Relevant bibliographies by topics / Hodgkin-Huxley model

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Academic literature on the topic 'Hodgkin-Huxley model'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 April 2022

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Journal articles on the topic "Hodgkin-Huxley model"

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He, Ji-Huan. "A modified Hodgkin–Huxley model." Chaos, Solitons & Fractals 29, no. 2 (July 2006): 303–6. http://dx.doi.org/10.1016/j.chaos.2005.08.144.

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Guckenheimer, John, and Ricardo A. Oliva. "Chaos in the Hodgkin--Huxley Model." SIAM Journal on Applied Dynamical Systems 1, no. 1 (January 2002): 105–14. http://dx.doi.org/10.1137/s1111111101394040.

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McCormick, David A., Yousheng Shu, and Yuguo Yu. "Hodgkin and Huxley model — still standing?" Nature 445, no. 7123 (January 2007): E1—E2. http://dx.doi.org/10.1038/nature05523.

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Cano, Gaspar, and Rui Dilão. "Intermittency in the Hodgkin-Huxley model." Journal of Computational Neuroscience 43, no. 2 (June 14, 2017): 115–25. http://dx.doi.org/10.1007/s10827-017-0653-9.

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Wang, Lin, Ying Jie Wang, Xiao Yu Chen, and Xiao Qiang Liang. "Soliton Solutions and Applications on Neuronal Hodgkin-Huxley Model." Applied Mechanics and Materials 411-414 (September 2013): 3265–68. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.3265.

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from one neuron to another neuron fibers generate action potentials of nerve cells, leading to nervous excitement along. Hodgkin - Huxley neuron model has been used to solve many physiological phenomenon. This paper presents Neuralfiber conduction theory, based on the Hodgkin - Huxley model, considering the propagation of nerve impulses nerve fiber soliton solutions, and to further discuss the application of numerical results are two aspects raised.
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Bazsó, Fülöp, László Zalányi, and Gábor Csárdi. "Channel noise in Hodgkin–Huxley model neurons." Physics Letters A 311, no. 1 (May 2003): 13–20. http://dx.doi.org/10.1016/s0375-9601(03)00454-7.

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Coutin, Laure, Jean-Marc Guglielmi, and Nicolas Marie. "On a fractional stochastic Hodgkin–Huxley model." International Journal of Biomathematics 11, no. 05 (July 2018): 1850061. http://dx.doi.org/10.1142/s1793524518500614.

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The model studied in this paper is a stochastic extension of the so-called neuron model introduced by Hodgkin and Huxley. In the sense of rough paths, the model is perturbed by a multiplicative noise driven by a fractional Brownian motion, with a vector field satisfying the viability condition of Coutin and Marie for [Formula: see text]. An application to the modeling of the membrane potential of nerve fibers damaged by a neuropathy is provided.
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Shama, Farzin, Saeed Haghiri, and Mohammad Amin Imani. "FPGA Realization of Hodgkin-Huxley Neuronal Model." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 5 (May 2020): 1059–68. http://dx.doi.org/10.1109/tnsre.2020.2980475.

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Naundorf, Björn, Fred Wolf, and Maxim Volgushev. "Hodgkin and Huxley model — still standing? (Reply)." Nature 445, no. 7123 (January 2007): E2—E3. http://dx.doi.org/10.1038/nature05534.

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Shorten, P. "A Hodgkin–Huxley Model Exhibiting Bursting Oscillations." Bulletin of Mathematical Biology 62, no. 4 (July 2000): 695–715. http://dx.doi.org/10.1006/bulm.2000.0172.

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Dissertations / Theses on the topic "Hodgkin-Huxley model"

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Pu, Shusen. "Noise Decomposition for Stochastic Hodgkin-Huxley Models." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1605789507246466.

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Thompson, Ian M. "Artificial neural networks in medicine : theory and application in biomedical systems." Thesis, University of Newcastle Upon Tyne, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262994.

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Du, Toit Francois. "Control analysis of the action potential and its propagation in the Hodgkin-Huxley model." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/5294.

