@articlegoldman2023asynchronous, title=Asynchronous and slow-wave oscillatory
states in connectome-based models of mouse, monkey and human cerebral cortex,
author=Goldman, Jennifer S and Sacha, Maria and kusch, lionel and Destexhe,
Alain, journal=bioRxiv, pages=2023–08, year=2023, publisher=Cold Spring Harbor
Laboratory

@articleschirner2018inferring, title=Inferring multi-scale neural mechanisms
with brain network modelling, author=Schirner, Michael and McIntosh, Anthony
Randal and Jirsa, Viktor and Deco, Gustavo and Ritter, Petra, journal=elife,
volume=7, pages=e28927, year=2018, publisher=eLife Sciences Publications, Ltd

@articlegoldman2019bridging, title=Bridging single neuron dynamics to global
brain states, author=Goldman, Jennifer S and Tort-Colet, Núria and Di Volo,
Matteo and Susin, Eduarda and Bouté, Jules and Dali, Melissa and Carlu,
Mallory and Nghiem, Trang-Anh and Górski, Tomasz and Destexhe, Alain,
journal=Frontiers in systems neuroscience, volume=13, pages=75, year=2019,
publisher=Frontiers

@articlezerlaut2018modeling, title=Modeling mesoscopic cortical dynamics using
a mean-field model of conductance-based networks of adaptive exponential
integrate-and-fire neurons, author=Zerlaut, Yann and Chemla, Sandrine and
Chavane, Frederic and Destexhe, Alain, journal=Journal of computational
neuroscience, volume=44, number=1, pages=45–61, year=2018, publisher=Springer

@articleSanzleon2013, abstract = We present TheVirtualBrain (TVB), a
neuroinformatics platform for full brain network simulations using
biologically realistic connectivity. This simulation environment enables the
model-based inference of neurophysiological mechanisms across different brain
scales that underlie the generation of macroscopic neuroimaging signals
including functional MRI (fMRI), EEG and MEG. Researchers from different
backgrounds can benefit from an integrative software platform including a
supporting framework for data management (generation, organization, storage,
integration and sharing) and a simulation core written in Python. TVB allows
the reproduction and evaluation of personalized configurations of the brain by
using individual subject data. This personalization facilitates an exploration
of the consequences of pathological changes in the system, permitting to
investigate potential ways to counteract such unfavorable processes. The
architecture of TVB supports interaction with MATLAB packages, for example,
the well known Brain Connectivity Toolbox. TVB can be used in a client-server
configuration, such that it can be remotely accessed through the Internet
thanks to its web-based HTML5, JS and WebGL graphical user interface. TVB is
also accessible as a standalone cross-platform Python library and application,
and users can interact with the scientific core through the scripting
interface IDLE, enabling easy modeling, development and debugging of the
scientific kernel. This second interface makes TVB extensible by combining it
with other libraries and modules developed by the Python scientific community.
In this article, we describe the theoretical background and foundations that
led to the development of TVB, the architecture and features of its major
software components as well as potential neuroscience applications. 2013
Sanz_leon, Knock, Woodman, Domide, Mersmann, Mcintosh and Jirsa., author =
Sanzleon, Paula and Knock, Stuart A. and Woodman, M. Marmaduke and Domide, Lia
and Mersmann, Jochen and Mcintosh, Anthony R. and Jirsa, Viktor, doi =
10.3389/fninf.2013.00010, file = :home/myguy/Downloads/fninf-07-00010.pdf:pdf,
issn = 16625196, journal = Frontiers in Neuroinformatics, keywords =
Connectivity,Connectome,Full-brain network model,GPUs,Large-scale
simulation,Neural field,Neural mass,Python,Time delays,Virtual brain,Web
platform, number = MAY, title = The virtual brain: A simulator of primate
brain network dynamics, volume = 7, year = 2013

@articleNeuroML, doi = 10.1371/journal.pcbi.1000815, author = Gleeson, Padraig
AND Crook, Sharon AND Cannon, Robert C. AND Hines, Michael L. AND Billings,
Guy O. AND Farinella, Matteo AND Morse, Thomas M. AND Davison, Andrew P. AND
Ray, Subhasis AND Bhalla, Upinder S. AND Barnes, Simon R. AND Dimitrova, Yoana
D. AND Silver, R. Angus, journal = PLOS Computational Biology, publisher =
Public Library of Science, title = NeuroML: A Language for Describing Data
Driven Models of Neurons and Networks with a High Degree of Biological Detail,
year = 2010, month = 06, volume = 6, url =
https://doi.org/10.1371/journal.pcbi.1000815, pages = 1-19, abstract =
Biologically detailed single neuron and network models are important for
understanding how ion channels, synapses and anatomical connectivity underlie
the complex electrical behavior of the brain. While neuronal simulators such
as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these
data-driven neuronal models, the specialized languages they employ are
generally not interoperable, limiting model accessibility and preventing reuse
of model components and cross-simulator validation. To overcome these problems
we have used an Open Source software approach to develop NeuroML, a neuronal
model description language based on XML (Extensible Markup Language). This
enables these detailed models and their components to be defined in a
standalone form, allowing them to be used across multiple simulators and
archived in a standardized format. Here we describe the structure of NeuroML
and demonstrate its scope by converting into NeuroML models of a number of
different voltage- and ligand-gated conductances, models of electrical
coupling, synaptic transmission and short-term plasticity, together with
morphologically detailed models of individual neurons. We have also used these
NeuroML-based components to develop an highly detailed cortical network model.
NeuroML-based model descriptions were validated by demonstrating similar model
behavior across five independently developed simulators. Although our results
confirm that simulations run on different simulators converge, they reveal
limits to model interoperability, by showing that for some models convergence
only occurs at high levels of spatial and temporal discretisation, when the
computational overhead is high. Our development of NeuroML as a common
description language for biophysically detailed neuronal and network models
enables interoperability across multiple simulation environments, thereby
improving model transparency, accessibility and reuse in computational
neuroscience., number = 6,

@articlePyLems, abstract = NeuroML is an XML-based model description language,
which provides a powerful common data format for defining and exchanging
models of neurons and neuronal networks. In the latest version of NeuroML, the
structure and behavior of ion channel, synapse, cell, and network model
descriptions are based on underlying definitions provided in LEMS, a domain-
independent language for expressing hierarchical mathematical models of
physical entities. While declarative approaches for describing models have led
to greater exchange of model elements among software tools in computational
neuroscience, a frequent criticism of XML-based languages is that they are
difficult to work with directly. Here we describe two Application Programming
Interfaces (APIs) written in Python (¡ext-link ext-link-type=”uri”
xlink:href=”http://www.python.org”
xmlns:xlink=”http://www.w3.org/1999/xlink”¿http://www.python.org¡/ext-link¿),
which simplify the process of developing and modifying models expressed in
NeuroML and LEMS. The libNeuroML API provides a Python object model with a
direct mapping to all NeuroML concepts defined by the NeuroML Schema, which
facilitates reading and writing the XML equivalents. In addition, it offers a
memory-efficient, array-based internal representation, which is useful for
handling large-scale connectomics data. The libNeuroML API also includes
support for performing common operations that are required when working with
NeuroML documents. Access to the LEMS data model is provided by the PyLEMS
API, which provides a Python implementation of the LEMS language, including
the ability to simulate most models expressed in LEMS. Together, libNeuroML
and PyLEMS provide a comprehensive solution for interacting with NeuroML
models in a Python environment., author = Vella, Michael and Cannon, Robert C
and Crook, Sharon and Davison, Andrew P and Ganapathy, Gautham and Robinson,
Hugh P C and Silver, R Angus and Gleeson, Padraig, doi =
10.3389/fninf.2014.00038, issn = 1662-5196, journal = Frontiers in
Neuroinformatics, pages = 38, title = libNeuroML and PyLEMS: using Python to
combine procedural and declarative modeling approaches in computational
neuroscience, url =
https://www.frontiersin.org/article/10.3389/fninf.2014.00038, volume = 8, year
= 2014

