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README.md

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 # fast_tvb
 Fast and parallel C implementation of TVB
+
+## Pull and run fast_tvb as a Docker container here
+## https://hub.docker.com/r/thevirtualbrain/fast_tvb
+
+
+The code implements a brain network model composed of connected ReducedWongWang neural mass models (Wong & Wang, 2006) with feedback inhibition control (FIC). For more information on the model and FIC please see Deco et al. (2014), Schirner et al. (2018) and Shen et al. (2019).
+
+For more information on The Virtual Brain (TVB) please see 
+www.thevirtualbrain.org
+
+For questions and other enquiries please contact 
+
+Michael Schirner (michael.schirner@charite.de) or 
+Petra Ritter (petra.ritter@charite.de).
+
+# Generating the Docker container
+
+If you want to generate the Docker container from scratch, two steps are necessary: first, compiling the C file, and, second, generating a Docker container with the new binary.
+
+## Step 1: Compile tvbii_multicore.c
+
+We will use a Docker container to compile the C code, see folder `step1_compile_C_code`.
+1. Make sure you have the Docker client installed and running (https://www.docker.com/products/docker-desktop)
+2. Download/pull this repository onto your local machine.
+3. Create a folder `/path/to/tvb_binary` where the compiled binary will be stored.
+4. Open a command line shell and enter the directory `step1_compile_C_code`.
+5. Run the following:
+```
+docker build -t tvb_c .
+docker run --rm --mount type=bind,source=/path/to/tvb_binary,target=/output tvb_c /compile_and_copy.sh
+cp /path/to/tvb_binary/tvb ../step2_create_Docker_container/
+cat /path/to/tvb_binary/build_output.txt
+```
+The first command builds the Docker container with the build environment for compiling the C file. The second command compiles the C file and copies the output into the folder `/path/to/tvb_binary` in your local filesystem. The third command copies the created binary `tvb` into the folder `step2_create_Docker_container`. The fourth command lets you inspect the output of the compiler -- closely inspect whether it contains error messages or warning that need to be fixed.
+6. Now that our fast_tvb binary is created, we will create a Docker container that contains the binary for convenient execution on different platforms. Enter the folder `step2_create_Docker_container`.
+7. To build the Docker file run
+```
+docker build -t <your repo>/fast_tvb .
+```
+replacing `<your repo>` with the name of your Dockerhub repository.
+8. To push it into your repository run
+```
+docker push <your repo>/fast_tvb
+```
+9. To run `fast_tvb` run
+```
+docker run --rm --mount type=bind,source=/path/to/step2_create_Docker_container/output,target=/output --mount type=bind,source=/path/to/step2_create_Docker_container/input,target=/input  <your repo>/fast_tvb /start_simulation.sh param_set.txt gavg 4
+```
+The folder `step2_create_Docker_container` contains a demo brain network model in the folder `input`.
+
+
+For more information on how to use `fast_tvb`, please follow the instructions at https://hub.docker.com/r/thevirtualbrain/fast_tvb.
+
+
+# References
+
+  Deco, G., Ponce-Alvarez, A., Hagmann, P., Romani, G. L., Mantini, D., & Corbetta, M. (2014). How local excitation–inhibition ratio impacts the whole brain dynamics. Journal of Neuroscience, 34(23), 7886-7898.
+
+  Schirner, M., McIntosh, A. R., Jirsa, V., Deco, G., & Ritter, P. (2018). Inferring multi-scale neural mechanisms with brain network modelling. Elife, 7, e28927.
+
+  Shen, K., Bezgin, G., Schirner, M., Ritter, P., Everling, S., McIntosh, A. R. (2019) A macaque connectome for large-scale network simulations in TheVirtualBrain. (under review)
+
+  Wong, K. F., & Wang, X. J. (2006). A recurrent network mechanism of time integration in perceptual decisions. Journal of Neuroscience, 26(4), 1314-1328.