Note

Go to the end to download the full example code.

# PyNEST Microcircuit: Network ParametersΒΆ

Run this example as a Jupyter notebook:

See our guide for more information and troubleshooting.

A dictionary with base network and neuron parameters is enhanced with derived parameters.

```
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)
```