@ARTICLELEMS,

AUTHOR=Cannon, Robert C. and Gleeson, Padraig and Crook, Sharon and Ganapathy,
Gautham and Marin, Boris and Piasini, Eugenio and Silver, R. Angus,

TITLE=LEMS: a language for expressing complex biological models in concise and
hierarchical form and its use in underpinning NeuroML 2,

JOURNAL=Frontiers in Neuroinformatics,

VOLUME=8,

PAGES=79,

YEAR=2014,

URL=https://www.frontiersin.org/article/10.3389/fninf.2014.00079,

DOI=10.3389/fninf.2014.00079,

ISSN=1662-5196,

ABSTRACT=Computational models are increasingly important for studying complex
neurophysiological systems. As scientific tools, it is essential that such
models can be reproduced and critically evaluated by a range of scientists.
However, published models are currently implemented using a diverse set of
modeling approaches, simulation tools, and computer languages making them
inaccessible and difficult to reproduce. Models also typically contain
concepts that are tightly linked to domain-specific simulators, or depend on
knowledge that is described exclusively in text-based documentation. To
address these issues we have developed a compact, hierarchical, XML-based
language called LEMS (Low Entropy Model Specification), that can define the
structure and dynamics of a wide range of biological models in a fully machine
readable format. We describe how LEMS underpins the latest version of NeuroML
and show that this framework can define models of ion channels, synapses,
neurons and networks. Unit handling, often a source of error when reusing
models, is built into the core of the language by specifying physical
quantities in models in terms of the base dimensions. We show how LEMS,
together with the open source Java and Python based libraries we have
developed, facilitates the generation of scripts for multiple neuronal
simulators and provides a route for simulator free code generation. We
establish that LEMS can be used to define models from systems biology and map
them to neuroscience-domain specific simulators, enabling models to be shared
between these traditionally separate disciplines. LEMS and NeuroML 2 provide a
new, comprehensive framework for defining computational models of neuronal and
other biological systems in a machine readable format, making them more
reproducible and increasing the transparency and accessibility of their
underlying structure and properties.

@miscNumbaWeb, author = Numba, title = Creating NumPy universal functions, url
= https://numba.pydata.org/numba-doc/latest/user/vectorize.html, year = 2012

@inproceedingsNumba, author = Lam, Siu Kwan and Pitrou, Antoine and Seibert,
Stanley, title = Numba: A LLVM-Based Python JIT Compiler, year = 2015, isbn =
9781450340052, publisher = Association for Computing Machinery, address = New
York, NY, USA, url = https://doi.org/10.1145/2833157.2833162, doi =
10.1145/2833157.2833162, abstract = Dynamic, interpreted languages, like
Python, are attractive for domain-experts and scientists experimenting with
new ideas. However, the performance of the interpreter is often a barrier when
scaling to larger data sets. This paper presents a just-in-time compiler for
Python that focuses in scientific and array-oriented computing. Starting with
the simple syntax of Python, Numba compiles a subset of the language into
efficient machine code that is comparable in performance to a traditional
compiled language. In addition, we share our experience in building a JIT
compiler using LLVM[1]., booktitle = Proceedings of the Second Workshop on the
LLVM Compiler Infrastructure in HPC, articleno = 7, numpages = 6, keywords =
Python, compiler, LLVM, location = Austin, Texas, series = LLVM ’15

@articleNeuroML, abstract = Computational models are increasingly important
for studying complex neurophysiological systems. As scientific tools, it is
essential that such models can be reproduced and critically evaluated by a
range of scientists. However, published models are currently implemented using
a diverse set of modeling approaches, simulation tools, and computer languages
making them inaccessible and difficult to reproduce. Models also typically
contain concepts that are tightly linked to domain-specific simulators, or
depend on knowledge that is described exclusively in text-based documentation.
To address these issues we have developed a compact, hierarchical, XML-based
language called LEMS (Low Entropy Model Specification), that can define the
structure and dynamics of a wide range of biological models in a fully machine
readable format. We describe how LEMS underpins the latest version of NeuroML
and show that this framework can define models of ion channels, synapses,
neurons and networks. Unit handling, often a source of error when reusing
models, is built into the core of the language by specifying physical
quantities in models in terms of the base dimensions. We show how LEMS,
together with the open source Java and Python based libraries we have
developed, facilitates the generation of scripts for multiple neuronal
simulators and provides a route for simulator free code generation. We
establish that LEMS can be used to define models from systems biology and map
them to neuroscience-domain specific simulators, enabling models to be shared
between these traditionally separate disciplines. LEMS and NeuroML 2 provide a
new, comprehensive framework for defining computational models of neuronal and
other biological systems in a machine readable format, making them more
reproducible and increasing the transparency and accessibility of their
underlying structure and properties., author = Cannon, Robert C. and Gleeson,
Padraig and Crook, Sharon and Ganapathy, Gautham and Marin, Boris and Piasini,
Eugenio and Silver, R. Angus, doi = 10.3389/fninf.2014.00079, file =
:home/michiel/Downloads/fninf-08-00079.pdf:pdf, issn = 16625196, journal =
Frontiers in Neuroinformatics, keywords = Model description language,Model
sharing,Simulation,Spiking neural networks,Standardization, number = SEP,
pages = 1–21, title = LEMS: A language for expressing complex biological
models in concise and hierarchical form and its use in underpinning NeuroML 2,
volume = 8, year = 2014

@articlePyRates, author = Gast, Richard AND Rose, Daniel AND Salomon,
Christoph AND Möller, Harald E. AND Weiskopf, Nikolaus AND Knösche, Thomas R.,
journal = PLOS ONE, publisher = Public Library of Science, title = PyRates—A
Python framework for rate-based neural simulations, year = 2019, month = 12,
volume = 14, url = https://doi.org/10.1371/journal.pone.0225900, pages = 1-26,
abstract = In neuroscience, computational modeling has become an important
source of insight into brain states and dynamics. A basic requirement for
computational modeling studies is the availability of efficient software for
setting up models and performing numerical simulations. While many such tools
exist for different families of neural models, there is a lack of tools
allowing for both a generic model definition and efficiently parallelized
simulations. In this work, we present PyRates, a Python framework that
provides the means to build a large variety of rate-based neural models.
PyRates provides intuitive access to and modification of all mathematical
operators in a graph, thus allowing for a highly generic model definition. For
computational efficiency and parallelization, the model is translated into a
compute graph. Using the example of two different neural models belonging to
the family of rate-based population models, we explain the mathematical
formalism, software structure and user interfaces of PyRates. We show via
numerical simulations that the behavior of the PyRates model implementations
is consistent with the literature. Finally, we demonstrate the computational
capacities and scalability of PyRates via a number of benchmark simulations of
neural networks differing in size and connectivity., number = 12, doi =
10.1371/journal.pone.0225900

