Relevant bibliographies by topics / Neurons - Modeling

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

Academic literature on the topic 'Neurons - Modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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Journal articles on the topic "Neurons - Modeling"

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JOHNSTON, DAVID, SIMON PETER MEKHAIL, MARY ANN GO, and VINCENT R. DARIA. "MODELING NEURONAL RESPONSE TO SIMULTANEOUS AND SEQUENTIAL MULTI-SITE SYNAPTIC STIMULATION." International Journal of Modern Physics: Conference Series 17 (January 2012): 1–8. http://dx.doi.org/10.1142/s2010194512007878.

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The flow of information in the brain theorizes that each neuron in a network receives synaptic inputs and sends off its processed signals to neighboring neurons. Here, we model these synaptic inputs to understand how each neuron processes these inputs and transmits neurotransmitters to neighboring neurons. We use the NEURON simulation package to stimulate a neuron at multiple synaptic locations along its dendritic tree. Accumulation of multiple synaptic inputs causes changes in the neuron's membrane potential leading to firing of an action potential. Our simulations show that simultaneous syna
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Wang, Wei, Jiqing Han, Tieran Zheng, Guibin Zheng, and Xingyu Zhou. "Speaker Verification via Modeling Kurtosis Using Sparse Coding." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 03 (2016): 1659008. http://dx.doi.org/10.1142/s0218001416590084.

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This paper proposes a new model for speaker verification by employing kurtosis statistical method based on sparse coding of human auditory system. Since only a small number of neurons in primary auditory cortex are activated in encoding acoustic stimuli and sparse independent events are used to represent the characteristics of the neurons. Each individual dictionary is learned from individual speaker samples where dictionary atoms correspond to the cortex neurons. The neuron responses possess statistical properties of acoustic signals in auditory cortex so that the activation distribution of i
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Nykamp, Duane Q., and Daniel Tranchina. "A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Extension to Slow Inhibitory Synapses." Neural Computation 13, no. 3 (2001): 511–46. http://dx.doi.org/10.1162/089976601300014448.

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A previously developed method for efficiently simulating complex networks of integrate-and-fire neurons was specialized to the case in which the neurons have fast unitary postsynaptic conductances. However, inhibitory synaptic conductances are often slower than excitatory ones for cortical neurons, and this difference can have a profound effect on network dynamics that cannot be captured with neurons that have only fast synapses. We thus extend the model to include slow inhibitory synapses. In this model, neurons are grouped into large populations of similar neurons. For each population, we ca
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KINOUCHI, OSAME, and MARCELO H. R. TRAGTENBERG. "MODELING NEURONS BY SIMPLE MAPS." International Journal of Bifurcation and Chaos 06, no. 12a (1996): 2343–60. http://dx.doi.org/10.1142/s0218127496001508.

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We introduce a simple generalization of graded response formal neurons which presents very complex behavior. Phase diagrams in full parameter space are given, showing regions with fixed points, periodic, quasiperiodic and chaotic behavior. These diagrams also represent the possible time series learnable by the simplest feed-forward network, a two input single-layer perceptron. This simple formal neuron (‘dynamical perceptron’) behaves as an excitable ele ment with characteristics very similar to those appearing in more complicated neuron models like FitzHugh-Nagumo and Hodgkin-Huxley systems:
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Sun, Yu-Juan, and Wei-Min Zhang. "Modeling Neuronal Systems as an Open Quantum System." Symmetry 13, no. 9 (2021): 1603. http://dx.doi.org/10.3390/sym13091603.

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We propose a physical model for neurons to describe how neurons interact with one another through the surrounding materials of neuronal cell bodies. We model the neuronal cell surroundings, include the dendrites, the axons and the synapses, as well as the surrounding glial cells, as a continuous distribution of oscillating modes inspired from the electric circuital picture of neuronal action potential. By analyzing the dynamics of this neuronal model by using the master equation approach of open quantum systems, we investigated the collective behavior of neurons. After applying stimulations to
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Almog, Mara, and Alon Korngreen. "Is realistic neuronal modeling realistic?" Journal of Neurophysiology 116, no. 5 (2016): 2180–209. http://dx.doi.org/10.1152/jn.00360.2016.

