

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Brain tumors are a group of cancers that originate from different cells of the central nervous system or cancers of other tissues in the brain. Excessive cell growth in the brain is called a tumor. Tumor cells need food and blood to survive. Growth and proliferation of tumor cells in the cranial space, cause strain inside the brain and thus disrupt vital human structures. Therefore, diagnosis in the early stages of brain tumors is crucial. This study introduces a new optimized method for early diagnosis of the brain tumor. The method has five main parts of noise reduction, tumor segmentation, morphology, feature extraction based on wavelet and gray-level co-occurrence matrix, and classification based on an optimized deep belief network. For optimizing the classifier network, an enhanced version of the moth search algorithm is utilized. Simulation results are applied to three different datasets, FLAIR, T1, and T2, and the accuracy results of the presented method are compared with two other metaheuristics, particle swarm optimization and Bat algorithms. The final results showed that the presented technique has good achievements toward the compared methods.

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43

Tyson, Adam L., Charly V. Rousseau, Christian J. Niedworok, Sepiedeh Keshavarzi, Chryssanthi Tsitoura, Lee Cossell, Molly Strom, and Troy W. Margrie. "A deep learning algorithm for 3D cell detection in whole mouse brain image datasets." PLOS Computational Biology 17, no. 5 (May 28, 2021): e1009074. http://dx.doi.org/10.1371/journal.pcbi.1009074.

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Understanding the function of the nervous system necessitates mapping the spatial distributions of its constituent cells defined by function, anatomy or gene expression. Recently, developments in tissue preparation and microscopy allow cellular populations to be imaged throughout the entire rodent brain. However, mapping these neurons manually is prone to bias and is often impractically time consuming. Here we present an open-source algorithm for fully automated 3D detection of neuronal somata in mouse whole-brain microscopy images using standard desktop computer hardware. We demonstrate the applicability and power of our approach by mapping the brain-wide locations of large populations of cells labeled with cytoplasmic fluorescent proteins expressed via retrograde trans-synaptic viral infection.

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Al Qahtani, Waleed M. S., and Mohamed I. El-Anwar. "Advanced Computational Methods in Bio-Mechanics." Open Access Macedonian Journal of Medical Sciences 6, no. 4 (April 8, 2018): 742–46. http://dx.doi.org/10.3889/oamjms.2018.149.

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A novel partnership between surgeons and machines, made possible by advances in computing and engineering technology, could overcome many of the limitations of traditional surgery. By extending surgeons’ ability to plan and carry out surgical interventions more accurately and with fewer traumas, computer-integrated surgery (CIS) systems could help to improve clinical outcomes and the efficiency of healthcare delivery. CIS systems could have a similar impact on surgery to that long since realised in computer-integrated manufacturing. Mathematical modelling and computer simulation have proved tremendously successful in engineering.Computational mechanics has enabled technological developments in virtually every area of our lives. One of the greatest challenges for mechanists is to extend the success of computational mechanics to fields outside traditional engineering, in particular to biology, the biomedical sciences, and medicine. Biomechanics has significant potential for applications in orthopaedic industry, and the performance arts since skills needed for these activities are visibly related to the human musculoskeletal and nervous systems.Although biomechanics is widely used nowadays in the orthopaedic industry to design orthopaedic implants for human joints, dental parts, external fixations and other medical purposes, numerous researches funded by billions of dollars are still running to build a new future for sports and human healthcare in what is called biomechanics era.

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45

Sutariya, Vijaykumar, Anastasia Groshev, Prabodh Sadana, Deepak Bhatia, and Yashwant Pathak. "Artificial Neural Network in Drug Delivery and Pharmaceutical Research." Open Bioinformatics Journal 7, no. 1 (December 13, 2013): 49–62. http://dx.doi.org/10.2174/1875036201307010049.

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Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

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46

Taguchi, H., K. Yasuda, H. Aoyama, Y. Sawamura, J. Ikeda, K. Fujieda, Y. Iwasaki, and H. Shirato. "Low-Dose Craniospinal Irradiation (CSI) With Computed Tomographic (CT) Simulation and Ifosfamide, Cisplatin, and Etopiside for Non-Metastatic Embryonal Tumors in the Central Nervous System." International Journal of Radiation Oncology*Biology*Physics 69, no. 3 (November 2007): S241. http://dx.doi.org/10.1016/j.ijrobp.2007.07.1237.

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47

Grygoryan, Rafik D. "Modeling of Mechanisms Providing the Overall Control of Human Circulation." Advances in Human Physiology Research 4, no. 1 (July 5, 2022): 5. http://dx.doi.org/10.30564/ahpr.v4i1.4763.