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Thesis (MSc (Biochemistry))--University of Stellenbosch, 2010.
ENGLISH ABSTRACT: The Hodgkin-Huxley model, created in 1952, was one of the first models in computational neuroscience and remains the best studied neuronal model to date. Although many other models have a more detailed system description than the Hodgkin-Huxley model, it nonetheless gives an accurate account of various high-level neuronal behaviours. The fields of computational neuroscience and Systems Biology have developed as separate disciplines for a long time and only fairly recently has the neurosciences started to incorporate methods from Systems Biology. Metabolic Control Analysis (MCA), a Systems Biology tool, has not been used in the neurosciences. This study aims to further bring these two fields together, by testing the feasibility of an MCA approach to analyse the Hodgkin-Huxley model. In MCA it is not the parameters of the system that are perturbed, as in the more traditional sensitivity analysis, but the system processes, allowing the formulation of summation and connectivity theorems. In order to determine if MCA can be performed on the Hodgkin-Huxley model, we identified all the discernable model processes of the neuronal system. We performed MCA and quantified the control of the model processes on various high-level time invariant system observables, e.g. the action potential (AP) peak, firing threshold, propagation speed and firing frequency. From this analysis we identified patterns in process control, e.g. the processes that would cause an increase in sodium current, would also cause the AP threshold to lower (decrease its negative value) and the AP peak, propagation speed and firing frequency to increase. Using experimental inhibitor titrations from literature we calculated the control of the sodium channel on AP characteristics and compared it with control coefficients derived from our model simulation. Additionally, we performed MCA on the model’s time-dependent state variables during an AP. This revealed an intricate linking of the system variables via the membrane potential. We developed a method to quantify the contribution of the individual feedback loops in the system. We could thus calculate the percentage contribution of the sodium, potassium and leak currents leading to the observed global change after a system perturbation. Lastly, we compared ion channel mutations to our model simulations and showed how MCA can be useful in identifying targets to counter the effect of these mutations. In this thesis we extended the framework of MCA to neuronal systems and have successfully applied the analysis framework to quantify the contribution of the system processes to the model behaviour.
AFRIKAANSE OPSOMMINMG: Die Hodgkin-Huxley-model, wat in 1952 ontwikkel is, was een van die eerste modelle in rekenaarmagtige neurowetenskap en is vandag steeds een van die bes-bestudeerde neuronmodelle. Hoewel daar vele modelle bestaan met ’n meer uitvoerige sisteembeskrywing as die Hodgkin-Huxley-model gee dié model nietemin ’n akkurate beskrywing van verskeie hoëvlak-sisteemverskynsels. Die twee velde van sisteembiologie en neurowetenskap het lank as onafhanklike dissiplines ontwikkel en slegs betreklik onlangs het die veld van neurowetenskap begin om metodes van sisteembiologie te benut. ’n Sisteembiologiemetode genaamd metaboliese kontrole-analise (MKA) is tot dusver nog nie in die neurowetenskap gebruik nie. Hierdie studie het gepoog om die twee velde nader aan mekaar te bring deurdat die toepasbaarheid van die MKA-raamwerk op die Hodgkin-Huxley-model getoets word. In MKA is dit nie die parameters van die sisteem wat geperturbeer word soos in die meer tradisionele sensitiwiteitsanalise nie, maar die sisteemprosesse. Dit laat die formulering van sommasie- en konnektiwiteitsteoremas toe. Om die toepasbaarheid van die MKA-raamwerk op die Hodgkin-Huxleymodel te toets, is al die onderskeibare modelprosesse van die neurale sisteem geïdentifiseer. Ons het MKA toegepas en die kontrole van die model-prosesse op verskeie hoëvlak, tydsonafhanklike waarneembare sisteemvlak-eienskappe, soos die aksiepotensiaal-kruin, aksiepotensiaal-drempel, voortplantingspoed en aksiepotensiaal-frekwensie, gekwantifiseer. Vanuit hierdie analise kon daar patrone in die proseskontrole geïdentifiseer word, naamlik dat die prosesse wat ’n toename in die natriumstroom veroorsaak, ook sal lei tot ’n afname in die aksiepotensiaal-drempel (die negatiewe waarde verminder) en tot ’n toename in die aksiepotensiaal-kruin, voortplantingspoed en aksiepotensiaalfrekwensie. Deur gebruik te maak van eksperimentele stremmer-titrasies vanuit die literatuur kon die kontrole van die natriumkanaal op die aksiepotensiaaleienskappe bereken en vergelyk word met die kontrole-koëffisiënte vanuit die modelsimulasie. Ons het ook MKA op die model se tydsafhanklike veranderlikes deur die verloop van die aksiepotensiaal uitgevoer. Die analise het getoon dat die sisteemveranderlikes ingewikkeld verbind is via die membraanpotensiaal. Ons het ’n metode ontwikkel om die bydrae van die individuele terugvoerlusse in die sisteem te kwantifiseer. Die persentasie-bydrae van die natrium-, kalium- en lekstrome wat tot die waarneembare globale verandering ná ’n sisteemperturbasie lei, kon dus bepaal word. Laastens het ons ioonkanaalmutasies met ons modelsimulasies vergelyk en getoon hoe MKA nuttig kan wees in die identifisering van teikens om die effek van hierdie mutasies teen te werk. In hierdie tesis het ons die raamwerk van MKA uitgebrei na neurale sisteme en die analise-raamwerk suksesvol toegepas om die bydrae van die sisteemprosesse tot die modelgedrag te kwantifiseer.
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Rameh, Raffael Bechara. "Aproximações dos modelos de Hodgkin-Huxley e FitzHugh-Nagumo usando equações diferenciais com atraso." Universidade Federal de Juiz de Fora (UFJF), 2018. https://repositorio.ufjf.br/jspui/handle/ufjf/8081.