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Scientific models are abstractions that aim to explain natural phenomena. A successful model shows how a complex phenomenon arises from relatively simple principles while preserving major physical or biological rules and predicting novel experiments. A model should not be a facsimile of reality; it is an aid for understanding it. Contrary to this basic premise, with the 21st century has come a surge in computational efforts to model biological processes in great detail. Here we discuss the oxymoronic, realistic modeling of single neurons. This rapidly advancing field is driven by the discovery
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Fan, Yile, Yuanpeng Li, Naiyang Xue, and Dan Ding. "Analysis of Regularized Poisson GLM Spike-Train Modeling." Journal of Physics: Conference Series 2173, no. 1 (2022): 012019. http://dx.doi.org/10.1088/1742-6596/2173/1/012019.

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Abstract This paper introduces a method for modeling and analyzing neural impulse sequences. In this paper, we define the response value of a scale-independent neuron and construct the correlation graph of the neuron under the response value. The minimum cut algorithm is applied continuously to obtain the maximum group of neurons. According to the characteristics of the firing of neurons, a Poisson-process based model is proposed to mathematically model the neural coding, and the gradient descent method is used to optimize it. Through the modeling analysis method, information such as maximum n
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Chambers, Jordan D., Joel C. Bornstein, Henrik Sjövall, and Evan A. Thomas. "Recurrent networks of submucous neurons controlling intestinal secretion: a modeling study." American Journal of Physiology-Gastrointestinal and Liver Physiology 288, no. 5 (2005): G887—G896. http://dx.doi.org/10.1152/ajpgi.00491.2004.

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Secretomotor neurons, immunoreactive for vasoactive intestinal peptide (VIP), are important in controlling chloride secretion in the small intestine. These neurons form functional synapses with other submucosal VIP neurons and transmit via slow excitatory postsynaptic potentials (EPSPs). Thus they form a recurrent network with positive feedback. Intrinsic sensory neurons within the submucosa are also likely to form recurrent networks with positive feedback, provide substantial output to VIP neurons, and receive input from VIP neurons. If positive feedback within recurrent networks is sufficien
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Todo, Yuki, Zheng Tang, Hiroyoshi Todo, Junkai Ji, and Kazuya Yamashita. "Neurons with Multiplicative Interactions of Nonlinear Synapses." International Journal of Neural Systems 29, no. 08 (2019): 1950012. http://dx.doi.org/10.1142/s0129065719500126.

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Neurons are the fundamental units of the brain and nervous system. Developing a good modeling of human neurons is very important not only to neurobiology but also to computer science and many other fields. The McCulloch and Pitts neuron model is the most widely used neuron model, but has long been criticized as being oversimplified in view of properties of real neuron and the computations they perform. On the other hand, it has become widely accepted that dendrites play a key role in the overall computation performed by a neuron. However, the modeling of the dendritic computations and the assi
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Price, N. S. C., S. Ono, M. J. Mustari, and M. R. Ibbotson. "Comparing Acceleration and Speed Tuning in Macaque MT: Physiology and Modeling." Journal of Neurophysiology 94, no. 5 (2005): 3451–64. http://dx.doi.org/10.1152/jn.00564.2005.

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Studies of individual neurons in area MT have traditionally investigated their sensitivity to constant speeds. We investigated acceleration sensitivity in MT neurons by comparing their responses to constant steps and linear ramps in stimulus speed. Speed ramps constituted constant accelerations and decelerations between 0 and 240°/s. Our results suggest that MT neurons do not have explicit acceleration sensitivity, although speed changes affected their responses in three main ways. First, accelerations typically evoked higher responses than the corresponding deceleration rate at all rates test
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Dissertations / Theses on the topic "Neurons - Modeling"

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Kuebler, Eric Stephen. "Harnessing the Variability of Neuronal Activity: From Single Neurons to Networks." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37855.

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Neurons and networks of the brain may use various strategies of computation to provide the neural substrate for sensation, perception, or cognition. To simplify the scenario, two of the most commonly cited neural codes are firing rate and temporal coding, whereby firing rates are typically measured over a longer duration of time (i.e., seconds or minutes), and temporal codes use shorter time windows (i.e., 1 to 100 ms). However, it is possible that neurons may use other strategies. Here, we highlight three methods of computation that neurons, or networks, of the brain may use to encode and/or
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Landucci, Elisa. "Modeling Rett syndrome with iPSCs-derived neurons." Doctoral thesis, Università di Siena, 2018. http://hdl.handle.net/11365/1051069.

