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63	63	== The Virtual Brain: Simulate brain activity ==
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65		-The Virtual Brain is the main TVB software package. It is a neuroinformatics platform that provides an ecosystem of tools for simulating and analysing large-scale brain network dynamics based on biologically realistic connectivity. TVB can be operated via GUI and programmatic Python interface. On the EBRAINS Collaboratory Platform TVB Simulator usage is introduced through IPython Notebooks in the main TVB [[collab>>doc:Collabs.the-virtual-brain.WebHome||target="_blank"]]. Additionally, the TVB GUI can be directly accessed as [[a Web App>>https://thevirtualbrain.apps.hbp.eu/user/profile]]. Via the Web App users can configure simulations that are – depending on their complexity – either simulated directly on the web server or on a supercomputer, thereby making resource-consuming TVB functionality accessible to researchers that do not have access to supercomputers. Compiled standalone versions of the main software package can be downloaded from thevirtualbrain.org. In the following we take you through the main steps of brain network model simulation.
	65	+The Virtual Brain is the main TVB software package. It is a neuroinformatics platform that provides an ecosystem of tools for simulating and analysing large-scale brain network dynamics based on biologically realistic connectivity.
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67		-* Construct and downloaded the structural connectivity generated with the TVB pipeline. Alternatively, you can use demo SC that is shipped with the main TVB package. Next, go to the TVB Collaboratory and work through the example “[[Load TVB Connectivity>>https://collab.humanbrainproject.eu/#/collab/1609/nav/15645]]”
68		-* Having loaded the SC, next work through the basic process of setting up a simulation, see
69		-** Simulate with the reduced [[Wong-Wang model>>https://collab.humanbrainproject.eu/#/collab/1609/nav/15656]]
70		-** Simulate with the [[Jansen Rit Model>>https://collab.humanbrainproject.eu/#/collab/1609/nav/15654]]
71		-* Next, explore how you can output BOLD activity using the [[BOLD monitor>>https://collab.humanbrainproject.eu/#/collab/1609/nav/15660]]
72		-* Having simulated a longer time series (at least a few minutes of activity) you can compute a functional connectivity matrix following the examples [[here>>https://collab.humanbrainproject.eu/#/collab/1609/nav/15657]].
	67	+TVB can be operated via GUI and programmatic Python interface.
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	69	+* On the EBRAINS Collaboratory Platform TVB Simulator usage is introduced through IPython Notebooks in the main TVB [[collab>>doc:Collabs.the-virtual-brain.WebHome||target="_blank"]].
	70	+* Additionally, the TVB GUI can be directly accessed as [[a Web App>>https://tvb.apps.ebrains.eu/]]. Via the Web App users can configure simulations that are – depending on their complexity – either simulated directly on the web server or on a supercomputer, thereby making resource-consuming TVB functionality accessible to researchers that do not have access to supercomputers.
	71	+* Compiled standalone versions of the main software package can be downloaded from [[thevirtualbrain.org>>http://www.thevirtualbrain.org]].
	72	+
	73	+In the following we take you through the main steps of brain network model simulation:
	74	+
	75	+* Construct and downloaded the structural connectivity generated with the TVB pipeline. Alternatively, you can use demo SC that is shipped with the main TVB package.
	76	+* Having loaded the SC (in GUI or command line), next work through the basic process of setting up a simulation, see here [[https:~~/~~/docs.thevirtualbrain.org/demos/Demos.html>>https://docs.thevirtualbrain.org/demos/Demos.html]]
	77	+** Simulate with the reduced Wong-Wang model
	78	+** Simulate with the Jansen Rit Model
	79	+* Next, explore how you can output BOLD activity using the BOLD monitor
	80	+* Having simulated a longer time series (at least a few minutes of activity) you can compute a functional connectivity matrix
	81	+
74	74	Congratulations, you performed your first brain simulation. You may now want to play with parameters and look how it affects the simulated FC – a goal may be to maximize the fit between simulated and empirical FC. Often a good first step is to vary the global coupling scaling factor: start at a low value (little exchange of synaptic currents between brain regions) and then increase until fMRI time series of the different brain regions become increasingly correlated.