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Para representar diferentes fenômenos e sistemas modelos matemáticos são largamente utilizados. Muitos deles são fundamentados em sistemas de equações diferenciais ordinárias (EDOs), isto é, baseiam-se em conjuntos de igualdades que envolvem variáveis dependentes, suas derivadas de primeira ordem e a variável independente. Neste trabalho, estudamos a modelagem da geração do potencial de ação em células excitáveis, como os neurônios. Existem dois modelos tradicionais e pioneiros que se destacam nessa área: Hodgkin-Huxley e FitzHugh-Nagumo. O objetivo desta dissertação é avaliar a possibilidade de modelar a geração do potencial de ação via uma única equação diferencial com atraso. Equações diferenciais com atraso são importantes por sua capacidade em reproduzir uma grande diversidade de fenômenos. Porém, seu uso na modelagem do potencial de ação de células excitáveis é ainda incipiente. Nesta dissertação, o método usado para alcançar este objetivo se baseou no desenvolvimento, inicialmente, de uma equação integro-diferencial que aproxima o sistema de EDOs. Em seguida, desenvolvemos uma aproximação para as integrais que usa termos tanto no instante atual quanto em instante anteriores, i.e., atrasados no tempo. Dessa forma, mostramos que é possível aproximar cada um dos sistemas de EDOS dos modelos de Hodgkin-Huxley e FitzHugh-Nagumo por uma única equação diferencial com atraso. Por fim, estes novos modelos são comparados com os originais, e são apontadas direções para a continuidade desta pesquisa.
To represent different phenomena and systems mathematical models are widely used. Many of them are based on systems of ordinary differential equations (ODEs), that is, they are based on sets of equalities involving dependent variables, their derivatives of first order and the independent variable. In this work, we study the modeling of action potential generation in excitable cells, such as neurons. There are two traditional and pioneering models that stand out in this area: Hodgkin-Huxley and FitzHugh-Nagumo. The objective of this dissertation is to evaluate the possibility of modeling the generation of the action potential via a single differential equation with delay. Differential equations with delay are important because of their capacity to reproduce a great diversity of phenomena. However, its use in modeling the action potential of excitable cells is still incipient. In this dissertation, the method used to achieve this goal was based on the development, initially, of an integral-differential equation that approximates the ODE system. Next, we develop an approximation for integrals that uses terms at both the current instant and the previous instant, i.e., time delayed. Thus, we show that it is possible to approximate each of the ODEs systems of the Hodgkin-Huxley and FitzHugh-Nagumo models by a single differential equation with delay. Finally, these new models are compared with the original ones, and directions are indicated for future works.
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Borges, Rafael Ribaski. "PLASTICIDADE SINAPTICA EM REDES NEURONAIS." UNIVERSIDADE ESTADUAL DE PONTA GROSSA, 2016. http://tede2.uepg.br/jspui/handle/prefix/860.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
In this thesis, it was investigated the influence of synaptic plasticity in the dynamics of neuronal networks. Specifically, we analyzed the effect on creation and suppression of synchronization spikes in networks composed of neurons with excitatory and inhibitory synapses. The spike timing-dependent plasticity (STDP) changes the strength of existing synapses in the neuronal network. To simulate the dynamics of each neuron, we considered the Hodgkin and Huxley model (HH), that is able to provide the main features of the temporal evolution of the membrane potential of each cell. The Kuramoto order parameter was utilized as synchronization diagnostic. First of all, we have studied the dynamics spikes in neuronal networks on a global and random topology with excitatory synapses with plasticity (STDP). It was observed that the STDP improves synchronization in a sufficiently dense neuronal networks. However, this effect is maximized by the insertion of an external perturbation of moderate intensity. Further, the system behavior was analyzed using the combination of inhibitory and excitatory synapses, both with spike timing-dependent plasticity. Our results indicated that the network becomes desynchronized when the intensity of inhibitory synapses is increased. Nevertheless, for small intensities of these synapses there was an increase in the values of the order parameter when the system with STDP was perturbed.
Nesta tese foi investigada a influência dos modelos de plasticidade sináptica na dinâmica de redes neuronais. Especificamente, foi analisado o efeito da plasticidade na criação e supressão da sincronização ao de disparos em redes compostas por neurônios com sinapses excitatórias e inibitórias. O modelo de plasticidade sináptica dependente do tempo entre disparos (do inglês: Spike-timing-dependent plasticity: STDP), modifica a intensidade das sinapses existentes na rede neuronal. Para simular a dinâmica de cada neurônio foi utilizado o modelo de Hodgkin e Huxley (HH), que ´e capaz de fornecer as principais características da evolução ao temporal do potencial de membrana de cada célula. Como diagnóstico de sincronização foi utilizado o parâmetro de ordem de Kuramoto. Primeiramente foi investigada a dinâmica de disparos em redes neuronais com topologia global e aleatória com sinapses excitatórias com plasticidade (STDP). Observou-se que a STDP contribui para a sincronização ao do sistema em redes neuronais suficientemente densas. No entanto, este efeito é maximizado com a inserção de uma perturbação o externa de intensidade moderada. Na sequência, foi analisado o comportamento do sistema com a combinação de sinapses excitatórias e inibitórias, ambas com STDP. Os resultados indicaram que a rede torna-se não sincronizada com o aumento da intensidade das sinapses inibitórias. Entretanto, para pequenas intensidades destas sinapses, observou-se um acréscimo nos valores do parâmetro de ordem quando o sistema com STDP foi perturbado.
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Vähäsöyrinki, M. (Mikko). "Voltage-gated K+ channels in Drosophila photoreceptors:biophysical study of neural coding." Doctoral thesis, University of Oulu, 2004. http://urn.fi/urn:isbn:9514275993.