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Rett syndrome is a severe neurodevelopmental disorder. The condition affects approximately one in every 10.000 females and is only rarely seen in males. Causative mutations in the transcriptional regulator MeCP2 have been identified in more than 95% of classic Rett patients; mutations in CDKL5 are responsible for the early onset seizures Rett variant and mutations in FOXG1 gene lead to the congenital Rett variant. To shed light on molecular mechanisms underlying Rett syndrome onset and progression in disease-relevant cells, we took advantage of the breakthrough genetic reprogramming technology
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Chen, Jen-Yung. "Multi-compartment modeling in the gustatory system in rats." Diss., Online access via UMI:, 2005.

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Berwald, Emil. "Towards mesoscopic modeling of firing neurons: a feasibility study." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-222876.

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Ion channel models are related to non-equilibrium statistical physics, fluid mechanics and electromagnetism. Some classes of ordinary differential equations that model ion channels can be seen as a limit of finite state-space continuous-time Markov chains. The purpose of this thesis is to qualitatively investigate the numerical results of systems of equations that incorporate ion channels modeled by such Markov chains and an electrical circuit model of a single neuron with isopotential extracellular space. This may be useful for making more detailed micro-physical simulations of neurons. A sub
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Lim, Hun Ki. "COMPUTATIONAL MODELING OF MULITSENSORY PROCESSING USING NETWORK OF SPIKING NEURONS." VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/2523.

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Multisensory processing in the brain underlies a wide variety of perceptual phenomena, but little is known about the underlying mechanisms of how multisensory neurons are generated and how the neurons integrate sensory information from environmental events. This lack of knowledge is due to the difficulty of biological experiments to manipulate and test the characteristics of multisensory processing. By using a computational model of multisensory processing this research seeks to provide insight into the mechanisms of multisensory processing. From a computational perspective, modeling of brain
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Nadkarni, Suhita. "Dynamics of Dressed Neurons: Modeling the Neural-Glial Circuit and Exploring its Normal and Pathological Implications." Ohio : Ohio University, 2005. http://www.ohiolink.edu/etd/view.cgi?ohiou1125689320.

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Petersson, Marcus. "Dendritic and axonal ion channels supporting neuronal integration : From pyramidal neurons to peripheral nociceptors." Doctoral thesis, KTH, Beräkningsbiologi, CB, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-102362.

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The nervous system, including the brain, is a complex network with billions of complex neurons. Ion channels mediate the electrical signals that neurons use to integrate input and produce appropriate output, and could thus be thought of as key instruments in the neuronal orchestra. In the field of neuroscience we are not only curious about how our brains work, but also strive to characterize and develop treatments for neural disorders, in which the neuronal harmony is distorted. By modulating ion channel activity (pharmacologically or otherwise) it might be possible to effectively restore neur
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Karam, Philippe Chucri. "Modeling passive and active mechanisms in motoneuron dendrites." Thesis, Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/13713.

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Garcia, Paul Anthony. "Modeling the Intersegmental Coordination of Heart Motor Neurons in the Medicinal Leech." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/5064.

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We constructed a model of the coordination of segmental heart motor neurons driving blood circulation in leeches. The heart motor neuron models were conductance-based; conductances of voltage-gated and synaptic currents were adjusted to match the firing pattern of heart motor neurons from the living system. Each motor neuron receives a specific pattern of inhibitory input from rhythmic premotor heart interneurons and translates this spatiotemporal pattern into the fictive heartbeat motor pattern. The temporal pattern of synaptic input to the model was derived from extracellularly recorded spi
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Shepardson, Dylan. "Algorithms for inverting Hodgkin-Huxley type neuron models." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31686.