@articlefasttvb, abstract = The Virtual Brain (TVB) is now available as open-
source cloud ecosystem on EBRAINS, a shared digital research platform for
brain science. It offers services for constructing, simulating and analysing
brain network models (BNMs) including the TVB network simulator; magnetic
resonance imaging (MRI) processing pipelines to extract structural and
functional connectomes; multiscale co-simulation of spiking and large-scale
networks; a domain specific language for automatic high-performance code
generation from user-specified models; simulation-ready BNMs of patients and
healthy volunteers; Bayesian inference of epilepsy spread; data and code for
mouse brain simulation; and extensive educational material. TVB cloud services
facilitate reproducible online collaboration and discovery of data assets,
models, and software embedded in scalable and secure workflows, a precondition
for research on large cohort data sets, better generalizability and clinical
translation., archivePrefix = arXiv, arxivId = 2102.05888, author = Schirner,
Michael and Domide, Lia and Perdikis, Dionysios and Triebkorn, Paul and
Stefanovski, Leon and Pai, Roopa and Popa, Paula and Valean, Bogdan and
Palmer, Jessica and Langford, Chloê and Blickensdörfer, André and van der
Vlag, Michiel and Diaz-Pier, Sandra and Peyser, Alexander and Klijn, Wouter
and Pleiter, Dirk and Nahm, Anne and Schmid, Oliver and Woodman, Marmaduke and
Zehl, Lyuba and Fousek, Jan and Petkoski, Spase and Kusch, Lionel and Hashemi,
Meysam and Marinazzo, Daniele and Mangin, Jean-François and Flöel, Agnes and
Akintoye, Simisola and Stahl, Bernd Carsten and Cepic, Michael and Johnson,
Emily and Deco, Gustavo and McIntosh, Anthony R. and Hilgetag, Claus C. and
Morgan, Marc and Schuller, Bernd and Upton, Alex and McMurtrie, Colin and
Dickscheid, Timo and Bjaalie, Jan G. and Amunts, Katrin and Mersmann, Jochen
and Jirsa, Viktor and Ritter, Petra, eprint = 2102.05888, file =
:home/michiel/Downloads/2102.05888.pdf:pdf, title = Brain Modelling as a
Service: The Virtual Brain on EBRAINS, url = http://arxiv.org/abs/2102.05888,
year = 2021

@articleMontbrio2015, abstract = A major goal of neuroscience, statistical
physics, and nonlinear dynamics is to understand how brain function arises
from the collective dynamics of networks of spiking neurons. This challenge
has been chiefly addressed through large-scale numerical simulations.
Alternatively, researchers have formulated mean-field theories to gain insight
into macroscopic states of large neuronal networks in terms of the collective
firing activity of the neurons, or the firing rate. However, these theories
have not succeeded in establishing an exact correspondence between the firing
rate of the network and the underlying microscopic state of the spiking
neurons. This has largely constrained the range of applicability of such
macroscopic descriptions, particularly when trying to describe neuronal
synchronization. Here, we provide the derivation of a set of exact macroscopic
equations for a network of spiking neurons. Our results reveal that the spike
generation mechanism of individual neurons introduces an effective coupling
between two biophysically relevant macroscopic quantities, the firing rate and
the mean membrane potential, which together govern the evolution of the
neuronal network. The resulting equations exactly describe all possible
macroscopic dynamical states of the network, including states of synchronous
spiking activity. Finally, we show that the firing-rate description is
related, via a conformal map, to a low-dimensional description in terms of the
Kuramoto order parameter, called Ott-Antonsen theory. We anticipate that our
results will be an important tool in investigating how large networks of
spiking neurons self-organize in time to process and encode information in the
brain., archivePrefix = arXiv, arxivId = 1506.06581, author = Montbrió, Ernest
and Pazó, Diego and Roxin, Alex, doi = 10.1103/PhysRevX.5.021028, eprint =
1506.06581, file = :home/michiel/Downloads/PhysRevX.5.021028.pdf:pdf, issn =
21603308, journal = Physical Review X, keywords = biological
physics,interdisciplinary physics, number = 2, pages = 1–15, title =
Macroscopic description for networks of spiking neurons, volume = 5, year =
2015

@articleMLart2018, abstract = Advances in experimental techniques and
computational power allowing researchers to gather anatomical and
electrophysiological data at unprecedented levels of detail have fostered the
development of increasingly complex models in computational neuroscience.
Large-scale, biophysically detailed cell models pose a particular set of
computational challenges, and this has led to the development of a number of
domain-specific simulators. At the other level of detail, the ever growing
variety of point neuron models increases the implementation barrier even for
those based on the relatively simple integrate-and-fire neuron model.
Independently of the model complexity, all modeling methods crucially depend
on an efficient and accurate transformation of mathematical model descriptions
into efficiently executable code. Neuroscientists usually publish model
descriptions in terms of the mathematical equations underlying them. However,
actually simulating them requires they be translated into code. This can cause
problems because errors may be introduced if this process is carried out by
hand, and code written by neuroscientists may not be very computationally
efficient. Furthermore, the translated code might be generated for different
hardware platforms, operating system variants or even written in different
languages and thus cannot easily be combined or even compared. Two main
approaches to addressing this issues have been followed. The first is to limit
users to a fixed set of optimized models, which limits flexibility. The second
is to allow model definitions in a high level interpreted language, although
this may limit performance. Recently, a third approach has become increasingly
popular: using code generation to automatically translate high level
descriptions into efficient low level code to combine the best of previous
approaches. This approach also greatly enriches efforts to standardize
simulator-independent model description languages. In the past few years, a
number of code generation pipelines have been developed in the computational
neuroscience community, which differ considerably in aim, scope and
functionality. This article provides an overview of existing pipelines
currently used within the community and contrasts their capabilities and the
technologies and concepts behind them., author = Blundell, Inga and Brette,
Romain and Cleland, Thomas A and Close, Thomas G and Coca, Daniel and Davison,
Andrew P and Diaz-Pier, Sandra and Fernandez Musoles, Carlos and Gleeson,
Padraig and Goodman, Dan F M and Hines, Michael and Hopkins, Michael W and
Kumbhar, Pramod and Lester, David R and Marin, Bóris and Morrison, Abigail and
Müller, Eric and Nowotny, Thomas and Peyser, Alexander and Plotnikov, Dimitri
and Richmond, Paul and Rowley, Andrew and Rumpe, Bernhard and Stimberg, Marcel
and Stokes, Alan B and Tomkins, Adam and Trensch, Guido and Woodman, Marmaduke
and Eppler, Jochen Martin, doi = 10.3389/fninf.2018.00068, issn = 1662-5196,
journal = Frontiers in Neuroinformatics, pages = 68, title = Code Generation
in Computational Neuroscience: A Review of Tools and Techniques, url =
https://www.frontiersin.org/article/10.3389/fninf.2018.00068, volume = 12,
year = 2018

@ARTICLERabuffo2021, title = ”Neuronal cascades shape whole-brain functional
dynamics at rest”, author = ”Rabuffo, Giovanni and Fousek, Jan and Bernard,
Christophe and Jirsa, Viktor”, journal = ”eNeuro”, month = sep, year = 2021,
url = ”http://dx.doi.org/10.1523/ENEURO.0283-21.2021”, keywords = ”EEG/fMRI;
Neuronal Cascades; network modeling; resting state”, issn = ”2373-2822”, pmid
= ”34583933”, doi = ”10.1523/ENEURO.0283-21.2021”

@softwaresubramoney_anand_2019_2590760, author = Subramoney, Anand and Diaz-
Pier, Sandra and Rao, Arjun and Scherr, Franz and Salaj, Darjan and
Bohnstingl, Thomas and Jordan, Jakob and Kopp, Nikolaus and Hackhofer, Daniel
and Stekovic, Sinisa, title = IGITUGraz/L2L: v1.0.0-beta, month = mar, year =
2019, publisher = Zenodo, version = v1.0.0-beta, doi = 10.5281/zenodo.2590760,
url = https://doi.org/10.5281/zenodo.2590760

@miscpeyser2019linking, title=Linking experimental and computational
connectomics, author=Peyser, Alexander and Diaz Pier, Sandra and Klijn, Wouter
and Morrison, Abigail and Triesch, Jochen, year=2019, publisher=MIT Press One
Rogers Street, Cambridge, MA 02142-1209, USA journals-info …