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Abstract The activity of neurons is critically dependent upon the suite of voltage-dependent ion channels expressed in their membranes. In particular, voltage-gated K+ channels are extremely diverse in their function, contributing to the regulation of distinct aspects of neuronal activity by shaping the voltage responses. In this study the role of K+ channels in neural coding is investigated in Drosophila photoreceptors by using biophysical models with parameters derived from the electrophysiological experiments. Due to their biophysical properties, the Shaker channels attenuate the fast transients and amplify the slower signal components, enabling photoreceptors to use their voltage range more effectively. Slow delayed rectifier channels, shown to be encoded by the Shab gene, activate at high light intensities, thereby attenuating the light-induced depolarization and preventing response saturation. Activation of Shab channels also reduces the membrane time constant making it possible to encode faster events. Interactions between the voltage-gated K+ channels and the currents generated by the light induced conductance (LIC) were investigated during naturalistic stimulation in wild type and Shaker mutant photoreceptors. It is shown that in addition to eliminating the Shaker current, the mutation increased the Shab current and affected the current flowing through the LIC. Part of these changes could be attributed to direct feedback from the Shaker channels via the membrane potential. However, it is suggested that also other changes may occur in the LIC due to mutation in K+ channels, possibly during photoreceptor development. Comparison of the Shaker and Shab mutant photoreceptors with the wild type revealed that a concurrent decrease in the steady-state input resistance followed from deletion of the voltage-gated K+ channels. This allowed partial compensation of the compression and saturation caused by the loss of Shaker channels and it maintained the characteristics of the light-voltage relationship in Shab mutant photoreceptors. However, wild type properties were not fully restored in either mutant. Indeed, decreased input resistance results in reduced efficiency of neural processing, assessed by the metabolic cost of information. Results of this study demonstrate the importance of the voltage-gated K+ channels for neural coding precision and highlight the robustness of neuronal information processing gained through regulation of the electrical properties.
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Ruzov, Vladimir. "Neuromodulation: Action Potential Modeling." DigitalCommons@CalPoly, 2014. https://digitalcommons.calpoly.edu/theses/1217.

