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第1行： − '''Computational models in epilepsy''' mainly focus on describing an [[Electrophysiology|electrophysiological]] manifestation associated with [[epilepsy]] called [[seizure]]s. For this purpose, [[computational neuroscience]]s use [[differential equation]]s to reproduce the temporal evolution of the signals recorded experimentally. A book published in 2008, ''Computational Neuroscience in Epilepsy'',.<ref>{{Cite book|url=https://www.worldcat.org/oclc/281558250|title=Computational neuroscience in epilepsy|date=2008|publisher=Academic|others=Ivan Soltesz, Kevin Staley|isbn=978-0-12-373649-9|edition=1st|location=Amsterdam|oclc=281558250}}</ref> summarizes different works done up to this time. The goals of using its models are diverse, from prediction to comprehension of underlying mechanisms.<ref>{{Cite journal|last=Lytton|first=William W.|date=August 2008|title=Computer modelling of epilepsy|url=https://www.nature.com/articles/nrn2416|journal=Nature Reviews Neuroscience|language=en|volume=9|issue=8|pages=626–637|doi=10.1038/nrn2416|issn=1471-0048|pmc=2739976|pmid=18594562}}</ref>+ − Computational models in epilepsy mainly focus on describing an electrophysiological manifestation associated with epilepsy called seizures. For this purpose, computational neurosciences use differential equations to reproduce the temporal evolution of the signals recorded experimentally. A book published in 2008, Computational Neuroscience in Epilepsy,. summarizes different works done up to this time. The goals of using its models are diverse, from prediction to comprehension of underlying mechanisms.+ − 癫痫的计算模型主要关注描述与癫痫相关的电生理表现，称为癫痫发作。为此，计算神经科学使用微分方程来重现实验记录的信号的时间演变。2008年出版的一本书，《癫痫的计算神经科学》 ,。总结了到目前为止所做的不同工作。使用它的模型的目的是多种多样的，从预测到理解潜在的机制。+ − The crisis phenomenon (seizure) exists and shares certain dynamical properties across different scales<ref>{{Cite web|last1=Depannemaecker|first1=Damien|last2=Destexhe|first2=Alain|last3=Jirsa|first3=Viktor|last4=Bernard|first4=Christophe|date=2021-02-22|title=Modeling Seizures: From Single Neurons to Networks|url=https://www.preprints.org/manuscript/202102.0478/v1|doi=10.20944/preprints202102.0478.v1}}</ref> and different organisms.<ref>{{Cite journal|last1=Jirsa|first1=Viktor K.|last2=Stacey|first2=William C.|last3=Quilichini|first3=Pascale P.|last4=Ivanov|first4=Anton I.|last5=Bernard|first5=Christophe|date=2014-06-10|title=On the nature of seizure dynamics|url=https://doi.org/10.1093/brain/awu133|journal=Brain|volume=137|issue=8|pages=2210–2230|doi=10.1093/brain/awu133|issn=1460-2156|pmc=4107736|pmid=24919973}}</ref> It is possible to distinguish different approaches: the phenomenological models focus on the dynamics observed, generally reduced to few dimension it facilitates the study from the point of view of the [[Dynamical systems theory|theory of dynamical systems]]<ref>{{Cite journal|last1=Saggio|first1=Maria Luisa|last2=Spiegler|first2=Andreas|last3=Bernard|first3=Christophe|last4=Jirsa|first4=Viktor K.|date=2017-07-25|title=Fast–Slow Bursters in the Unfolding of a High Codimension Singularity and the Ultra-slow Transitions of Classes|url=https://doi.org/10.1186/s13408-017-0050-8|journal=The Journal of Mathematical Neuroscience|volume=7|issue=1|pages=7|doi=10.1186/s13408-017-0050-8|issn=2190-8567|pmc=5526832|pmid=28744735}}</ref> and more mechanistic models that explain the biophysical interactions underlying seizures. It is also possible to use these approaches to model and analyse the interactions between different regions of the brain<ref>{{Cite journal|last1=Breakspear|first1=M.|last2=Roberts|first2=J. A.|last3=Terry|first3=J. R.|last4=Rodrigues|first4=S.|last5=Mahant|first5=N.|last6=Robinson|first6=P. A.|date=2005-11-09|title=A Unifying Explanation of Primary Generalized Seizures Through Nonlinear Brain Modeling and Bifurcation Analysis|url=https://doi.org/10.1093/cercor/bhj072|journal=Cerebral Cortex|volume=16|issue=9|pages=1296–1313|doi=10.1093/cercor/bhj072|pmid=16280462|issn=1460-2199}}</ref> (In this case the notion of [[Large-scale brain networks|network]] plays an important role<ref>{{Cite journal|last1=Terry|first1=John R.