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Thesis (Ph.D)--Algorithms, Combinatorics, and Optimization, Georgia Institute of Technology, 2010.<br>Committee Chair: Tovey, Craig; Committee Member: Butera, Rob; Committee Member: Nemirovski, Arkadi; Committee Member: Prinz, Astrid; Committee Member: Sokol, Joel. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Books on the topic "Neurons - Modeling"

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Pacut, Andrzej. Stochastic modeling at diverse scales: From Poisson to network neurons. Oficyna Wydawnicza Politechniki Warszawskiej, 2000.

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R, Baylog Louis, ed. Dendritic spines biochemistry, modeling and properties. Nova Science Publishers, 2009.

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Christof, Koch, and Segev Idan, eds. Methods in neuronal modeling: From synapses to networks. MIT Press, 1992.

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An introduction to the mathematics of neurons: Modeling in the frequency domain. 2nd ed. Cambridge University Press, 1997.

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1956-, Koch Christof, and Segev Idan, eds. Methods in neuronal modeling: From synapses to networks. MIT Press, 1989.

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Beňušková, L̕. Computational neurogenetic modeling. Springer, 2007.

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N, Reeke George, ed. Modeling in the neurosciences: From biological systems to neuromimetic robotics. 2nd ed. Taylor & Francis, 2005.

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1956-, Koch Christof, and Segev Idan, eds. Methods in neuronal modeling: From ions to networks. 2nd ed. MIT Press, 1998.

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R, Poznanski Roman, ed. Modeling in the neurosciences: From ionic channels to neural networks. Harwood Academic Publishers, 1999.

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Börgers, Christoph. An Introduction to Modeling Neuronal Dynamics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51171-9.

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Book chapters on the topic "Neurons - Modeling"

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Buccino, Alessio Paolo, Miroslav Kuchta, Jakob Schreiner, and Kent-André Mardal. "Improving Neural Simulations with the EMI Model." In Modeling Excitable Tissue. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61157-6_7.

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Abstract Mathematical modeling of neurons is an essential tool to investigate neuronal activity alongside with experimental approaches. However, the conventional modeling framework to simulate neuronal dynamics and extracellular potentials makes several assumptions that might need to be revisited for some applications. In this chapter we apply the EMI model to investigate the ephaptic effect and the effect of the extracellular probes on the measured potential. Finally, we introduce reduced EMI models, which provide a more computationally efficient framework for simulating neurons with complex morphologies.
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Makarov, Sergey N., Jyrki Ahveninen, Matti Hämäläinen, Yoshio Okada, Gregory M. Noetscher, and Aapo Nummenmaa. "Multiscale Modeling of EEG/MEG Response of a Compact Cluster of Tightly Spaced Pyramidal Neocortical Neurons." In Brain and Human Body Modeling 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45623-8_11.

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AbstractIn this study, the boundary element fast multipole method or BEM-FMM is applied to model compact clusters of tightly spaced pyramidal neocortical neurons firing simultaneously and coupled with a high-resolution macroscopic head model. The algorithm is capable of processing a very large number of surface-based unknowns along with a virtually unlimited number of elementary microscopic current dipole sources distributed within the neuronal arbor.The realistic cluster size may be as large as 10,000 individual neurons, while the overall computation times do not exceed several minutes on a standard server. Using this approach, we attempt to establish how well the conventional lumped-dipole model used in electroencephalography/magnetoencephalography (EEG/MEG) analysis approximates a compact cluster of realistic neurons situated either in a gyrus (EEG response dominance) or in a sulcus (MEG response dominance).
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Murphey, Carey R. "Hypertext Software for Modeling Neurons." In The Neurobiology of Computation. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2235-5_16.

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Schierwagen, Andreas. "Mathematical and Computational Modeling of Neurons and Neuronal Ensembles." In Computer Aided Systems Theory - EUROCAST 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04772-5_22.

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Middlebrooks, John C. "Location Coding by Auditory Cortical Neurons." In Central Auditory Processing and Neural Modeling. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5351-9_13.

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Börgers, Christoph. "Model Neurons of Bifurcation Type 1." In An Introduction to Modeling Neuronal Dynamics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51171-9_12.

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Börgers, Christoph. "Model Neurons of Bifurcation Type 2." In An Introduction to Modeling Neuronal Dynamics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51171-9_14.

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Börgers, Christoph. "Model Neurons of Bifurcation Type 3." In An Introduction to Modeling Neuronal Dynamics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51171-9_16.

