@Article{Tsodyks1997,
  author    = {Tsodyks, Misha V. and Markram, Henry},
  journal   = {Proceedings of the National Academy of Sciences},
  title     = {The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability},
  year      = {1997},
  issn      = {1091-6490},
  month     = jan,
  number    = {2},
  pages     = {719--723},
  volume    = {94},
  doi       = {10.1073/pnas.94.2.719},
  file      = {:Tsodyks1997 - The Neural Code between Neocortical Pyramidal Neurons Depends on Neurotransmitter Release Probability.pdf:PDF},
  groups    = {TsodyksMarkram},
  publisher = {Proceedings of the National Academy of Sciences},
}

@Article{Jirsa2014,
  author    = {Jirsa, Viktor K. and Stacey, William C. and Quilichini, Pascale P. and Ivanov, Anton I. and Bernard, Christophe},
  journal   = {Brain},
  title     = {On the nature of seizure dynamics},
  year      = {2014},
  issn      = {0006-8950},
  month     = jun,
  number    = {8},
  pages     = {2210--2230},
  volume    = {137},
  doi       = {10.1093/brain/awu133},
  file      = {:Jirsa2014 - On the Nature of Seizure Dynamics.pdf:PDF:https\://academic.oup.com/brain/article-pdf/137/8/2210/13797867/awu133.pdf},
  groups    = {Neural Mass Models},
  publisher = {Oxford University Press (OUP)},
}

@Article{Breakspear2003,
  author    = {Breakspear, Michael and R. Terry, John and J. Friston, Karl},
  journal   = {Neurocomputing},
  title     = {Modulation of excitatory synaptic coupling facilitates synchronization and complex dynamics in a nonlinear model of neuronal dynamics},
  year      = {2003},
  issn      = {0925-2312},
  month     = jun,
  pages     = {151--158},
  volume    = {52–54},
  doi       = {10.1016/s0925-2312(02)00740-3},
  file      = {:Breakspear2003 - Modulation of Excitatory Synaptic Coupling Facilitates Synchronization and Complex Dynamics in a Nonlinear Model of Neuronal Dynamics.pdf:PDF:http\://www.fil.ion.ucl.ac.uk/~karl/Modulation%20of%20excitatory%20synaptic%20coupling%20facilitates%20synchronization.pdf},
  groups    = {Neural Mass Models, LarterBreakspear},
  publisher = {Elsevier BV},
}

@Article{Breakspear2003a,
  author          = {Breakspear, Michael and Terry, John R. and Friston, Karl J.},
  journal         = {Network (Bristol, England)},
  title           = {Modulation of excitatory synaptic coupling facilitates synchronization and complex dynamics in a biophysical model of neuronal dynamics.},
  year            = {2003},
  issn            = {0954-898X},
  month           = nov,
  pages           = {703--732},
  volume          = {14},
  abstract        = {In this paper, complex dynamical synchronization in a non-linear model of a neural system is studied, and the computational significance of the behaviours is explored. The local neural dynamics is determined by voltage- and ligand-gated ion channels and feedback between densely interconnected excitatory and inhibitory neurons. A mesoscopic array of local networks is modelled by introducing coupling between the local networks via weak excitatory-to-excitatory connectivity. It is shown that with modulation of this long-range synaptic coupling, the system undergoes a transition from independent oscillations to stable chaotic synchronization. Between these states exists a 'weakly' stable state associated with complex, intermittent behaviour in the temporal domain and clusters of synchronous regions in the spatial domain. The paper concludes with a discussion of the putative relevance of such processes in the brain, including the role of neuromodulatory systems and the mechanisms underlying sensory perception, adaptation, computation and complexity.},
  citation-subset = {IM},
  completed       = {2004-01-09},
  country         = {England},
  doi             = {10.1088/0954-898X/14/4/305},
  groups          = {LarterBreakspear},
  issn-linking    = {0954-898X},
  issue           = {4},
  keywords        = {Action Potentials; Animals; Computer Simulation; Cortical Synchronization; Excitatory Postsynaptic Potentials; Feedback; Humans; Ion Channel Gating, physiology; Membrane Potentials; Models, Neurological; Neural Inhibition; Neural Networks, Computer; Neurons, physiology; Nonlinear Dynamics; Synapses, physiology; Time Factors},
  nlm-id          = {9431867},
  owner           = {NLM},
  pmid            = {14653499},
  pubmodel        = {Print},
  pubstate        = {ppublish},
  revised         = {2019-12-10},
}