@articlenakagawa2013bottom, title=Bottom up modeling of the connectome:
linking structure and function in the resting brain and their changes in
aging, author=Nakagawa, Tristan T and Jirsa, Viktor K and Spiegler, Andreas
and McIntosh, Anthony R and Deco, Gustavo, journal=Neuroimage, volume=80,
pages=318–329, year=2013, publisher=Elsevier

@articledeco2014identification, title=Identification of optimal structural
connectivity using functional connectivity and neural modeling, author=Deco,
Gustavo and McIntosh, Anthony R and Shen, Kelly and Hutchison, R Matthew and
Menon, Ravi S and Everling, Stefan and Hagmann, Patric and Jirsa, Viktor K,
journal=Journal of Neuroscience, volume=34, number=23, pages=7910–7916,
year=2014, publisher=Soc Neuroscience

@articlefalcon2016new, title=A new neuroinformatics approach to personalized
medicine in neurology: The Virtual Brain, author=Falcon, Maria I and Jirsa,
Viktor and Solodkin, Ana, journal=Current opinion in neurology, volume=29,
number=4, pages=429, year=2016, publisher=NIH Public Access

@articleeinevoll2019scientific, title=The scientific case for brain
simulations, author=Einevoll, Gaute T and Destexhe, Alain and Diesmann, Markus
and Grün, Sonja and Jirsa, Viktor and de Kamps, Marc and Migliore, Michele and
Ness, Torbjørn V and Plesser, Hans E and Schürmann, Felix, journal=Neuron,
volume=102, number=4, pages=735–744, year=2019, publisher=Elsevier

@articlelynn2019physics, title=The physics of brain network structure,
function and control, author=Lynn, Christopher W and Bassett, Danielle S,
journal=Nature Reviews Physics, volume=1, number=5, pages=318–332, year=2019,
publisher=Nature Publishing Group

@articlebansal2018personalized, title=Personalized brain network models for
assessing structure–function relationships, author=Bansal, Kanika and Nakuci,
Johan and Muldoon, Sarah Feldt, journal=Current Opinion in Neurobiology,
volume=52, pages=42–47, year=2018, publisher=Elsevier

@articlehashemi2020bayesian, title=The Bayesian Virtual Epileptic Patient: A
probabilistic framework designed to infer the spatial map of epileptogenicity
in a personalized large-scale brain model of epilepsy spread, author=Hashemi,
Meysam and Vattikonda, AN and Sip, V and Guye, Maxime and Bartolomei, Fabrice
and Woodman, Michael Marmaduke and Jirsa, Viktor K, journal=NeuroImage,
volume=217, pages=116839, year=2020, publisher=Elsevier

@misccuda, author=NVIDIA and Vingelmann, Péter and Fitzek, Frank H.P.,
title=CUDA, release: 10.2.89, year=2020,
url=https://developer.nvidia.com/cuda-toolkit,

@articlejirsa2014nature, title=On the nature of seizure dynamics,
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Ivanov, Anton I and Bernard, Christophe, journal=Brain, volume=137, number=8,
pages=2210–2230, year=2014, publisher=Oxford University Press

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@articlewong2006recurrent, title=A recurrent network mechanism of time
integration in perceptual decisions, author=Wong, Kong-Fatt and Wang, Xiao-
Jing, journal=Journal of Neuroscience, volume=26, number=4, pages=1314–1328,
year=2006, publisher=Soc Neuroscience

@articlevan2020exploring, title=Exploring Complex Brain-Simulation Workloads
on Multi-GPU Deployments., author=van der Vlag, Michiel A and Smaragdos,
Georgios and Al-Ars, Zaid and Strydis, Christos, journal=ACM Trans. Archit.
Code Optim., volume=16, number=4, pages=53–1, year=2020

@inproceedingsschemmel2010wafer, title=A wafer-scale neuromorphic hardware
system for large-scale neural modeling, author=Schemmel, Johannes and
Brüderle, Daniel and Grübl, Andreas and Hock, Matthias and Meier, Karlheinz
and Millner, Sebastian, booktitle=2010 IEEE International Symposium on
Circuits and Systems (ISCAS), pages=1947–1950, year=2010, organization=IEEE

@INPROCEEDINGS arbor2019, author=N. Abi Akar and B. Cumming and V. Karakasis
and A. Küsters and W. Klijn and A. Peyser and S. Yates, booktitle=2019 27th
Euromicro International Conference on Parallel, Distributed and Network-Based
Processing (PDP), title=Arbor — A Morphologically-Detailed Neural Network
Simulation Library for Contemporary High-Performance Computing Architectures,
year=2019, month=feb, volume=, number=, pages=274–282,
doi=10.1109/EMPDP.2019.8671560, ISSN=2377-5750

@articlefurber2012overview, title=Overview of the spinnaker system
architecture, author=Furber, Steve B and Lester, David R and Plana, Luis A and
Garside, Jim D and Painkras, Eustace and Temple, Steve and Brown, Andrew D,
journal=IEEE Transactions on Computers, volume=62, number=12, pages=2454–2467,
year=2012, publisher=IEEE

@articlefox2007spontaneous, title=Spontaneous fluctuations in brain activity
observed with functional magnetic resonance imaging, author=Fox, Michael D and
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pages=700–711, year=2007, publisher=Nature Publishing Group

@misctensorflow2015-whitepaper, title= TensorFlow: Large-Scale Machine
Learning on Heterogeneous Systems, url=https://www.tensorflow.org/,
note=Software available from tensorflow.org, author= Martín Abadi and Ashish
Agarwal and Paul Barham and Eugene Brevdo and Zhifeng Chen and Craig Citro and
Greg S. Corrado and Andy Davis and Jeffrey Dean and Matthieu Devin and Sanjay
Ghemawat and Ian Goodfellow and Andrew Harp and Geoffrey Irving and Michael
Isard and Yangqing Jia and Rafal Jozefowicz and Lukasz Kaiser and Manjunath
Kudlur and Josh Levenberg and Dandelion Mané and Rajat Monga and Sherry Moore
and Derek Murray and Chris Olah and Mike Schuster and Jonathon Shlens and
Benoit Steiner and Ilya Sutskever and Kunal Talwar and Paul Tucker and Vincent
Vanhoucke and Vijay Vasudevan and Fernanda Viégas and Oriol Vinyals and Pete
Warden and Martin Wattenberg and Martin Wicke and Yuan Yu and Xiaoqiang Zheng,
year=2015,

@articleGeNN, abstract = Large-scale numerical simulations of detailed brain
circuit models are important for identifying hypotheses on brain functions and
testing their consistency and plausibility. An ongoing challenge for
simulating realistic models is, however, computational speed. In this paper,
we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to
facilitate the use of graphics accelerators for computational models of large-
scale neuronal networks to address this challenge. GeNN is an open source
library that generates code to accelerate the execution of network simulations
on NVIDIA GPUs, through a flexible and extensible interface, which does not
require in-depth technical knowledge from the users. We present performance
benchmarks showing that 200-fold speedup compared to a single core of a CPU
can be achieved for a network of one million conductance based Hodgkin-Huxley
neurons but that for other models the speedup can differ. GeNN is available
for Linux, Mac OS X and Windows platforms. The source code, user manual,
tutorials, Wiki, in-depth example projects and all other related information
can be found on the project website http://genn-team.github.io/genn/., author
= Yavuz, Esin and Turner, James and Nowotny, Thomas, doi = 10.1038/srep18854,
file = :home/michiel/Documents/Atrticles/rateML/srep18854.pdf:pdf, issn =
20452322, journal = Scientific Reports, month = jan, pages = 18854, pmid =
26740369, publisher = The Author(s), title = GeNN: A code generation framework
for accelerated brain simulations, url = http://dx.doi.org/10.1038/srep18854
http://10.0.4.14/srep18854
https://www.nature.com/articles/srep18854#supplementary-information, volume =
6, year = 2016