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There have been many different studies performed in order to examine various properties of neurons. One of the most important properties of neurons is an ability to originate and propagate action potential. The action potential is a source of communication between different neural structures located in different anatomical regions. Many different studies use modeling to describe the action potential and its properties. These models mathematically describe physical properties of neurons and analyze and explain biological and electrochemical processes such as action potential initiation and propagation. Therefore, one of the most important functions of neurons is an ability to provide communication between different neural structures located in different anatomical regions. This is achieved by transmitting electrical signals from one part of the body to another. For example, neurons transmit signals from the brain to the motor neurons (efferent neurons) and from body tissues back to the brain (afferent neurons). This communication process is extremely important for a being to function properly. One of the most valuable studies in neuroscience was conducted by Alan Hodgkin and Andrew Huxley. In their work, Alan Hodgkin and Andrew Huxley used a giant squid axon to create a mathematical model which analyzes and explains the ionic mechanisms underlying the initiation and propagation of action potentials. They received the 1963 Nobel Prize in Physiology/Medicine for their valuable contribution to medical science. The Hodgkin and Huxley model is a mathematical model that describes how the action potential is initiated and how it propagates in a neuron. It is a set of nonlinear ordinary differential equations that approximates the electrical characteristics of excitable cells such as neurons and cardiomyocytes. This work focuses on modeling the Hodgkin and Huxley model using MATLAB extension - Simulink. This tool provides a graphical editor, customizable block libraries, and solvers for modeling and simulating dynamic systems. Simulink model is used to describe the mechanisms and underlying processes involved in action potential initiation and propagation.
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Takalo, J. (Jouni). "Towards natural insect vision research." Doctoral thesis, University of Oulu, 2013. http://urn.fi/urn:isbn:9789526203249.

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Abstract Visual world is naturally correlated both spatially and temporally. The correlations are used in vision to enhance performance of neurons. For gaining maximal neural performance of the visual neurons, the experiments, from stimulus to the analysis, should be designed to take advantage of the correlations. In this thesis methods for generating and analyzing natural stimuli were examined by using computations and algorithms. For analyzing responses to natural stimuli in visual neurons, a method with only a few assumptions was developed for estimating information rate in long responses. The novel method gave a good agreement with Shannon information rate with linear system and Gaussian input but was able to handle also nonlinear processing and non-Gaussian data. Secondly, a computer controlled 3D virtual environment with a spherical screen was developed, with a large visual field. The image of the world was projected to the screen with a DLP projector, giving good enough temporal performance for insect vision research. A track-ball was used in closed loop experiments. Thirdly, properties of single photon (“bump”) information transfer at various light levels were investigated in cockroach photoreceptor with a coarse computational model. At dim light (< 10 ph/s), where single bump responses were visible, shot noise was dominant. At higher light levels latency distribution of the bump decreased the information rate, but amplitude distribution of bump did not have an effect. Fourthly, the contribution of K⁺ channels to information rate and energy consumption was investigated by creating a database of computation models with varying channel compositions. The information rate has a maximum as a function of mean conductance, which was a sum of the average K⁺ conductance and the leak conductance. This maximum was fine-tuned by the K⁺ channel composition, which had high so-called novel contribution and relatively low amount of other conductances.
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Daouzli, Adel Mohamed. "Systèmes neuromorphiques : étude et implantation de fonctions d'apprentissage et de plasticité." Thesis, Bordeaux 1, 2009. http://www.theses.fr/2009BOR13806/document.