@Article{Cabral2011,
  author    = {Cabral, Joana and Hugues, Etienne and Sporns, Olaf and Deco, Gustavo},
  journal   = {NeuroImage},
  title     = {Role of local network oscillations in resting-state functional connectivity},
  year      = {2011},
  issn      = {1053-8119},
  month     = jul,
  number    = {1},
  pages     = {130--139},
  volume    = {57},
  doi       = {10.1016/j.neuroimage.2011.04.010},
  file      = {:Cabral2011 - Role of Local Network Oscillations in Resting State Functional Connectivity.pdf:PDF},
  groups    = {BNM-Studies, Kuramoto},
  publisher = {Elsevier BV},
}

@Article{FitzHugh1961,
  author    = {FitzHugh, Richard},
  journal   = {Biophysical Journal},
  title     = {Impulses and Physiological States in Theoretical Models of Nerve Membrane},
  year      = {1961},
  issn      = {0006-3495},
  month     = jul,
  number    = {6},
  pages     = {445--466},
  volume    = {1},
  abstract  = {Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting "BVP model" has two variables of state, representing excitability and refractoriness, and qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve. This BVP model serves as a simple representative of a class of excitable-oscillatory systems including the Hodgkin-Huxley (HH) model of the squid giant axon. The BVP phase plane can be divided into regions corresponding to the physiological states of nerve fiber (resting, active, refractory, enhanced, depressed, etc.) to form a "physiological state diagram," with the help of which many physiological phenomena can be summarized. A properly chosen projection from the 4-dimensional HH phase space onto a plane produces a similar diagram which shows the underlying relationship between the two models. Impulse trains occur in the BVP and HH models for a range of constant applied currents which make the singular point representing the resting state unstable.},
  doi       = {10.1016/s0006-3495(61)86902-6},
  file      = {:FitzHugh1961 - Impulses and Physiological States in Theoretical Models of Nerve Membrane.pdf:PDF},
  groups    = {Modeling Theory},
  publisher = {Elsevier BV},
}

@Article{Jansen1993,
  author    = {Jansen, Ben H. and Zouridakis, George and Brandt, Michael E.},
  journal   = {Biological Cybernetics},
  title     = {A neurophysiologically-based mathematical model of flash visual evoked potentials},
  year      = {1993},
  issn      = {1432-0770},
  month     = jan,
  number    = {3},
  pages     = {275--283},
  volume    = {68},
  abstract  = {Evidence is presented that a neurophysiologi- produce the spontaneous EEG activity may also be
cally-inspired mathematical model, originally developed responsible for the generation of EP activity. The 
for the generation of spontaneous EEG (electroen- present study was conducted to explore this hypothesis. 
cephalogram) activity, can produce VEP (visual evoked In this paper, we report on the degree to which the 
potential)-like waveforms when pulse-like signals serve flash-visual evoked potential (VEP) can be simulated 
as input. It was found that the simulated VEP activity using a neurophysiologicaUy-based mathematical model 
was mainly due to intracortical excitatory connections originally developed for the generation of spontaneous 
rather than direct thalamic input. Also, the model-gen- electroencephalogram (EEG) activity, when presented 
erated VEPs exhibited similar relationships between with a transient input. 
prestimulus EEG characteristics and subsequent VEP In using this model, we take a system's approach to 
morphology, as seen in human data. Specifically, the EP generation. That is, we consider the evoked poten- 
large correlation between the N1 amplitude and the tial as the output ~f a system excited by a transient, 
prestimulus alpha phase angle, and the insensitivity of reflecting the volley of neural activity generated by a 
P2 to the latter feature, as observed in actual VEPs to sensory event. This transient sums with other cortical 
low intensity flashes, was also found in the model-gen- and/or sub-cortical activity entering the same region of 
erated data. These findings provide support for the the cortex. At the most abstract level, the sensory input 
hypothesis that the spontaneous EEG and the VEP are can be viewed as an impulse, and the evoked output as 
generated by some of the same neural structures and an impulse response. The nonlinearity of the neural 
that the VEP is due to distributed activity, rather than networks processing the sensory input, and the fact that 
dipolar sources. these networks receive sensory and nonsensory-related},
  doi       = {10.1007/bf00224863},
  file      = {:Jansen1993 - A Neurophysiologically Based Mathematical Model of Flash Visual Evoked Potentials.pdf:PDF},
  publisher = {Springer Science and Business Media LLC},
}

