Computational protocol: A Diffusive Homeostatic Signal Maintains Neural Heterogeneity and Responsiveness in Cortical Networks

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Protocol publication

[…] Unless explicitly defined, the parameters used throughout the paper are given in . For synthesis, diffusion, and decay of NO we have attempted to match data when available [, ], although the dearth of experimental measurements does not permit for great precision [, ]. Additionally, parameters were chosen such that the timescale of homeostasis is separated from that of firing rate fluctuations. This is a reasonable assumption, given that activity-dependent NO modulation likely acts within 10 minutes or slower [], although NO diffusion occurs on the order of 10 seconds. τ HIP was chosen to be long enough so as to avoid oscillations but short enough so as to allow feasible large scale simulations. This is a common assumption in computational studies []. Larger simulations, up to N = 25000, were run with no discernible difference in results.All numerical simulations were implemented using the Brian simulator, v1.4.1 [], and the mean-field analysis was implemented using IPython Notebook []. The 2D diffusion equation was solved numerically using an explicit finite difference equation method, using the numpy python package []. Data analysis was performed with the numpy Python package and plotting with the matplotlib package and seaborn library [, ]. Simulation code and IPython Notebooks which perform the data analysis and plotting are available at https://github.com/yannaodh/sweeney-2015. […]

Pipeline specifications

Software tools Numpy, matplotlib, Seaborn
Application Miscellaneous
Chemicals Nitric Oxide