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Dans ces travaux de thèse, nous nous sommes intéressés à l'influence du bruit synaptique sur la plasticité synaptique dans un réseau de neurones biophysiquement réalistes. Le simulateur utilisé est un système électronique neuromorphique. Nous avons implanté un modèle de neurones à conductances basé sur le formalisme de Hodgkin et Huxley, et un modèle biophysique de plasticité. Ces travaux ont inclus la configuration du système, le développement d'outils pour l'exploiter, son utilisation ainsi que la mise en place d'une plateforme le rendant accessible à la communauté scientifique via Internet et l'utilisation de scripts PyNN (langage de description de simulations en neurosciences computationnelles)
In this work, we have investigated the effect of input noise patterns on synaptic plasticity applied to a neural network. The study was realised using a neuromorphic hardware simulation system. We have implemented a neural conductance model based on Hodgkin and Huxley formalism, and a biophysical model for plasticity. The tasks performed during this thesis project included the configuration of the system, the development of software tools, the analysis tools to explore experimental results, and the development of the software modules for the remote access to the system via Internet using PyNN scripts (PyNN is a neural network description language commonly used in computational neurosciences)
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Kondo, Shingo, and Masahiro Ohka. "Stochastic resonance aided tactile sensing." Cambridge University Press, 2009. http://hdl.handle.net/2237/14323.

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Books on the topic "Hodgkin-Huxley model"

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Melendy, Robert F. Bang-bang control development of permeability changes in a membrane model.: Permeability correction mechanisms inherent in the Hodgkin-Huxley model. Corvallis, OR: OSU Libraries, 1997.

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Borkowski, Lech S. Nonlinear dynamics of Hodgkin-Huxley neurons. Poznań: Wydawn. Nauk. UAM, 2010.

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Cronin, Jane. Mathematical aspects of Hodgkin-Huxley neural theory. Cambridge [Cambridgeshire]: Cambridge University Press, 1987.

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Woodward, James. Explanation in Neurobiology. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199685509.003.0004.

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This chapter employs an interventionist framework to elucidate some issues having to do with explanation in neurobiology. I argue that this framework can be used to distinguish theories and models that are explanatory from those that are merely descriptive. This framework can also be used to characterize a notion of a mechanistic explanation, according to which mechanistic explanations are those that meet interventionist criteria for successful explanation and certain additional constraints as well. However, from an interventionist perspective, although mechanistic theories have a number of virtues, it is a mistake to think that such models are the only legitimate kind of explanation in neuroscience and psychology. In particular, some (but not all) dynamical systems models in neuroscience are explanatory as are many models, such as the Hodgkin-Huxley model, that abstract away from mechanistically relevant low-level detail.
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Koch, Christof. Biophysics of Computation. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195104912.001.0001.

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Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes. Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation. Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.
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Wendling, Fabrice, and Fernando H. Lopes da Silva. Dynamics of EEGs as Signals of Neuronal Populations. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0003.

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This chapter gives an overview of approaches used to understand the generation of electroencephalographic (EEG) signals using computational models. The basic concept is that appropriate modeling of neuronal networks, based on relevant anatomical and physiological data, allows researchers to test hypotheses about the nature of EEG signals. Here these models are considered at different levels of complexity. The first level is based on single cell biophysical properties anchored in classic Hodgkin-Huxley theory. The second level emphasizes on detailed neuronal networks and their role in generating different kinds of EEG oscillations. At the third level are models derived from the Wilson-Cowan approach, which constitutes the backbone of neural mass models. Another part of the chapter is dedicated to models of epileptiform activities. Finally, the themes of nonlinear dynamic systems and topological models in EEG generation are discussed.
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Book chapters on the topic "Hodgkin-Huxley model"

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Beeman, David. "Hodgkin-Huxley Model." In Encyclopedia of Computational Neuroscience, 1–13. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_127-3.

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Beeman, David. "Hodgkin-Huxley Model." In Encyclopedia of Computational Neuroscience, 1389–99. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_127.

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Nelson, Mark, and John Rinzel. "The Hodgkin-Huxley Model." In The Book of GENESIS, 29–51. New York, NY: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4684-0189-9_4.

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Nelson, Mark, and John Rinzel. "The Hodgkin—Huxley Model." In The Book of GENESIS, 29–49. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1634-6_4.

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Peterson, James K. "The Basic Hodgkin–Huxley Model." In Calculus for Cognitive Scientists, 401–84. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-880-9_12.