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Börgers, Christoph. "Linear Integrate-and-Fire (LIF) Neurons." In An Introduction to Modeling Neuronal Dynamics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51171-9_7.

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Bardakjian, Berj L., W. Neil Wright, Taufik A. Valiante, and Peter L. Carlen. "Nonlinear System Identification of Hippocampal Neurons." In Advanced Methods of Physiological System Modeling. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4757-9024-5_9.

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Conference papers on the topic "Neurons - Modeling"

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Sarma, Sridevi, Sabato Santaniello, and Uri Eden. "Modeling, estimation and control of neurons and neuronal networks." In 2014 22nd Mediterranean Conference of Control and Automation (MED). IEEE, 2014. http://dx.doi.org/10.1109/med.2014.6961539.

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McCormick, Bruce H., and K. Mulchandani. "L-system modeling of neurons." In Visualization in Biomedical Computing 1994, edited by Richard A. Robb. SPIE, 1994. http://dx.doi.org/10.1117/12.185231.

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Rasamuel, Marino, Daniel Gaffe, Timothee Levi, and Benoit Miramond. "Synchronous approach for modeling spiking neurons." In 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2019. http://dx.doi.org/10.1109/biocas.2019.8919084.

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Sengor, N. Serap, Yusuf Kuyumcu, and Koray Ciflci. "Modeling cortical states by spiking neurons." In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014. http://dx.doi.org/10.1109/siu.2014.6830461.

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Pitta, Marina Galdino da Rocha, Jordy Silva de Carvalho, Luzilene Pereira de Lima, and Ivan da Rocha Pitta. "iPSC therapies applied to rehabilitation in parkinson’s disease." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.022.

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Background: Parkinson’s disease (PD) is a neurological disorder that affects movement, mainly due to damage and degeneration of the nigrostriatal dopaminergic pathway. The diagnosis is made through a clinical neurological analysis where motor characteristics are considered. There is still no cure, and treatment strategies are focused on symptoms control. Cell replacement therapies emerge as an alternative. Objective: This review focused on current techniques of induced pluripotent stem cells (iPSCs). Methods: The search terms used were: “Parkinson’s Disease”, “Stem cells” and “iPSC”. Open arti
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Medini, Chaitanya, Anjitha Thekkekuriyadi, Surya Thayyilekandi, Manjusha Nair, Bipin Nair, and Shyam Diwakar. "Modeling basal ganglia microcircuits using spiking neurons." In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2016. http://dx.doi.org/10.1109/icacci.2016.7732383.

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Liu, Zhiheng, and Xia Shi. "Modeling of Synchronous Behaviors of Excitatory and Inhibitory Neurons in Complex Neuronal Networks." In 2018 IEEE 4th International Conference on Computer and Communications (ICCC). IEEE, 2018. http://dx.doi.org/10.1109/compcomm.2018.8780741.

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Opsal, Nathaniel, Pete Canfield, Tyler Banks, and Satish S. Nair. "An Efficient Pipeline for Biophysical Modeling of Neurons." In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2021. http://dx.doi.org/10.1109/ner49283.2021.9441222.

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Franklin, C. C., Y. Chen, V. S. K. Guntu, and S. S. Nair. "Reduced Order Modeling of Neurons for Network Simulations." In ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. ASME, 2012. http://dx.doi.org/10.1115/dscc2012-movic2012-8718.

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Smaragdos, Georgios, Sebastian Isaza, Martijn F. van Eijk, Ioannis Sourdis, and Christos Strydis. "FPGA-based biophysically-meaningful modeling of olivocerebellar neurons." In FPGA'14: The 2014 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. ACM, 2014. http://dx.doi.org/10.1145/2554688.2554790.

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Reports on the topic "Neurons - Modeling"

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Markova, Oksana, Serhiy Semerikov та Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, 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 sp
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Esfahani, Naser Madani, Behzad Heidari, Hamed Boustanzar, and Ghasem Sattari. Modeling of Uniaxial Compressive Strength via Genetic Programming and Neuro-Fuzzy. Cogeo@oeaw-giscience, 2011. http://dx.doi.org/10.5242/iamg.2011.0304.

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