@InBook{Kuramoto1975,
  author    = {Kuramoto, Yoshiki},
  pages     = {420--422},
  publisher = {Springer-Verlag},
  title     = {Self-entrainment of a population of coupled non-linear oscillators},
  year      = {1975},
  isbn      = {3540071741},
  booktitle = {Lecture Notes in Physics},
  doi       = {10.1007/bfb0013365},
  file      = {:Kuramoto1975 - Self Entrainment of a Population of Coupled Non Linear Oscillators.pdf:PDF:https\://link.springer.com/content/pdf/10.1007/BFb0013365.pdf},
  groups    = {Neural Mass Models},
}

@Article{Nagumo1962,
  author    = {Nagumo, J. and Arimoto, S. and Yoshizawa, S.},
  journal   = {Proceedings of the IRE},
  title     = {An Active Pulse Transmission Line Simulating Nerve Axon},
  year      = {1962},
  issn      = {0096-8390},
  month     = oct,
  number    = {10},
  pages     = {2061--2070},
  volume    = {50},
  doi       = {10.1109/jrproc.1962.288235},
  file      = {:Nagumo1962 - An Active Pulse Transmission Line Simulating Nerve Axon.pdf:PDF},
  groups    = {Modeling Theory},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
}

@Article{Strogatz2000,
  author    = {Strogatz, Steven H.},
  journal   = {Physica D: Nonlinear Phenomena},
  title     = {From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators},
  year      = {2000},
  issn      = {0167-2789},
  month     = sep,
  number    = {1–4},
  pages     = {1--20},
  volume    = {143},
  doi       = {10.1016/s0167-2789(00)00094-4},
  file      = {:Strogatz2000 - From Kuramoto to Crawford_ Exploring the Onset of Synchronization in Populations of Coupled Oscillators.pdf:PDF},
  groups    = {Modeling Theory},
  publisher = {Elsevier BV},
}

@Article{Jansen1995,
  author     = {Jansen, Ben H. and Rit, Vincent G.},
  journal    = {Biological Cybernetics},
  title      = {Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns},
  year       = {1995},
  issn       = {1432-0770},
  month      = sep,
  number     = {4},
  pages      = {357--366},
  volume     = {73},
  doi        = {10.1007/bf00199471},
  file       = {:Jansen1995 - Electroencephalogram and Visual Evoked Potential Generation in a Mathematical Model of Coupled Cortical Columns.pdf:PDF:https\://link.springer.com/content/pdf/10.1007/BF00199471.pdf},
  groups     = {Neural Mass Models, JansenRit},
  publisher  = {Springer Science and Business Media LLC},
  readstatus = {read},
}

@Article{Stefanovski2019,
  author    = {Stefanovski, Leon and Triebkorn, Paul and Spiegler, Andreas and Diaz-Cortes, Margarita-Arimatea and Solodkin, Ana and Jirsa, Viktor and McIntosh, Anthony Randal and Ritter, Petra},
  journal   = {Frontiers in Computational Neuroscience},
  title     = {Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer’s Disease},
  year      = {2019},
  issn      = {1662-5188},
  month     = aug,
  volume    = {13},
  doi       = {10.3389/fncom.2019.00054},
  file      = {:Stefanovski2019 - Linking Molecular Pathways and Large Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer’s Disease.pdf:PDF:https\://www.frontiersin.org/articles/10.3389/fncom.2019.00054/pdf},
  groups    = {Alzheimer, Annotate!},
  publisher = {Frontiers Media SA},
  ranking   = {rank5},
}

@Article{Deco2014,
  author    = {Gustavo Deco and Adrián Ponce-Alvarez and Patric Hagmann and Gian Luca Romani and Dante Mantini and Maurizio Corbetta},
  journal   = {Journal of Neuroscience},
  title     = {How local excitation-inhibition ratio impacts the whole brain dynamics},
  year      = {2014},
  issn      = {1529-2401},
  pages     = {7886--7898},
  volume    = {34},
  doi       = {10.1523/JNEUROSCI.5068-13.2014},
  file      = {:Deco2014 - How Local Excitation Inhibition Ratio Impacts the Whole Brain Dynamics.pdf:PDF},
  groups    = {LiteratureDB},
  issue     = {23},
  keywords  = {Anatomical connectivity,Functional connectivity,Large-scale brain model,Local feedback inhibition,Resting-state activity},
  pmid      = {24899711},
  publisher = {Society for Neuroscience},
}