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van Wijk van Brievingh, Rogier P., and Ignacio A. García Alves. "The Excitable Membrane: The Hodgkin-Huxley Model." In Biomedical Modeling and Simulation on a PC, 94–129. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4613-9163-0_7.

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Sakumura, Yuichi, Norio Konno, and Kazuyuki Aihara. "Markov Chain Model Approximating the Hodgkin-Huxley Neuron." In Artificial Neural Networks — ICANN 2001, 1153–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44668-0_161.

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Schneidman, Elad, Barry Freedman, and Idan Segev. "Spike Timing Reliability in a Stochastic Hodgkin-Huxley Model." In Computational Neuroscience, 261–66. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-4831-7_44.

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Sherief, Hany H., A. M. A. El-Sayed, S. H. Behiry, and W. E. Raslan. "Using Fractional Derivatives to Generalize the Hodgkin–Huxley Model." In Fractional Dynamics and Control, 275–82. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0457-6_23.

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Theunissen, Frédéric E., Frank H. Eeckman, and John P. Miller. "A Modified Hodgkin-Huxley Spiking Model with Continuous Spiking Output." In Computation and Neural Systems, 9–17. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3254-5_2.

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Conference papers on the topic "Hodgkin-Huxley model"

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Isler, Yalcin, Mehmet Kuntalp, and Gokhan Gonel. "Microcontroller based Hodgkin-Huxley model neuron simulation." In 2009 14th National Biomedical Engineering Meeting. IEEE, 2009. http://dx.doi.org/10.1109/biyomut.2009.5130348.

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Lankarany, M., W. P. Zhu, M. N. S. Swamy, and Taro Toyoizumi. "Blind Deconvolution of Hodgkin-Huxley neuronal model." In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013. http://dx.doi.org/10.1109/embc.2013.6610407.

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"FPGA Implementation of Hodgkin-Huxley Neuron Model." In International Conference on Neural Computation Theory and Applications. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004152605220528.

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Ionescu, Alexandra, Alina Orosanu, and Mihai Iordache. "Memristive model of the Hodgkin-Huxley axon." In 2021 12th International Symposium on Advanced Topics in Electrical Engineering (ATEE). IEEE, 2021. http://dx.doi.org/10.1109/atee52255.2021.9425098.

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Csercsik, David, Gabor Szederkenyi, Katalin M. Hangos, and Imre Farkas. "Model synthesis identification a Hodgkin-Huxley-type neuron model." In 2009 European Control Conference (ECC). IEEE, 2009. http://dx.doi.org/10.23919/ecc.2009.7074874.

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Romanyshyn, Yuriy, Sergei Yelmanov, Hryhoriy Vaskiv, and Igor Grybyk. "Bifurcations Features of the Hodgkin-Huxley Neuron Model." In 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). IEEE, 2020. http://dx.doi.org/10.1109/tcset49122.2020.235564.

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Zhang, Yue, Kuanquan Wang, Yongfeng Yuan, Dong Sui, Henggui Zhang, and Henggui Zhang. "Stability and bifurcation analysis of Hodgkin-Huxley model." In 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2013. http://dx.doi.org/10.1109/bibm.2013.6732717.

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Devi, M., Durga Choudhary, and Akhil Ranjan Garg. "Information Processing in Extended Hodgkin-Huxley Neuron Model." In 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE). IEEE, 2020. http://dx.doi.org/10.1109/icetce48199.2020.9091733.

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Mai Lu, Jin-Long Wang, Jia Wen, and Xu-Wei Dong. "Implementation of Hodgkin-Huxley neuron model in FPGAs." In 2016 Asia-Pacific International Symposium on Electromagnetic Compatibility (APEMC). IEEE, 2016. http://dx.doi.org/10.1109/apemc.2016.7522959.

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Liao, Fang, Xuyang Lou, Baotong Cui, and Wei Wu. "State filtering and parameter estimation for Hodgkin-Huxley model." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727811.

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Reports on the topic "Hodgkin-Huxley model"

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Enderle, John D., and Edward J. Engelken. Simulation of Oculomotor Post-Inhibitory Rebound Burst Firing using a Hodgkin-Huxley Model of a Neuron. Fort Belvoir, VA: Defense Technical Information Center, February 1995. http://dx.doi.org/10.21236/ada293821.

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