@articleStimberg2019, title = Brian 2, an intuitive and efficient neural
simulator, volume = 8, issn = 2050-084X, doi = 10.7554/eLife.47314, journal =
eLife, author = Stimberg, Marcel and Brette, Romain and Goodman, Dan FM,
editor = Skinner, Frances K, month = aug, year = 2019, pages = e47314

@inproceedingsmci/Plotnikov2016, author = Plotnikov, Dimitri AND Blundell,
Inga AND Ippen, Tammo AND Eppler, Jochen Martin AND Morrison, Abigail AND
Rumpe, Bernhard, title = NESTML: a modeling language for spiking neurons,
booktitle = Modellierung 2016, year = 2016, editor = Oberweis, Andreas AND
Reussner, Ralf , pages = 93-108 , publisher = Gesellschaft für Informatik
e.V., address = Bonn

@InbookDavison2013, author=Davison, Andrew, editor=Jaeger, Dieter and Jung,
Ranu, title=”NineML”, bookTitle=”Encyclopedia of Computational Neuroscience”,
year=”2013”, publisher=”Springer New York”, address=”New York, NY”,
pages=”1–2”, isbn=”978-1-4614-7320-6”, doi=”10.1007/978-1-4614-7320-6_375-2”,
url=”https://doi.org/10.1007/978-1-4614-7320-6_375-2”

@Article harris2020array, title = Array programming with NumPy, author =
Charles R. Harris and K. Jarrod Millman and Stéfan J. van der Walt and Ralf
Gommers and Pauli Virtanen and David Cournapeau and Eric Wieser and Julian
Taylor and Sebastian Berg and Nathaniel J. Smith and Robert Kern and Matti
Picus and Stephan Hoyer and Marten H. van Kerkwijk and Matthew Brett and Allan
Haldane and Jaime Fernández del Río and Mark Wiebe and Pearu Peterson and
Pierre Gérard-Marchant and Kevin Sheppard and Tyler Reddy and Warren Weckesser
and Hameer Abbasi and Christoph Gohlke and Travis E. Oliphant, year = 2020,
month = sep, journal = Nature, volume = 585, number = 7825, pages = 357–362,
doi = 10.1038/s41586-020-2649-2, publisher = Springer Science and Business
Media LLC, url = https://doi.org/10.1038/s41586-020-2649-2

@miscCurand, author = NVIDIA, Corporation, journal = NVIDIA Corporation, Santa
Clara, California, title = CURAND library, url =
http://scholar.google.com/scholar?hl=en&btnG=Searchq=intitle:CURAND+Library#0,
year = 2008

@articlemolder2021sustainable, title=Sustainable data analysis with Snakemake,
author=Mölder, Felix and Jablonski, Kim Philipp and Letcher, Brice and Hall,
Michael B and Tomkins-Tinch, Christopher H and Sochat, Vanessa and Forster,
Jan and Lee, Soohyun and Twardziok, Sven O and Kanitz, Alexander and others,
journal=F1000Research, volume=10, year=2021, publisher=Faculty of 1000 Ltd

@articleCABRAL2011130, title = Role of local network oscillations in resting-
state functional connectivity, journal = NeuroImage, volume = 57, number = 1,
pages = 130-139, year = 2011, issn = 1053-8119, doi =
https://doi.org/10.1016/j.neuroimage.2011.04.010, url =
https://www.sciencedirect.com/science/article/pii/S1053811911003880, author =
Joana Cabral and Etienne Hugues and Olaf Sporns and Gustavo Deco, keywords =
Resting state, Network oscillations, Modeling, Structural connectivity,
Functional connectivity, Default-mode network, abstract = Spatio-temporally
organized low-frequency fluctuations (¡0.1Hz), observed in BOLD fMRI signal
during rest, suggest the existence of underlying network dynamics that emerge
spontaneously from intrinsic brain processes. Furthermore, significant
correlations between distinct anatomical regions—or functional connectivity
(FC)—have led to the identification of several widely distributed resting-
state networks (RSNs). This slow dynamics seems to be highly structured by
anatomical connectivity but the mechanism behind it and its relationship with
neural activity, particularly in the gamma frequency range, remains largely
unknown. Indeed, direct measurements of neuronal activity have revealed
similar large-scale correlations, particularly in slow power fluctuations of
local field potential gamma frequency range oscillations. To address these
questions, we investigated neural dynamics in a large-scale model of the human
brain’s neural activity. A key ingredient of the model was a structural brain
network defined by empirically derived long-range brain connectivity together
with the corresponding conduction delays. A neural population, assumed to
spontaneously oscillate in the gamma frequency range, was placed at each
network node. When these oscillatory units are integrated in the network, they
behave as weakly coupled oscillators. The time-delayed interaction between
nodes is described by the Kuramoto model of phase oscillators, a biologically-
based model of coupled oscillatory systems. For a realistic setting of axonal
conduction speed, we show that time-delayed network interaction leads to the
emergence of slow neural activity fluctuations, whose patterns correlate
significantly with the empirically measured FC. The best agreement of the
simulated FC with the empirically measured FC is found for a set of parameters
where subsets of nodes tend to synchronize although the network is not
globally synchronized. Inside such clusters, the simulated BOLD signal between
nodes is found to be correlated, instantiating the empirically observed RSNs.
Between clusters, patterns of positive and negative correlations are observed,
as described in experimental studies. These results are found to be robust
with respect to a biologically plausible range of model parameters. In
conclusion, our model suggests how resting-state neural activity can originate
from the interplay between the local neural dynamics and the large-scale
structure of the brain.

@articleKlockner2012, abstract = A common lament in the field of scientific
computing concerns the ever-widening gap between hypothetical machine
capabilities and the effort an individual programmer is able to spend to move
a computational application towards exploiting a machine to the full extent of
its capability. More sophisticated tools, compilers, and libraries are
generally hoped to level this field, enabling users to achieve good results
even with modest investment. PyCUDA is a contribution to the tools for
graphics processing unit (GPU) computing. PyCUDA has a two-fold aim. First, it
aims to simplify the usage of existing basic concepts of CUDA C. Importantly,
it does not attempt to change or reinvent the basic notions of GPU
programming, but instead, as a foundation for further tools, just exposes them
as is through a carefully engineered interface. Key features of this interface
include generous use of sensible defaults, automatic error checking, and
automatic resource management. Second, and strictly on top of the first, basic
layer, PyCUDA provides abstractions that support a number of very common usage
patterns involving a GPU equivalent of NumPy arrays. Although PyCUDA gives the
user access to a compiled language (CUDA C), it attempts to avoid the
development iteration penalty commonly associated with compiled languages,
instead retaining the satisfaction and immediacy of scripting. PyCUDA and
PyOpenCL, as open-source projects, thrive on user feedback, particularly
feedback regarding limitations, bugs, or missing features that users
encounter. 2012 Elsevier Inc. All rights reserved., archivePrefix = arXiv,
arxivId = 1304.5553, author = Klöckner, Andreas and Pinto, Nicolas and
Catanzaro, Bryan and Lee, Yunsup and Ivanov, Paul and Fasih, Ahmed, doi =
10.1016/B978-0-12-385963-1.00027-7, eprint = 1304.5553, file =
:home/michiel/Downloads/1304.5553.pdf:pdf, isbn = 9780123859631, journal = GPU
Computing Gems Jade Edition, pages = 373–385, title = GPU Scripting and Code
Generation with PyCUDA, year = 2012 @articleOtt2008, abstract = It is shown
that, in the infinite size limit, certain systems of globally coupled phase
oscillators display low dimensional dynamics. In particular, we derive an
explicit finite set of nonlinear ordinary differential equations for the
macroscopic evolution of the systems considered. For example, an exact, closed
form solution for the nonlinear time evolution of the Kuramoto problem with a
Lorentzian oscillator frequency distribution function is obtained. Low
dimensional behavior is also demonstrated for several prototypical extensions
of the Kuramoto model, and time-delayed coupling is also considered., author =
Ott, Edward and Antonsen, Thomas M, doi = 10.1063/1.2930766, issn = 1089-7682
(Electronic), journal = Chaos (Woodbury, N.Y.), keywords =
Algorithms,Biological Clocks,Computer Simulation,Feedback,Metabolic Networks
and Pathways,Models, Theoretical,Nerve Net,Nonlinear
Dynamics,Oscillometry,methods,physiology, month = sep, number = 3, pages =
37113, pmid = 19045487, title = Low dimensional behavior of large systems of
globally coupled oscillators., volume = 18, year = 2008