|last2=Benjamin|first2=Oscar|last3=Richardson|first3=Mark P.|date=2012|title=Seizure generation: The role of nodes and networks|url=https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1528-1167.2012.03560.x|journal=Epilepsia|language=en|volume=53|issue=9|pages=e166–e169|doi=10.1111/j.1528-1167.2012.03560.x|pmid=22709380|s2cid=25085531|issn=1528-1167}}</ref>) and the transition to ictal state.<ref>{{Cite journal|last1=Wendling|first1=Fabrice|last2=Hernandez|first2=Alfredo|last3=Bellanger|first3=Jean-Jacques|last4=Chauvel|first4=Patrick|last5=Bartolomei|first5=Fabrice|date=October 2005|title=Interictal to ictal transition in human temporal lobe epilepsy: insights from a computational model of intracerebral EEG|journal=Journal of Clinical Neurophysiology|volume=22|issue=5|pages=343–356|issn=0736-0258|pmc=2443706|pmid=16357638}}</ref> These large-scale approaches have the advantage of being able to be related to the recordings made in humans thanks to [[Electroencephalography|electroencephalography (EEG)]]. It offers new directions for clinical research, particularly as an additional tool in the treatment of refractory epilepsy <ref>{{Cite journal|date=2017-01-15|title=The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread|url=https://www.sciencedirect.com/science/article/pii/S1053811916300891|journal=NeuroImage|language=en|volume=145|pages=377–388|doi=10.1016/j.neuroimage.2016.04.049|issn=1053-8119|last1=Jirsa|first1=V.K.|last2=Proix|first2=T.|last3=Perdikis|first3=D.|last4=Woodman|first4=M.M.|last5=Wang|first5=H.|last6=Gonzalez-Martinez|first6=J.|last7=Bernard|first7=C.|last8=Bénar|first8=C.|last9=Guye|first9=M.|last10=Chauvel|first10=P.|last11=Bartolomei|first11=F.|issue=Pt B|pmid=27477535|s2cid=36510741}}</ref><ref>{{Cite journal|last1=Khambhati|first1=Ankit N.|last2=Davis|first2=Kathryn A.|last3=Lucas|first3=Timothy H.|last4=Litt|first4=Brian|last5=Bassett|first5=Danielle S.|date=September 2016|title=Virtual Cortical Resection Reveals Push-Pull Network Control Preceding Seizure Evolution|url=https://linkinghub.elsevier.com/retrieve/pii/S089662731630424X|journal=Neuron|language=en|volume=91|issue=5|pages=1170–1182|doi=10.1016/j.neuron.2016.07.039|pmc=5017915|pmid=27568515}}</ref>+ − − The crisis phenomenon (seizure) exists and shares certain dynamical properties across different scales and different organisms. It is possible to distinguish different approaches: the phenomenological models focus on the dynamics observed, generally reduced to few dimension it facilitates the study from the point of view of the theory of dynamical systems and more mechanistic models that explain the biophysical interactions underlying seizures. It is also possible to use these approaches to model and analyse the interactions between different regions of the brain (In this case the notion of network plays an important role) and the transition to ictal state. These large-scale approaches have the advantage of being able to be related to the recordings made in humans thanks to electroencephalography (EEG). It offers new directions for clinical research, particularly as an additional tool in the treatment of refractory epilepsy − − 危机现象(癫痫)存在于不同尺度和不同生物体之间，并具有一定的动力学特性。可以区分不同的方法: 现象学模型侧重于观察到的动力学，一般降低到很少的维度，这有利于从动力系统理论的角度进行研究和更多的解释癫痫发作的生物物理学相互作用的机制模型。也可以用这些方法来模拟和分析大脑不同区域之间的相互作用(在这种情况下，网络的概念起着重要作用)和向发作状态的过渡。这些大规模的方法有一个优势，那就是可以通过脑电图与人类的记录相关联。这多亏了脑电图。它为临床研究提供了新的方向，特别是作为治疗难治性癫痫的补充手段 − − Other approaches are to use the models to try to understand the mechanisms underlying these seizures using biophysical descriptions from the neuron scale.