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Multiple humoral and nervous mechanisms, each influencing the cardiovascular system (CVS) with its specific dynamics and power, had been evolutionarily saved both in animals and in human organisms. Most of such mechanisms are considered to be controllers of CVS’s function, but there is no concept clearly explaining the interaction of global and local controllers in intact human organisms under physiological or pathological conditions. Methodological and ethical constraints create practically insuperable obstacles while experiments on animals mainly concern artificial situations with certain switched-of mechanisms. Currently, mathematical modeling and computer simulations provide the most promising way for expanding and deepening our understanding of regulators’ interactions. As most of CVS’s models describe only partial control mechanisms, a special model (SM) capable of simulating every combination of control mechanisms is encouraged. This paper has three goals: i) to argue the uncial modeling concept and its physiological basis, ii) to describe SM, and iii) to give basic information about SM’s test research. SM describes human hemodynamics, which is under influence of arterial baroreceptor reflexes, peripheral chemoreceptor reflexes, central (CRAS) and local (lRAS) renin-angiotensin systems, local ischemia, and autoregulation of total brain flow. SM, performed in form of special software (SS), is tested under specific endogenous and/or exogenous alterations. The physiologist using SS can easily construct the desirable configuration of regulator mechanisms, their actual state, and scenarios of computer experiments. Tests illustrated the adequateness of SM, are the first step of SM’s research. Nuances of the interaction of modeled regulator mechanisms have to be illustrated in special publications.

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Liu, Zhaoming, Nailong Liu, Hongwei Wang, Shen Tian, Ning Bai, Feng Zhang, and Long Cui. "A New Type of Industrial Robot Trajectory Generation Component Based on Motion Modularity Technology." Journal of Robotics 2020 (January 28, 2020): 1–11. http://dx.doi.org/10.1155/2020/3196983.

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Motion modularity is the main method of motion control for higher animals. That means the complex movements of the muscles are made up of basic motion primitives, and the brain or central nervous system does not care about the specific details of the movement. However, the industrial robot control system does not adopt the technical roadmap of motion modularity, it generates complex trajectories by providing a large number of sampling points. This approach is equivalent to using the brain to directly guide the specific movement of the muscle and has to rely on a faster Fieldbus system to obtain complex motion trajectories. This work constructs a modularized industrial robot trajectory generation component based on Dynamic Movement Primitives (DMP) theory. With this component, the robot controller can generate complex trajectories without increasing the sampling points and can obtain good trajectory accuracy. Finally, the rationality of this system is proved by simulations and experiments.

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49

Madwed, J. B., and R. J. Cohen. "Heart rate response to hemorrhage-induced 0.05-Hz oscillations in arterial pressure in conscious dogs." American Journal of Physiology-Heart and Circulatory Physiology 260, no. 4 (April 1, 1991): H1248—H1253. http://dx.doi.org/10.1152/ajpheart.1991.260.4.h1248.

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We have previously reported that oscillations at 0.05 Hz can be generated by a simple computer model incorporating a negative-feedback reflex mechanism and an effector mechanism with a time delay. Computer simulations by inhibiting the vagal effector mechanism and activating the adrenergic effector mechanism elicited low-frequency oscillations at a frequency of 0.05 Hz in heart rate. We have observed that the cardiovascular system of the conscious dog, when stressed by the loss of blood, generates oscillations in arterial pressure and heart rate at a frequency of 0.05 Hz. We investigated in six conscious dogs the role of the sympathetic and parasympathetic nervous systems in generating these heart rate oscillations. During baseline conditions, the predominant peak in the arterial pressure and heart rate power spectra was located at the respiratory frequency, while the low-frequency oscillations were small. After a 30-ml/kg hemorrhage or after an 8-, 15-, or 30-ml/kg hemorrhage with glycopyrrolate, a muscarinic-blocking agent, low-frequency oscillations at a frequency of 0.05 Hz predominated, while the respiratory frequency oscillations were negligible. Since respiratory frequency oscillations have been reported to reflect vagal activity, and since the low-frequency oscillations were present after vagal blockade, these hemorrhage-induced low-frequency oscillations in heart rate may be primarily mediated by the cardiac sympathetic nerves. Also cross-correlation analysis between arterial pressure and heart rate showed that a change in arterial pressure caused an opposite change in heart rate with a delay of 2-5 s. We conclude that hemorrhage-induced oscillations in heart rate at 0.05 Hz represent the arterial baroreceptor-beta-sympathetic reflex response to underlying arterial pressure oscillations.

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50

Pan, Yu Min, Quan Zhu Zhang, and Peng Qian Xue. "The Method and Application of Time Series Prediction Based Wavelet Neural Network." Advanced Materials Research 328-330 (September 2011): 2312–17. http://dx.doi.org/10.4028/www.scientific.net/amr.328-330.2312.

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This paper presents a research on modeling and prediction with wavelet neural network in the nonlinear time series of gas emitted. Because accurately predicting the amount of gas emitted from the mine is a very important matter for safety, this paper proposes a new algorithm of wavelet neural network model for time series gas emission prediction. The nervous cells function is the basis of nonlinear wavelets. A wavelet network composed by the wavelet basis function is computed by an expansion and contraction factor and a translation factor to reach the global best approximation effect. Which wavelet basis function has the features of extraction capabilities, self-learning neural network and wavelet transform of the localized nature. The intrinsic defects of artificial BP neural network, e.g., its slow learning speed, difficulty to determine rationally the network structure and existence of partial minimum points, are solved. The simulation results obtained show that the new prediction system has faster convergence and more accurate prediction.

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