Constructing and simulating compartmental models with different receptor types

This example demonstrates how to initialize a three-compartment model with different receptor types. Each compartment receives a different receptor.

The output shows the voltage in each of the three compartments.

Authors

WAM Wybo

import nest
import matplotlib.pyplot as plt

nest.ResetKernel()

somatic and dendritic parameters

soma_params = {
    'C_m': 10.0,    # [pF] Capacitance
    'g_C': 0.0,     # soma has no parent
    'g_L': 1.,      # [nS] Leak conductance
    'e_L': -70.0    # [mV] leak reversal
}
dend_params = {
    'C_m': 0.1,     # [pF] Capacitance
    'g_C': 0.1,     # [nS] Coupling conductance to parent (soma here)
    'g_L': 0.1,     # [nS] Leak conductance
    'e_L': -70.0    # [mV] leak reversal
}

create a model with three compartments

cm = nest.Create('cm_default')
cm.compartments = [
    {"parent_idx": -1, "params": soma_params},
    {"parent_idx":  0, "params": dend_params},
    {"parent_idx":  0, "params": dend_params}
]

spike threshold

nest.SetStatus(cm, {'V_th': -50.})

• GABA receptor in compartment 0 (soma)

• AMPA receptor in compartment 1 note that it is also possible to specify the receptor parameters, if we want to overwrite the default values

• AMPA+NMDA receptor in compartment 2

receptors = [
    {"comp_idx": 0, "receptor_type": "GABA"},
    {"comp_idx": 1, "receptor_type": "AMPA", "params": {"tau_r_AMPA": .2, "tau_d_AMPA": 3., "e_AMPA": 0.}},
    {"comp_idx": 2, "receptor_type": "AMPA_NMDA"}
]
cm.receptors = receptors

receptors get assigned an index which corresponds to the order in which they are added. For clearer bookkeeping, we explicitly define these indices here.

syn_idx_GABA, syn_idx_AMPA, syn_idx_NMDA = 0, 1, 2

create three spike generators

sg1 = nest.Create('spike_generator', 1, {'spike_times': [101., 105., 106., 110., 150.]})
sg2 = nest.Create('spike_generator', 1, {'spike_times': [115., 155., 160., 162., 170., 254., 260., 272., 278.]})
sg3 = nest.Create('spike_generator', 1, {'spike_times': [250., 255., 260., 262., 270.]})

connect the spike generators to the receptors

nest.Connect(sg1, cm, syn_spec={
    'synapse_model': 'static_synapse', 'weight': .1, 'delay': 0.5, 'receptor_type': syn_idx_AMPA})
nest.Connect(sg2, cm, syn_spec={
    'synapse_model': 'static_synapse', 'weight': .2, 'delay': 0.5, 'receptor_type': syn_idx_NMDA})
nest.Connect(sg3, cm, syn_spec={
    'synapse_model': 'static_synapse', 'weight': .3, 'delay': 0.5, 'receptor_type': syn_idx_GABA})

create and connect a current generator to compartment 1

dcg = nest.Create('dc_generator', {'amplitude': 1.})
nest.Connect(dcg, cm, syn_spec={
    'synapse_model': 'static_synapse', 'weight': 1., 'delay': 0.1, 'receptor_type': 1})

create and connect a multimeter to measure the three compartmental voltages

mm = nest.Create('multimeter', 1, {'record_from': ['v_comp0', 'v_comp1', 'v_comp2'], 'interval': .1})
nest.Connect(mm, cm)

nest.Simulate(400.)
res = nest.GetStatus(mm, 'events')[0]

plt.plot(res['times'], res['v_comp0'], c='b', label='v_comp0')
plt.plot(res['times'], res['v_comp1'], c='r', label='v_comp1')
plt.plot(res['times'], res['v_comp2'], c='g', label='v_comp2')
plt.legend()

plt.show()