@Article{Cortes2013,
  author    = {Cortes, Jesus M. and Desroches, Mathieu and Rodrigues, Serafim and Veltz, Romain and Muñoz, Miguel A. and Sejnowski, Terrence J.},
  journal   = {Proceedings of the National Academy of Sciences},
  title     = {Short-term synaptic plasticity in the deterministic Tsodyks–Markram model leads to unpredictable network dynamics},
  year      = {2013},
  issn      = {1091-6490},
  month     = sep,
  number    = {41},
  pages     = {16610--16615},
  volume    = {110},
  doi       = {10.1073/pnas.1316071110},
  file      = {:Cortes2013 - Short Term Synaptic Plasticity in the Deterministic Tsodyks–Markram Model Leads to Unpredictable Network Dynamics.pdf:PDF},
  groups    = {TsodyksMarkram},
  publisher = {Proceedings of the National Academy of Sciences},
}

@Article{Zimmermann2018,
  author    = {Zimmermann, J. and Perry, A. and Breakspear, M. and Schirner, M. and Sachdev, P. and Wen, W. and Kochan, N.A. and Mapstone, M. and Ritter, P. and McIntosh, A.R. and Solodkin, A.},
  journal   = {NeuroImage: Clinical},
  title     = {Differentiation of Alzheimer’s disease based on local and global parameters in personalized Virtual Brain models},
  year      = {2018},
  issn      = {2213-1582},
  pages     = {240--251},
  volume    = {19},
  doi       = {10.1016/j.nicl.2018.04.017},
  file      = {:Zimmermann2018 - Differentiation of Alzheimer’s Disease Based on Local and Global Parameters in Personalized Virtual Brain Models.pdf:PDF},
  groups    = {Alzheimer, Annotate!},
  publisher = {Elsevier BV},
}

@Article{Hansen2015,
  author    = {Hansen, Enrique C.A. and Battaglia, Demian and Spiegler, Andreas and Deco, Gustavo and Jirsa, Viktor K.},
  journal   = {NeuroImage},
  title     = {Functional connectivity dynamics: Modeling the switching behavior of the resting state},
  year      = {2015},
  issn      = {1053-8119},
  month     = jan,
  pages     = {525--535},
  volume    = {105},
  date      = {2014-11-10},
  day       = {10},
  doi       = {10.1016/j.neuroimage.2014.11.001},
  file      = {:Hansen2015 - Functional Connectivity Dynamics_ Modeling the Switching Behavior of the Resting State.pdf:PDF},
  groups    = {LiteratureDB},
  keywords  = {Functional connectivity Functional connectivity dynamics Structural connectivity Resting state Brain dynamics Whole brain computational model SC, structural connectivity, FC, functional connectivity, FCD, functional connectivity dynamics, MFM, mean-field model, LSM, linear stochastic model, eMFM, enhanced non-linearity mean-field model, RSN, resting state network, DSI, diffusion spectrum imaging, fMRI, functional magnetic resonance imaging, BOLD, blood oxygenation-level dependent},
  publisher = {Elsevier BV},
}

@Article{Larter1999,
  author    = {Larter, Raima and Speelman, Brent and Worth, Robert M.},
  journal   = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
  title     = {A coupled ordinary differential equation lattice model for the simulation of epileptic seizures},
  year      = {1999},
  issn      = {1089-7682},
  month     = sep,
  number    = {3},
  pages     = {795--804},
  volume    = {9},
  doi       = {10.1063/1.166453},
  file      = {:Larter1999 - A Coupled Ordinary Differential Equation Lattice Model for the Simulation of Epileptic Seizures.pdf:PDF:https\://pubs.aip.org/aip/cha/article-pdf/9/3/795/18301745/795_1_online.pdf},
  groups    = {BNM-Studies},
  publisher = {AIP Publishing},
}

@Comment{jabref-meta: databaseType:bibtex;}
