Data from: Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlations

van Albada SJ, Helias M, Diesmann M

Date Published: September 3, 2015

DOI: http://dx.doi.org/10.5061/dryad.2m85h

 

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Title Simulation data
Downloaded 10 times
Description binary/asymm/dist_Kin_scale: data on E-I network of Ginzburg neurons for Fig. 1C. binary/asymm/mcculloch_pitts: data on binary E-I network with population-specific connection probability for Fig. 5. binary/symm: data on binary E-I network with population-independent connection probability, scaled to preserve zero-lag covariances, for Fig. 6. spiking/E_I_LIF_network: data on scaling of network of leaky integrate-and-fire neurons with population-independent connection probability, preserving correlations, for Fig. 7. spiking/inhibitory_LIF_network: data on single-population network of inhibitory leaky integrate-and-fire neurons, for Fig. 2. spiking/microcircuit_param_scan: data on scaling of microcircuit model of Potjans & Diesmann (Cereb Cortex, 2014) with and without regard for covariances, for Figs. 3 and 4.
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Title Python scripts for analysis and plotting
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Description fig1.py, fig2.py, fig3.py, fig4.py, fig5.py, fig6.py, fig7.py: reproduce the figures of the paper using the data and additional figure files in this repository. binary_network_asymm.py, binary_network_symm.py, g_gamma_K_scaling.py, ginzburg_network_dist_Kin_scale.py, inhibitory_LIF_network.py: NEST simulation scripts for random recurrent networks. parameters_binary_asymm.py, parameters_binary_symm.py, parameters_ginzburg.py, parameters_inhibitory_LIF_network.py: corresponding parameter files. hambach_run_g_gamma_K_scaling.py, hambach_run_inhibitory_LIF_network.py, hambach_run_sim_dist_Kin_scale.py: scripts for running the corresponding simulations on a cluster. h5py_wrapper.py: for storing and loading h5 files. plotfuncs.py, plot_utils.py, ps_manip.py: general plotting scripts. ginzburg_theory.py, ginzburg_theory_distKin.py, hawkesnet.py, siegert.py: contain functions for computing covariances and related quantities. microcircuit_analysis.py: functions for loading and analyzing data from simulations of the Potjans and Diesmann (Cereb Cortex, 2014) microcircuit model. microcircuit: folder containing NEST simulation scripts for the Potjans and Diesmann microcircuit model.
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Title Accessory figure files
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Description fig_scaling_framework.json: configuration file for Fig. 1. relevant_scales.eps, why_scaling.eps, scaling_as_subsampling.eps: diagrams included in Fig. 1. redistributing_synapes.eps: diagram included in Fig. 6. spiking_network_scaling_diagram.eps: diagram included in Fig. 7.
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Title Simulation data - submission 2
Downloaded 9 times
Description binary/asymm/dist_Kin_scale: data on E-I network of Ginzburg neurons for Fig. 1C. binary/asymm/mcculloch_pitts: data on binary E-I network with population-specific connection probability for Fig. 3. binary/symm: data on binary E-I network with population-independent connection probability, scaled to preserve zero-lag covariances, for Fig. 6. spiking/E_I_LIF_network: data on scaling of network of leaky integrate-and-fire neurons with population-independent connection probability, preserving correlations, for Fig. 7. spiking/inhibitory_LIF_network: data on single-population network of inhibitory leaky integrate-and-fire neurons, for Fig. 2. spiking/LIF_network_rate_comparison: data on E-I network of leaky integrate-and-fire neurons with different firing rates, for Fig. 5. spiking/microcircuit: data on scaling of microcircuit model of Potjans & Diesmann (Cereb Cortex, 2014), for Fig. 4.
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Title Python scripts for analysis and plotting - submission 2
Downloaded 6 times
Description fig1.py, fig2.py, fig3.py, fig4.py, fig5.py, fig6.py, fig7.py: reproduce the figures of the paper using the data and additional figure files in this repository. binary_network_asymm.py, binary_network_symm.py, g_gamma_K_scaling.py, ginzburg_network_dist_Kin_scale.py, inhibitory_LIF_network.py, LIF_network_rate_comparison.py: NEST simulation scripts for random recurrent networks. parameters_binary_asymm.py, parameters_binary_symm.py, parameters_g_gamma_K_scaling.py, parameters_ginzburg.py, parameters_inhibitory_LIF_network.py, parameters_LIF_network_rate_comparison.py: corresponding parameter files. hambach_run_g_gamma_K_scaling.py, hambach_run_inhibitory_LIF_network.py, hambach_run_LIF_network_rate_comparison.py, hambach_run_sim_dist_Kin_scale.py: scripts for running the corresponding simulations on a cluster. h5py_wrapper.py: for storing and loading h5 files. plotfuncs.py, plot_utils.py, ps_manip.py: general plotting scripts. corr_hawkes_like.py, ginzburg_theory.py, ginzburg_theory_distKin.py, hawkesnet.py, siegert.py: contain functions for computing covariances and related quantities. microcircuit: folder containing NEST simulation scripts for the Potjans and Diesmann microcircuit model.
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Title Accessory figure files - submission 2
Downloaded 6 times
Description fig_scaling_framework.json: configuration file for Fig. 1. relevant_scales.eps, why_scaling.eps, scaling_as_subsampling.eps: diagrams included in Fig. 1. redistributing_synapes.eps: diagram included in Fig. 6. spiking_network_scaling_diagram.eps: diagram included in Fig. 7.
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When using this data, please cite the original publication:

van Albada SJ, Helias M, Diesmann M (2015) Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlations. PLOS Computational Biology 11(9): e1004490. http://dx.doi.org/10.1371/journal.pcbi.1004490

Additionally, please cite the Dryad data package:

van Albada SJ, Helias M, Diesmann M (2015) Data from: Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlations. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.2m85h
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