@InProceedingsLattnerAdve:tutorial, author=Chris Lattner and Vikram Adve,
title=”The LLVM Compiler Framework and Infrastructure Tutorial”, month=Sep,
year=2004, address=West Lafayette, Indiana, booktitle=”LCPC’04 Mini Workshop
on Compiler Research Infrastructures”

@articlerateml2022, abstract=Whole brain network models are now an established
tool in scientific and clinical research, however their use in a larger
workflow still adds significant informatics complexity. We propose a tool,
RateML, that enables users to generate such models from a succinct declarative
description, in which the mathematics of the model are described without
specifying how their simulation should be implemented. RateML builds on
NeuroML’s Low Entropy Model Specification (LEMS), an XML based language for
specifying models of dynamical systems, allowing descriptions of neural mass
and discretized neural field models, as implemented by the Virtual Brain (TVB)
simulator: the end user describes their model’s mathematics once and generates
and runs code for different languages, targeting both CPUs for fast single
simulations and GPUs for parallel ensemble simulations. High performance
parallel simulations are crucial for tuning many parameters of a model to
empirical data such as functional magnetic resonance imaging (fMRI), with
reasonable execution times on small or modest hardware resources.
Specifically, while RateML can generate Python model code, it enables
generation of Compute Unified Device Architecture C++ code for NVIDIA GPUs.
When a CUDA implementation of a model is generated, a tailored model driver
class is produced, enabling the user to tweak the driver by hand and perform
the parameter sweep. The model and driver can be executed on any compute
capable NVIDIA GPU with a high degree of parallelization, either locally or in
a compute cluster environment. The results reported in this manuscript show
that with the CUDA code generated by RateML, it is possible to explore
thousands of parameter combinations with a single Graphics Processing Unit for
different models, substantially reducing parameter exploration times and
resource usage for the brain network models, in turn accelerating the research
workflow itself. This provides a new tool to create efficient and broader
parameter fitting workflows, support studies on larger cohorts, and derive
more robust and statistically relevant conclusions about brain dynamics.,
author=van der Vlag, Michiel and Woodman, Marmaduke and Fousek, Jan and Diaz-
Pier, Sandra and Pérez Martín, Aarón and Jirsa, Viktor and Morrison, Abigail,
title=RateML: A Code Generation Tool for Brain Network Models,
journal=Frontiers in Network Physiology, volume=2, year=2022,
url=https://www.frontiersin.org/articles/10.3389/fnetp.2022.826345,
doi=10.3389/fnetp.2022.826345, issn=2674-0109

@ARTICLEGoldman2023,

AUTHOR=Goldman, Jennifer S. and Kusch, Lionel and Aquilue, David and
Yalçınkaya, Bahar Hazal and Depannemaecker, Damien and Ancourt, Kevin and
Nghiem, Trang-Anh E. and Jirsa, Viktor and Destexhe, Alain,

TITLE=A comprehensive neural simulation of slow-wave sleep and highly
responsive wakefulness dynamics,

JOURNAL=Frontiers in Computational Neuroscience,

VOLUME=16,

YEAR=2023,

URL=https://www.frontiersin.org/articles/10.3389/fncom.2022.1058957,

DOI=10.3389/fncom.2022.1058957,

ISSN=1662-5188,

ABSTRACT=Hallmarks of neural dynamics during healthy human brain states span
spatial scales from neuromodulators acting on microscopic ion channels to
macroscopic changes in communication between brain regions. Developing a
scale-integrated understanding of neural dynamics has therefore remained
challenging. Here, we perform the integration across scales using mean-field
modeling of Adaptive Exponential (AdEx) neurons, explicitly incorporating
intrinsic properties of excitatory and inhibitory neurons. The model was run
using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. We
report that when AdEx mean-field neural populations are connected via
structural tracts defined by the human connectome, macroscopic dynamics
resembling human brain activity emerge. Importantly, the model can
qualitatively and quantitatively account for properties of empirically
observed spontaneous and stimulus-evoked dynamics in space, time, phase, and
frequency domains. Large-scale properties of cortical dynamics are shown to
emerge from both microscopic-scale adaptation that control transitions between
wake-like to sleep-like activity, and the organization of the human structural
connectome; together, they shape the spatial extent of synchrony and phase
coherence across brain regions consistent with the propagation of sleep-like
spontaneous traveling waves at intermediate scales. Remarkably, the model also
reproduces brain-wide, enhanced responsiveness and capacity to encode
information particularly during wake-like states, as quantified using the
perturbational complexity index. The model was run using The Virtual Brain
(TVB) simulator, and is open-access in EBRAINS. This approach not only
provides a scale-integrated understanding of brain states and their underlying
mechanisms, but also open access tools to investigate brain responsiveness,
toward producing a more unified, formal understanding of experimental data
from conscious and unconscious states, as well as their associated
pathologies.

@article Aquilu2023, author = David Aquilué-Llorens and Jennifer S. Goldman
and Alain Destexhe, title = High-density exploration of activity states in a
multi-area brain model, elocation-id = 2023.05.18.541285, year = 2023, doi =
10.1101/2023.05.18.541285, publisher = Cold Spring Harbor Laboratory, abstract
= Biophysically-grounded whole-brain models were built recently using
tractography data to interconnect multiple mesoscopic models, which can
simulate the dynamics of neuronal populations with only a few equations. Mean-
field models of neural populations, specifically the Adapting AdEx mean-field,
was used for this purpose because it can integrate key biophysical mechanisms
such as spike-frequency adaptation and its regulation at cellular scales, to
the emergence of brain-scale dynamics. Using this approach, with the Virtual
Brain (TVB) environment, it has been possible to model the macroscopic
transitions between brain states, described by variation in brain-scale
dynamics between asynchronous and rapid dynamics during conscious brain
states, and synchronized slow-waves, with Up-and-Down state dynamics during
unconscious brain states, emerging from mechanisms at the cellular level.
Transitions between brain states are driven by changes in neuromodulation that
can be due to intrinsic regulation during sleep-wake cycles or extrinsic
factors such as anesthetics, which, in turn, affect spike-frequency
adaptation. Here, we perform a dense grid parameter exploration of the TVB-
AdEx model, making use of High Performance Computing, to thoroughly explore
the properties of this model. We find that there is a remarkable robustness of
the effect of adaptation to induce synchronized slow-wave activity. Moreover,
the occurrence of slow waves is often paralleled with a closer relation
between functional and structural connectivity. We find that hyperpolarization
can also generate unconscious-like synchronized Up and Down states, which may
be a mechanism underlying the action of anesthetics. We conclude that the
parameter space of the TVB-AdEx model reveals features identified
experimentally in sleep and anesthesia.Competing Interest StatementThe authors
have declared no competing interest., URL =
https://www.biorxiv.org/content/early/2023/05/18/2023.05.18.541285, eprint =
https://www.biorxiv.org/content/early/2023/05/18/2023.05.18.541285.full.pdf,
journal = bioRxiv

