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第5行： − 癫痫的计算模型主要集中在描述与癫痫发作有关的电生理表现。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。+
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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.
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<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>