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76	76
77	77	== TVB+NEST: Multiscale simulation ==
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79		-In the previous step we simulated the brain at a coarse spatial resolution: the macroscopic scale of brain regions (e.g. “M1”, “V1”, etc.) and long-range white matter fiber bundles. However, interesting computations often happen on smaller scales, like the mesoscopic scale of small neural populations or the microscopic scale of individual neurons and neural networks. TVB+NEST is a Python toolbox that makes it easier to simulate multi-scale networks, i.e., networks, where one part simulates activity on a coarse scale and another part simulates activity on a finer scale. Essentially, TVB+NEST is a Python wrapper for The Virtual Brain neuroinformatics platform and the NEST spiking network simulator. TVB+NEST exists as a web app and a download version. The web app runs on HBP computers, while the download version is implemented as standalone Docker container that can be downloaded. To run TVB+NEST follow the instructions [[here>>https://collab.humanbrainproject.eu/#/collab/19/nav/2108?state=software,TVB%20and%20NEST%202]] or directly open the App [[here>>https://tvb-nest.apps.hbp.eu/hub/login]].
	87	+In the previous step we simulated the brain at a coarse spatial resolution: the macroscopic scale of brain regions (e.g. “M1”, “V1”, etc.) and long-range white matter fiber bundles. However, interesting computations often happen on smaller scales, like the mesoscopic scale of small neural populations or the microscopic scale of individual neurons and neural networks. TVB+NEST is a Python toolbox that makes it easier to simulate multi-scale networks, i.e., networks, where one part simulates activity on a coarse scale and another part simulates activity on a finer scale. Essentially, TVB+NEST is a Python wrapper for The Virtual Brain neuroinformatics platform and the NEST spiking network simulator. TVB+NEST exists as a web app and a download version. The web app runs on HBP computers, while the download version is implemented as standalone Docker container that can be downloaded. To run TVB+NEST follow the code [[here>>https://github.com/the-virtual-brain/tvb-multiscale]] or directly use the python module in [[EBRAINS Lab>>https://lab.ebrains.eu/]] (where it was installed as a Spack package).
80	80
81		-Alternatively, download the standalone Docker container thevirtualbrain/tvb-nest from Dockerhub. In the previous sections you may have simulated a large-scale brain model, but are now interested how large-scale activity affects finer-scale activity in a specific region. To familiarize yourself with TVB+NEST, you may read through the following tutorials.
	89	+Alternatively, download the standalone Docker container thevirtualbrain/tvb-nest from Dockerhub. In the previous sections you may have simulated a large-scale brain model, but are now interested how large-scale activity affects finer-scale activity in a specific region. To familiarize yourself with TVB+NEST, you may read more [[here>>https://wiki.ebrains.eu/bin/view/Collabs/the-virtual-brain-multiscale/]].
82	82
83		-* [[Test the TVB+NEST installation>>https://collab.humanbrainproject.eu/#/collab/58136/nav/396829]]
84		-* [[Run a custom co-simulation>>https://collab.humanbrainproject.eu/#/collab/58136/nav/41768]]
85		-* [[How to run a co-simulation from a notebook>>https://collab.humanbrainproject.eu/#/collab/58136/nav/531966]]
86		-* [[Run a notebook from storage>>https://collab.humanbrainproject.eu/#/collab/58136/nav/482634]]
87		-
88	88	== Fast_TVB: Fast and parallel simulation ==
89	89
90	90	Fast_TVB is thousands of times faster than Python TVB as it uses several optimization techniques and is implemented in the hardware-near language C. In addition, it is able to simulate in parallel, i.e., users can specify a number of threads that will simultaneously perform the processing and occupy multiple processors, as often done on supercomputers.