multiscaleisn
Inhibition Stabilized Networks at multiple scales based on Sadeh et al. 2017
Science Score: 41.0%
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Repository
Inhibition Stabilized Networks at multiple scales based on Sadeh et al. 2017
Basic Info
- Host: GitHub
- Owner: OpenSourceBrain
- License: other
- Language: Python
- Default Branch: master
- Homepage: http://www.opensourcebrain.org/projects/multiscaleisn
- Size: 8.09 MB
Statistics
- Stars: 1
- Watchers: 6
- Forks: 0
- Open Issues: 1
- Releases: 1
Metadata Files
README.md
Multiscale ISN
Inhibition Stabilized Networks at multiple scales based on Sadeh et al. 2017.
To generate network/run model
The main script to generate the model is ISN.py and changing the parameters to the main
generate() function here will create different configurations of the network:
def generate(scale_populations = 1,
percentage_exc_detailed=0,
exc2_cell = 'SmithEtAl2013/L23_NoHotSpot',
percentage_inh_detailed=0,
scalex=1,
scaley=1,
scalez=1,
exc_exc_conn_prob = 0.25,
exc_inh_conn_prob = 0.25,
inh_exc_conn_prob = 0.75,
inh_inh_conn_prob = 0.75,
ee2_conn_prob = 0,
ie2_conn_prob = 0,
Bee = .1,
Bei = .1,
Bie = -.2,
Bii = -.2,
Bee2 = 1,
Bie2 = -2,
Be_bkg = .1,
Be_stim = .1,
r_bkg = 0,
r_bkg_ExtExc=0,
r_bkg_ExtInh=0,
r_bkg_ExtExc2=0,
r_stim = 0,
fraction_inh_pert=0.75,
fraction_inh_offset=0,
inh_offset_amp=0, # hyperpolarising/depolarising current to inh fraction_inh_offset of cells
Ttrans = 500, # transitent time to discard the data (ms)
Tblank= 1500, # simulation time before perturbation (ms)
Tstim = 1500, # simulation time of perturbation (ms)
Tpost = 500, # simulation time after perturbation (ms)
connections=True,
connections2=False,
exc_target_dendrites=False,
inh_target_dendrites=False,
duration = 1000,
dt = 0.025,
global_delay = .1,
max_in_pop_to_plot_and_save = 10,
format='xml',
suffix='',
run_in_simulator = None,
num_processors = 1,
target_dir='./temp/',
v_clamp=False,
simulation_seed=11111):
Generally the defaults work well to generate a spiking network showing ISN properties.
To generate the 2 main configurations of the network (point neurons only, point neurons + 10 detailed neurons) and save as NeuroML, run:
./regenerate_neuroml.sh
To run the 40 network simulations in NetPyNE for the point neuron network, run:
./runall.sh
To run the 40 network simulations in NetPyNE for the point neuron network with 10 detailed cells, run:
./runall_detailed.sh
Reusing this model
The code in this repository is provided under the terms of the software license included with it. If you use this model in your research, we respectfully ask you to cite the references outlined in the CITATION file.
Owner
- Name: OpenSourceBrain
- Login: OpenSourceBrain
- Kind: organization
- Website: www.opensourcebrain.org
- Repositories: 141
- Profile: https://github.com/OpenSourceBrain
Citation (CITATION.md)
## Citing this model The software in this repository is provided under the terms of the [software license](LICENSE) included with it. If you use this model in your research, we respectfully ask you to cite: **The original publication upon which this model is based** - Sadeh S, Silver RA, Mrsic-Flogel TD, Muir DR **Assessing the Role of Inhibition in Stabilizing Neocortical Networks Requires Large-Scale Perturbation of the Inhibitory Population.**. [J Neurosci. 2017](https://www.ncbi.nlm.nih.gov/pubmed/29074575), 37(49):12050-12067. **The latest release of this Open Source Brain repository** - This link should provide a DOI/citation for the latest version released: https://www.zenodo.org/badge/latestdoi/136594034. If you would like us to make a new release, please [open an issue](../../issues).