.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/Potjans_2014/network_params.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_Potjans_2014_network_params.py: PyNEST Microcircuit: Network Parameters --------------------------------------------- .. only:: html ---- Run this example as a Jupyter notebook: .. card:: :width: 25% :margin: 2 :text-align: center :link: https://lab.ebrains.eu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fnest%2Fnest-simulator-examples&urlpath=lab%2Ftree%2Fnest-simulator-examples%2Fnotebooks%2Fnotebooks%2FPotjans_2014%2Fnetwork_params.ipynb&branch=main :link-alt: JupyterHub service .. image:: https://nest-simulator.org/TryItOnEBRAINS.png .. grid:: 1 1 1 1 :padding: 0 0 2 0 .. grid-item:: :class: sd-text-muted :margin: 0 0 3 0 :padding: 0 0 3 0 :columns: 4 See :ref:`our guide ` for more information and troubleshooting. ---- A dictionary with base network and neuron parameters is enhanced with derived parameters. .. GENERATED FROM PYTHON SOURCE LINES 29-159 .. code-block:: Python import numpy as np def get_exc_inh_matrix(val_exc, val_inh, num_pops): """Creates a matrix for excitatory and inhibitory values. Parameters ---------- val_exc Excitatory value. val_inh Inhibitory value. num_pops Number of populations. Returns ------- matrix A matrix of of size (num_pops x num_pops). """ matrix = np.zeros((num_pops, num_pops)) matrix[:, 0:num_pops:2] = val_exc matrix[:, 1:num_pops:2] = val_inh return matrix net_dict = { # factor to scale the number of neurons "N_scaling": 0.1, # factor to scale the indegrees "K_scaling": 0.1, # neuron model "neuron_model": "iaf_psc_exp", # names of the simulated neuronal populations "populations": ["L23E", "L23I", "L4E", "L4I", "L5E", "L5I", "L6E", "L6I"], # number of neurons in the different populations (same order as # 'populations') "full_num_neurons": np.array([20683, 5834, 21915, 5479, 4850, 1065, 14395, 2948]), # mean rates of the different populations in the non-scaled version of the # microcircuit (in spikes/s; same order as in 'populations'); # necessary for the scaling of the network. # The values were obtained by running this PyNEST microcircuit without MPI, # 'local_num_threads' 4 and both 'N_scaling' and 'K_scaling' set to 1. "full_mean_rates": np.array([0.903, 2.965, 4.414, 5.876, 7.569, 8.633, 1.105, 7.829]), # connection probabilities (the first index corresponds to the targets # and the second to the sources) "conn_probs": np.array( [ [0.1009, 0.1689, 0.0437, 0.0818, 0.0323, 0.0, 0.0076, 0.0], [0.1346, 0.1371, 0.0316, 0.0515, 0.0755, 0.0, 0.0042, 0.0], [0.0077, 0.0059, 0.0497, 0.135, 0.0067, 0.0003, 0.0453, 0.0], [0.0691, 0.0029, 0.0794, 0.1597, 0.0033, 0.0, 0.1057, 0.0], [0.1004, 0.0622, 0.0505, 0.0057, 0.0831, 0.3726, 0.0204, 0.0], [0.0548, 0.0269, 0.0257, 0.0022, 0.06, 0.3158, 0.0086, 0.0], [0.0156, 0.0066, 0.0211, 0.0166, 0.0572, 0.0197, 0.0396, 0.2252], [0.0364, 0.001, 0.0034, 0.0005, 0.0277, 0.008, 0.0658, 0.1443], ] ), # mean amplitude of excitatory postsynaptic potential (in mV) "PSP_exc_mean": 0.15, # relative standard deviation of the weight "weight_rel_std": 0.1, # relative inhibitory weight "g": -4, # mean delay of excitatory connections (in ms) "delay_exc_mean": 1.5, # mean delay of inhibitory connections (in ms) "delay_inh_mean": 0.75, # relative standard deviation of the delay of excitatory and # inhibitory connections "delay_rel_std": 0.5, # turn Poisson input on or off (True or False) # if False: DC input is applied for compensation "poisson_input": True, # indegree of external connections to the different populations (same order # as in 'populations') "K_ext": np.array([1600, 1500, 2100, 1900, 2000, 1900, 2900, 2100]), # rate of the Poisson generator (in spikes/s) "bg_rate": 8.0, # delay from the Poisson generator to the network (in ms) "delay_poisson": 1.5, # initial conditions for the membrane potential, options are: # 'original': uniform mean and standard deviation for all populations as # used in earlier implementations of the model # 'optimized': population-specific mean and standard deviation, allowing a # reduction of the initial activity burst in the network # (default) "V0_type": "optimized", # parameters of the neuron model "neuron_params": { # membrane potential average for the neurons (in mV) "V0_mean": {"original": -58.0, "optimized": [-68.28, -63.16, -63.33, -63.45, -63.11, -61.66, -66.72, -61.43]}, # standard deviation of the average membrane potential (in mV) "V0_std": {"original": 10.0, "optimized": [5.36, 4.57, 4.74, 4.94, 4.94, 4.55, 5.46, 4.48]}, # reset membrane potential of the neurons (in mV) "E_L": -65.0, # threshold potential of the neurons (in mV) "V_th": -50.0, # membrane potential after a spike (in mV) "V_reset": -65.0, # membrane capacitance (in pF) "C_m": 250.0, # membrane time constant (in ms) "tau_m": 10.0, # time constant of postsynaptic currents (in ms) "tau_syn": 0.5, # refractory period of the neurons after a spike (in ms) "t_ref": 2.0, }, } # derive matrix of mean PSPs, # the mean PSP of the connection from L4E to L23E is doubled PSP_matrix_mean = get_exc_inh_matrix( net_dict["PSP_exc_mean"], net_dict["PSP_exc_mean"] * net_dict["g"], len(net_dict["populations"]) ) PSP_matrix_mean[0, 2] = 2.0 * net_dict["PSP_exc_mean"] updated_dict = { # matrix of mean PSPs "PSP_matrix_mean": PSP_matrix_mean, # matrix of mean delays "delay_matrix_mean": get_exc_inh_matrix( net_dict["delay_exc_mean"], net_dict["delay_inh_mean"], len(net_dict["populations"]) ), } net_dict.update(updated_dict) .. _sphx_glr_download_auto_examples_Potjans_2014_network_params.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: network_params.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: network_params.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_