@articledivolo, author = di Volo, Matteo and Romagnoni, Alberto and Capone,
Cristiano and Destexhe, Alain, title = ”Biologically Realistic Mean-Field
Models of Conductance-Based Networks of Spiking Neurons with Adaptation”,
journal = Neural Computation, volume = 31, number = 4, pages = 653-680, year =
2019, month = 04, abstract = ”Accurate population models are needed to build
very large-scale neural models, but their derivation is difficult for
realistic networks of neurons, in particular when nonlinear properties are
involved, such as conductance-based interactions and spike-frequency
adaptation. Here, we consider such models based on networks of adaptive
exponential integrate-and-fire excitatory and inhibitory neurons. Using a
master equation formalism, we derive a mean-field model of such networks and
compare it to the full network dynamics. The mean-field model is capable of
correctly predicting the average spontaneous activity levels in asynchronous
irregular regimes similar to in vivo activity. It also captures the transient
temporal response of the network to complex external inputs. Finally, the
mean-field model is also able to quantitatively describe regimes where high-
and low-activity states alternate (up-down state dynamics), leading to slow
oscillations. We conclude that such mean-field models are biologically
realistic in the sense that they can capture both spontaneous and evoked
activity, and they naturally appear as candidates to build very large-scale
models involving multiple brain areas.”, issn = 0899-7667, doi =
10.1162/neco_a_01173, url = https://doi.org/10.1162/neco_a_01173, eprint =
https://direct.mit.edu/neco/article-pdf/31/4/653/1050655/neco_a_01173.pdf,

@articleneurolibCakan2021, abstract = neurolib is a computational framework
for whole-brain modeling written in Python. It provides a set of neural mass
models that represent the average activity of a brain region on a mesoscopic
scale. In a whole-brain network model, brain regions are connected with each
other based on biologically informed structural connectivity, i.e., the
connectome of the brain. neurolib can load structural and functional datasets,
set up a whole-brain model, manage its parameters, simulate it, and organize
its outputs for later analysis. The activity of each brain region can be
converted into a simulated BOLD signal in order to calibrate the model against
empirical data from functional magnetic resonance imaging (fMRI). Extensive
model analysis is made possible using a parameter exploration module, which
allows one to characterize a model’s behavior as a function of changing
parameters. An optimization module is provided for fitting models to
multimodal empirical data using evolutionary algorithms. neurolib is designed
to be extendable and allows for easy implementation of custom neural mass
models, offering a versatile platform for computational neuroscientists for
prototyping models, managing large numerical experiments, studying the
structure–function relationship of brain networks, and for performing in-
silico optimization of whole-brain models., author = Cakan, Caglar and Jajcay,
Nikola and Obermayer, Klaus, doi = 10.1007/s12559-021-09931-9, file =
:home/michiel/Downloads/s12559-021-09931-9.pdf:pdf, issn = 18669964, journal =
Cognitive Computation, keywords = Brain networks,Neural mass
model,Neuroinformatics,Whole-brain model, pages = 1132–1152, publisher =
Springer US, title = neurolib: A Simulation Framework for Whole-Brain Neural
Mass Modeling, url = https://doi.org/10.1007/s12559-021-09931-9, volume = 2,
year = 2021

@ARTICLEyegenoglu2022, AUTHOR=Yegenoglu, Alper and Subramoney, Anand and
Hater, Thorsten and Jimenez-Romero, Cristian and Klijn, Wouter and Pérez
Martín, Aarón and van der Vlag, Michiel and Herty, Michael and Morrison,
Abigail and Diaz-Pier, Sandra, TITLE=Exploring Parameter and Hyper-Parameter
Spaces of Neuroscience Models on High Performance Computers With Learning to
Learn, JOURNAL=Frontiers in Computational Neuroscience, VOLUME=16, YEAR=2022,
URL=https://www.frontiersin.org/articles/10.3389/fncom.2022.885207,
DOI=10.3389/fncom.2022.885207, ISSN=1662-5188,

@articledomingo2017multimodal, title=Multimodal mechanistic signatures for
neurodegenerative diseases (NeuroMMSig): a web server for mechanism
enrichment, author=Domingo-Fernández, Daniel and Kodamullil, Alpha Tom and
Iyappan, Anandhi and Naz, Mufassra and Emon, Mohammad Asif and Raschka, Tamara
and Karki, Reagon and Springstubbe, Stephan and Ebeling, Christian and
Hofmann-Apitius, Martin, journal=Bioinformatics, volume=33, number=22,
pages=3679–3681, year=2017, publisher=Oxford University Press

@articlestefanovski2019linking, title=Linking molecular pathways and large-
scale computational modeling to assess candidate disease mechanisms and
pharmacodynamics in Alzheimer’s disease, 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
and Alzheimer’s Disease Neuroimaging Initiative, journal=Frontiers in
computational neuroscience, volume=13, pages=54, year=2019,
publisher=Frontiers Media SA

@miscsqlite2020hipp, title=SQLite, url=https://www.sqlite.org/index.html,
version=3.31.1, year=2020, author=Hipp, Richard D

@articlesilva_pereira_effect_2017, title = Effect of field spread on resting-
state MEG functional network analysis: a computational modeling study, volume
= 7, shorttitle = Effect of field spread on resting-state MEG functional
network analysis, doi = 10.1089/brain.2017.0525, abstract = A popular way to
analyze resting-state EEG and MEG data is to treat them as a functional
network in which sensors are identified with nodes and the interaction between
channel time-series with the network connections. Although conceptually
appealing, the network-theoretical approach to sensor-level EEG and MEG data
is challenged by the fact that EEG and MEG time-series are mixtures of source
activity. It is therefore of interest to assess the relationship between
functional networks of source activity and the ensuing sensor-level networks.
Since these topological features are of high interest in experimental studies,
we address the question of to what extent the network topology can be
reconstructed from sensor-level functional connectivity measures in case of
MEG data. Simple simulations that consider only a small number of regions do
not allow to assess network properties; therefore, we use a diffusion MRI-
constrained whole-brain computational model of resting-state activity. Our
motivation lies behind the fact that still many contributions found in the
literature perform network analysis at sensor level, and we aim at showing the
discrepancies between source- and sensor-level network topologies using
realistic simulations of resting-state cortical activity. Our main findings
are that the effect of field spread on network topology depends on the type of
interaction (instantaneous or lagged) and leads to an underestimation of
lagged functional connectivity at sensor level due to instantaneous mixing of
cortical signals, instantaneous interaction is more sensitive to field spread
than lagged interaction, and discrepancies are reduced when using planar
gradiometers rather than axial gradiometers. We therefore recommend to use
lagged interaction measures on planar gradiometer data when investigating
network properties of resting-state sensor-level MEG data., journal = Brain
connectivity, author = Silva Pereira, Silvana and Hindriks, Rikkert and
Mühlberg, Stefanie and Maris, Eric and Ede, Freek and Griffa, Alessandra and
Hagmann, Patrick and Deco, Gustavo, month = sep, year = 2017, file = Full Text
PDF:/home/kusch/Zotero/storage/PKZFAREM/Silva Pereira et al. - 2017 - Effect
of field spread on resting-state MEG functi.pdf:application/pdf,