<ref>{{Cite bioRxiv|last1=Depannemaecker|first1=Damien|last2=Ivanov|first2=Anton|last3=Lillo|first3=Davide|last4=Spek|first4=Len|last5=Bernard|first5=Christophe|last6=Jirsa|first6=Viktor|date=2020-10-23|title=A unified physiological framework of transitions between seizures, sustained ictal activity and depolarization block at the single neuron level|language=en|biorxiv=10.1101/2020.10.23.352021}}</ref><ref>{{Cite journal|last1=Cressman|first1=John R.|last2=Ullah|first2=Ghanim|last3=Ziburkus|first3=Jokubas|last4=Schiff|first4=Steven J.|last5=Barreto|first5=Ernest|date=April 2009|title=The influence of sodium and potassium dynamics on excitability, seizures, and the stability of persistent states: I. Single neuron dynamics|url=http://link.springer.com/10.1007/s10827-008-0132-4|journal=Journal of Computational Neuroscience|language=en|volume=26|issue=2|pages=159–170|doi=10.1007/s10827-008-0132-4|issn=0929-5313|pmc=2704057|pmid=19169801}}</ref><ref>{{Cite journal|last1=Destexhe|first1=A.|last2=Bal|first2=T.|last3=McCormick|first3=D. A.|last4=Sejnowski|first4=T. J.|date=1996-09-01|title=Ionic mechanisms underlying synchronized oscillations and propagating waves in a model of ferret thalamic slices|url=https://www.physiology.org/doi/10.1152/jn.1996.76.3.2049|journal=Journal of Neurophysiology|language=en|volume=76|issue=3|pages=2049–2070|doi=10.1152/jn.1996.76.3.2049|issn=0022-3077}}</ref><ref>{{Cite journal|last1=Almeida|first1=Antônio-Carlos G. De|last2=Rodrigues|first2=Antônio M.|last3=Scorza|first3=Fúlvio A.|last4=Cavalheiro|first4=Esper A.|last5=Teixeira|first5=Hewerson Z.|last6=Duarte|first6=Mário A.|last7=Silveira|first7=Gilcélio A.|last8=Arruda|first8=Emerson Z.|date=2008|title=Mechanistic hypotheses for nonsynaptic epileptiform activity induction and its transition from the interictal to ictal state—Computational simulation|url=https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1528-1167.2008.01686.x|journal=Epilepsia|language=en|volume=49|issue=11|pages=1908–1924|doi=10.1111/j.1528-1167.2008.01686.x|pmid=18513350|s2cid=12024463|issn=1528-1167}}</ref> This makes it possible to understand the role of homeostasis and to understand the link between physical quantities (such as the concentration of potassium for example) and the pathological dynamics observed.{{citation needed|date=July 2021}} − − Other approaches are to use the models to try to understand the mechanisms underlying these seizures using biophysical descriptions from the neuron scale. This makes it possible to understand the role of homeostasis and to understand the link between physical quantities (such as the concentration of potassium for example) and the pathological dynamics observed. − − 其他的方法是使用模型试图理解这些癫痫发作的机制，使用神经元尺度的生物物理描述。这使得理解稳态的作用和理解物理量(例如钾的浓度)和观察到的病理动力学之间的联系成为可能。 − − This area of research has evolved rapidly in recent years and continues to show promise for our understanding and treatment of epilepsies for either for direct clinical application in the case of refractory epilepsy or fundamental research to guide experimental works.{{citation needed|date=July 2021}} − − This area of research has evolved rapidly in recent years and continues to show promise for our understanding and treatment of epilepsies for either for direct clinical application in the case of refractory epilepsy or fundamental research to guide experimental works. − − 这一研究领域近年来发展迅速，并继续为我们理解和治疗癫痫显示出希望，无论是在难治性癫痫病例中的直接临床应用，还是指导实验工作的基础研究。
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此词条暂由彩云小译翻译，翻译字数共306，未经人工整理和审校，带来阅读不便，请见谅。
癫痫的计算模型主要关注描述与癫痫相关的电生理表现，称为癫痫发作。为此，计算神经科学使用微分方程来重现实验记录的信号的时间演变。2008年出版的一本书《癫痫的计算神经科学》，总结了到当年为止的不同工作。从预测信号到理解潜在的机制，使用癫痫的计算模型的目的是多样的。
危机现象(癫痫发作)存在于不同的尺度和不同的生物中，具有一定的动力学特性。我们可以区分不同的方法：现象学模型专注于观察到的动力学，通常将目标减少到几个维度，这有利于从动力学系统理论的角度进行研究，更多的机制模型可以解释癫痫发作背后生物物理学的相互作用。也可以用这些方法来建模并分析大脑不同区域之间的相互作用(在这种情况下，网络的概念起着重要作用)或发作状态的过渡期。这些大规模的方法的优势在于能够与人类的记录相关联，这是脑电图(electroencephalography, EEG)的功劳。现象学模型为临床研究提供了新的方向，特别是作为治疗难治性癫痫的补充手段。
其他方法是利用这些模型，从神经元尺度上利用生物物理学的描述，试图理解这些癫痫发作的机制。这使得我们能够理解体内平衡的作用，理解物理量(例如钾的浓度)和观察到的病理动力学之间的联系。
近年来，这一领域的研究进展迅速，无论是在难治性癫痫的直接临床应用，还是指导实验工作的基础研究，对我们理解和治疗癫痫都具有重要意义。
== References ==
== References ==