Intrinsic currents spiking

This example illustrates a neuron receiving spiking input through several different receptors (AMPA, NMDA, GABA_A, GABA_B), provoking spike output. The model, ht_neuron, also has intrinsic currents (I_NaP, I_KNa, I_T, and I_h). It is a slightly simplified implementation of neuron model proposed in Hill and Tononi (2005) Modeling Sleep and Wakefulness in the Thalamocortical System J Neurophysiol 93:1671

The neuron is bombarded with spike trains from four Poisson generators, which are connected to the AMPA, NMDA, GABA_A, and GABA_B receptors, respectively.

See also:

We imported all necessary modules for simulation, analysis and plotting.

import nest
import numpy as np
import matplotlib.pyplot as plt

Additionally, we set the verbosity using set_verbosity to suppress info messages. We also reset the kernel to be sure to start with a clean NEST.


We define the simulation parameters:

  • The rate of the input spike trains
  • The weights of the different receptors (names must match receptor types)
  • The time to simulate

Note that all parameter values should be doubles, since NEST expects doubles.

rate_in = 100.
w_recep = {'AMPA': 30., 'NMDA': 30., 'GABA_A': 5., 'GABA_B': 10.}
t_sim = 250.

num_recep = len(w_recep)

We create

  • one neuron instance
  • one Poisson generator instance for each synapse type
  • one multimeter to record from the neuron:
  • membrane potential
  • threshold potential
  • synaptic conductances
  • intrinsic currents

See for more details on multimeter configuration.

nrn = nest.Create('ht_neuron')
p_gens = nest.Create('poisson_generator', 4,
                     params={'rate': rate_in})
mm = nest.Create('multimeter',
                 params={'interval': 0.1,
                         'record_from': ['V_m', 'theta',
                                         'g_AMPA', 'g_NMDA',
                                         'g_GABA_A', 'g_GABA_B',
                                         'I_NaP', 'I_KNa', 'I_T', 'I_h']})

We now connect each Poisson generator with the neuron through a different receptor type.

First, we need to obtain the numerical codes for the receptor types from the model. The receptor_types entry of the default dictionary for the ht_neuron model is a dictionary mapping receptor names to codes.

In the loop, we use Python's tuple unpacking mechanism to unpack dictionary entries from our w_recep dictionary.

Note that we need to pack the pg variable into a list before passing it to Connect, because iterating over the p_gens list makes pg a "naked" GID.

receptors = nest.GetDefaults('ht_neuron')['receptor_types']
for pg, (rec_name, rec_wgt) in zip(p_gens, w_recep.items()):
    nest.Connect([pg], nrn, syn_spec={'receptor_type': receptors[rec_name],
                                      'weight': rec_wgt})

We then connnect the multimeter. Note that the multimeter is connected to the neuron, not the other way around.

nest.Connect(mm, nrn)

We are now ready to simulate.


We now fetch the data recorded by the multimeter. The data are returned as a dictionary with entry 'times' containing timestamps for all recorded data, plus one entry per recorded quantity.

All data is contained in the 'events' entry of the status dictionary returned by the multimeter. Because all NEST function return arrays, we need to pick out element 0 from the result of GetStatus.

data = nest.GetStatus(mm)[0]['events']
t = data['times']

The following function turns a name such as I_NaP into proper TeX code INaP for a pretty label.

def texify_name(name):
    return r'${}_{{\mathrm{{{}}}}}$'.format(*name.split('_'))

The next step is to plot the results. We create a new figure, and add one subplot each for membrane and threshold potential, synaptic conductances, and intrinsic currents.

fig = plt.figure()

Vax = fig.add_subplot(311)
Vax.plot(t, data['V_m'], 'b', lw=2, label=r'$V_m$')
Vax.plot(t, data['theta'], 'g', lw=2, label=r'$\Theta$')
Vax.set_ylabel('Potential [mV]')

except TypeError:
    Vax.legend()  # work-around for older Matplotlib versions
Vax.set_title('ht_neuron driven by Poisson processes')

Gax = fig.add_subplot(312)
for gname in ('g_AMPA', 'g_NMDA', 'g_GABA_A', 'g_GABA_B'):
    Gax.plot(t, data[gname], lw=2, label=texify_name(gname))

except TypeError:
    Gax.legend()  # work-around for older Matplotlib versions
Gax.set_ylabel('Conductance [nS]')

Iax = fig.add_subplot(313)
for iname, color in (('I_h', 'maroon'), ('I_T', 'orange'),
                     ('I_NaP', 'crimson'), ('I_KNa', 'aqua')):
    Iax.plot(t, data[iname], color=color, lw=2, label=texify_name(iname))

except TypeError:
    Iax.legend()  # work-around for older Matplotlib versions
Iax.set_ylabel('Current [pA]')
Iax.set_xlabel('Time [ms]')