    This is the readme for the Brian code associated with the paper:

    Y Sweeney, J Hellgren-Kotaleski, and M Hennig (2015).
    A diffusive homeostatic signal maintains neural heterogeneity and
    responsiveness in cortical networks. PLoS Comp. Biol. DOI:
    10.1371/journal.pcbi.1004389

    An up-to-date version of the code may be found at
    https://github.com/yannaodh/sweeney-2015

    To see the code used to implement the numerical diffusion method, see
    code/diffusion_minimal_example.py . Brief instructions on how to
    include this in an existing brian model are contained within.

    To see a simplified python script containing a spiking network model
    with diffusive homeostasis implemented (written in brian) see
    code/network_minimal_example.py .

    To generate the data for each figure run the run_simulations_figX.py
    script from command line using 'python run_simulations_figX.py', where
    X is the figure number. A lot of text output will be created showing
    the progress. You will need to be in the same directory as the
    'network_simulation.py' file, and other .py files.

    Python with numpy (http://www.numpy.org/) and the brian simulator
    (http://www.briansimulator.org) will need to be installed. These scripts were
    tested on a machine with OSX 10.10, Python v2.7.10, numpy v1.9.2, and
    brian v1.4.1

    IPython Notebook (http://ipython.org/notebook.html) will be required in order to interact with the IPython Notebooks containing the plots.

    As the simulations can take quite a long time to run (several hours on
    a multicore machine) and the resulting datafiles are quite large (~1
    GB total), sample data generated from the code are provided for each
    figure and may be downloaded from figshare.

    Figure 1: http://dx.doi.org/10.6084/m9.figshare.1454450

    Figure 4: http://dx.doi.org/10.6084/m9.figshare.1454455

    Figure 5: http://dx.doi.org/10.6084/m9.figshare.1454469

    For any queries, comments or requests please do not hesitate to
    contact me at yann.sweeney@ed.ac.uk.