@articleSazonov2009, abstract = The phase locking index (PLI) was introduced
to quantify in a statistical sense the phase synchronization of two signals.
It has been commonly used to process biosignals. In this article, we
investigate the PLI for measuring the interdependency of cortical source
signals (CSSs) recorded in the Electroencephalogram (EEG). To this end, we
consider simple analytical models for the mapping of simulated CSSs into the
EEG. For these models, the PLI is investigated analytically and through
numerical simulations. An evaluation is made of the sensitivity of the PLI to
the amount of crosstalk between the sources through biological tissues of the
head. It is found that the PLI is a useful interdependency measure for CSSs,
especially when the amount of crosstalk is small. Another common
interdependency measure is the coherence. A direct comparison of both measures
has not been made in the literature so far. We assess the performance of the
PLI and coherence for estimation and detection purposes based on,
respectively, a normalized variance and a novel statistical measure termed
contrast. Based on these performance measures, it is found that the PLI is
similar or better than the CM in most cases. This result is also confirmed
through analysis of EEGs recorded from epileptic patients., author = Sazonov,
Andrei V. and Ho, Chin Keong and Bergmans, Jan W.M. and Arends, Johan B.A.M.
and Griep, Paul A.M. and Verbitskiy, Evgeny A. and Cluitmans, Pierre J.M. and
Boon, Paul A.J.M., doi = 10.1007/s00422-008-0283-4, file =
:home/michiel/Documents/Articles/Analysis/422_2008_Article_283.pdf:pdf, issn =
03401200, journal = Biological Cybernetics, keywords =
Contrast,Correlation,Cortical sources,Coupling,Distribution
function,EEG,Interdependency,Model,Phase locking, number = 2, pages = 129–146,
pmid = 19152066, title = An investigation of the phase locking index for
measuring of interdependency of cortical source signals recorded in the EEG,
volume = 100, year = 2009

@articleSTEPHAN2007387, title = Comparing hemodynamic models with DCM, journal
= NeuroImage, volume = 38, number = 3, pages = 387-401, year = 2007, issn =
1053-8119, doi = https://doi.org/10.1016/j.neuroimage.2007.07.040, url =
https://www.sciencedirect.com/science/article/pii/S1053811907006489, author =
Klaas Enno Stephan and Nikolaus Weiskopf and Peter M. Drysdale and Peter A.
Robinson and Karl J. Friston, keywords = Dynamic causal modeling, Balloon
model, Effective connectivity, BOLD signal, Bayesian model selection, System
identification, abstract = The classical model of blood oxygen level-dependent
(BOLD) responses by Buxton et al. [Buxton, R.B., Wong, E.C., Frank, L.R.,
1998. Dynamics of blood flow and oxygenation changes during brain activation:
the Balloon model. Magn. Reson. Med. 39, 855–864] has been very important in
providing a biophysically plausible framework for explaining different aspects
of hemodynamic responses. It also plays an important role in the hemodynamic
forward model for dynamic causal modeling (DCM) of fMRI data. A recent study
by Obata et al. [Obata, T., Liu, T.T., Miller, K.L., Luh, W.M., Wong, E.C.,
Frank, L.R., Buxton, R.B., 2004. Discrepancies between BOLD and flow dynamics
in primary and supplementary motor areas: application of the Balloon model to
the interpretation of BOLD transients. NeuroImage 21, 144–153] linearized the
BOLD signal equation and suggested a revised form for the model coefficients.
In this paper, we show that the classical and revised models are special cases
of a generalized model. The BOLD signal equation of this generalized model can
be reduced to that of the classical Buxton model by simplifying the
coefficients or can be linearized to give the Obata model. Given the
importance of hemodynamic models for investigating BOLD responses and analyses
of effective connectivity with DCM, the question arises which formulation is
the best model for empirically measured BOLD responses. In this article, we
address this question by embedding different variants of the BOLD signal
equation in a well-established DCM of functional interactions among visual
areas. This allows us to compare the ensuing models using Bayesian model
selection. Our model comparison approach had a factorial structure, comparing
eight different hemodynamic models based on (i) classical vs. revised forms
for the coefficients, (ii) linear vs. non-linear output equations, and (iii)
fixed vs. free parameters, ε, for region-specific ratios of intra- and
extravascular signals. Using fMRI data from a group of twelve subjects, we
demonstrate that the best model is a non-linear model with a revised form for
the coefficients, in which ε is treated as a free parameter.

@articlebrette_adaptive_2005, title = Adaptive Exponential Integrate-and-Fire
Model as an Effective Description of Neuronal Activity, volume = 94, issn =
0022-3077, url = http://www.physiology.org/doi/10.1152/jn.00686.2005, doi =
10.1152/jn.00686.2005, abstract = We introduce a two-dimensional integrate-
and-fire model that combines an exponential spike mechanism with an adaptation
equation, based on recent theoretical findings. We describe a systematic
method to estimate its parameters with simple electrophysiological protocols
(current-clamp injection of pulses and ramps) and apply it to a detailed
conductance-based model of a regular spiking neuron. Our simple model predicts
correctly the timing of 96% of the spikes (±2 ms) of the detailed model in
response to injection of noisy synaptic conductances. The model is especially
reliable in high-conductance states, typical of cortical activity in vivo, in
which intrinsic conductances were found to have a reduced role in shaping
spike trains. These results are promising because this simple model has enough
expressive power to reproduce qualitatively several electrophysiological
classes described in vitro., number = 5, urldate = 2018-02-15, journal =
Journal of Neurophysiology, author = Brette, Romain and Gerstner, Wulfram,
month = nov, year = 2005, pages = 3637–3642, file = Full Text
PDF:/home/kusch/Zotero/storage/UNMA9QRR/Brette and Gerstner - 2005 - Adaptive
Exponential Integrate-and-Fire Model as
a.pdf:application/pdf;Snapshot:/home/kusch/Zotero/storage/FENAKEFP/jn.00686.html:text/html,

@articleboustani_master_2009, title = A Master Equation Formalism for
Macroscopic Modeling of Asynchronous Irregular Activity States, language = en,
author = El Boustani, Sami and Destexhe, Alain, year = 2009, keywords =
Neurons, Action Potentials, Animals, Cerebral Cortex, Computer Simulation,
Linear Models, Models, Neurological, Neural Inhibition, Neural Networks
(Computer), Time Factors, Wakefulness, pages = 55, file = Boustani et Destexhe
- A Master Equation Formalism for Macroscopic
Modeli.pdf:/home/kusch/Zotero/storage/CNGFYG79/Boustani et Destexhe - A Master
Equation Formalism for Macroscopic
Modeli.pdf:application/pdf;Snapshot:/home/kusch/Zotero/storage/8NZQ6JXG/A-Master-
Equation-Formalism-for-Macroscopic.html:text/html;Submitted
Version:/home/kusch/Zotero/storage/YG426HB7/El Boustani and Destexhe - 2009 -
A Master Equation Formalism for Macroscopic Modeli.pdf:application/pdf,

@articleCarlu2020, doi = 10.1152/jn.00399.2019, url =
https://doi.org/10.1152/jn.00399.2019, year = 2020, month = mar, publisher =
American Physiological Society, volume = 123, number = 3, pages = 1042–1051,
author = M. Carlu and O. Chehab and L. Dalla Porta and D. Depannemaecker and
C. Héricé and M. Jedynak and E. Köksal Ersöz and P. Muratore and S. Souihel
and C. Capone and Y. Zerlaut and A. Destexhe and M. di Volo, title = A mean-
field approach to the dynamics of networks of complex neurons, from nonlinear
Integrate-and-Fire to HodgkinHuxley models, journal = Journal of
Neurophysiology