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Current-based generalized leaky integrate and fire (GLIF) neuron example

Simple example of how to use the glif_psc neuron model for five different levels of GLIF neurons.

Four stimulation paradigms are illustrated for the GLIF model with externally applied current and spikes impinging

Voltage traces, current traces, threshold traces, and spikes are shown.

KEYWORDS: glif_psc

First, we import all necessary modules to simulate, analyze and plot this example.

import nest
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

We initialize NEST and set the simulation resolution.

nest.ResetKernel()
resolution = 0.05
nest.resolution = resolution

We also pre-define the synapse time constant array, [2.0, 1.0] ms for the two desired synaptic ports of the GLIF neurons. Note that the default synapse time constant is [2.0] ms, which is for neuron with one port.

syn_tau = [2.0, 1.0]

We create the five levels of GLIF model to be tested, i.e., lif, lif_r, lif_asc, lif_r_asc, lif_r_asc_a. For each level of GLIF model, we create a glif_psc node. The node is created by setting relative model mechanism parameters and the time constant of the 2 synaptic ports as mentioned above. Other neuron parameters are set as default. The five glif_psc node handles were combined as a list.

n_lif = nest.Create("glif_psc",
                    params={"spike_dependent_threshold": False,
                            "after_spike_currents": False,
                            "adapting_threshold": False,
                            "tau_syn": syn_tau})
n_lif_r = nest.Create("glif_psc",
                      params={"spike_dependent_threshold": True,
                              "after_spike_currents": False,
                              "adapting_threshold": False,
                              "tau_syn": syn_tau})
n_lif_asc = nest.Create("glif_psc",
                        params={"spike_dependent_threshold": False,
                                "after_spike_currents": True,
                                "adapting_threshold": False,
                                "tau_syn": syn_tau})
n_lif_r_asc = nest.Create("glif_psc",
                          params={"spike_dependent_threshold": True,
                                  "after_spike_currents": True,
                                  "adapting_threshold": False,
                                  "tau_syn": syn_tau})
n_lif_r_asc_a = nest.Create("glif_psc",
                            params={"spike_dependent_threshold": True,
                                    "after_spike_currents": True,
                                    "adapting_threshold": True,
                                    "tau_syn": syn_tau})

neurons = n_lif + n_lif_r + n_lif_asc + n_lif_r_asc + n_lif_r_asc_a

For the stimulation input to the glif_psc neurons, we create one excitation spike generator and one inhibition spike generator, each of which generates three spikes; we also create one step current generator and a Poisson generator, a parrot neuron (to be paired with the Poisson generator). The three different injections are spread to three different time periods, i.e., 0 ms ~ 200 ms, 200 ms ~ 500 ms, 600 ms ~ 900 ms. Each of the excitation and inhibition spike generators generates three spikes at different time points. Configuration of the current generator includes the definition of the start and stop times and the amplitude of the injected current. Configuration of the Poisson generator includes the definition of the start and stop times and the rate of the injected spike train.

espikes = nest.Create("spike_generator",
                      params={"spike_times": [10., 100., 150.],
                              "spike_weights": [20.] * 3})
ispikes = nest.Create("spike_generator",
                      params={"spike_times": [15., 99., 150.],
                              "spike_weights": [-20.] * 3})
cg = nest.Create("step_current_generator",
                 params={"amplitude_values": [400., ],
                         "amplitude_times": [200., ],
                         "start": 200., "stop": 500.})
pg = nest.Create("poisson_generator",
                 params={"rate": 150000., "start": 600., "stop": 900.})
pn = nest.Create("parrot_neuron")

The generators are then connected to the neurons. Specification of the receptor_type uniquely defines the target receptor. We connect current generator, the spike generators, Poisson generator (via parrot neuron) to receptor 0, 1, and 2 of the GLIF neurons, respectively. Note that Poisson generator is connected to parrot neuron to transit the spikes to the glif_psc neuron.

nest.Connect(cg, neurons, syn_spec={"delay": resolution})
nest.Connect(espikes, neurons,
             syn_spec={"delay": resolution, "receptor_type": 1})
nest.Connect(ispikes, neurons,
             syn_spec={"delay": resolution, "receptor_type": 1})
nest.Connect(pg, pn, syn_spec={"delay": resolution})
nest.Connect(pn, neurons, syn_spec={"delay": resolution, "receptor_type": 2})

A multimeter is created and connected to the neurons. The parameters specified for the multimeter include the list of quantities that should be recorded and the time interval at which quantities are measured.

mm = nest.Create("multimeter",
                 params={"interval": resolution,
                         "record_from": ["V_m", "I", "I_syn", "threshold",
                                         "threshold_spike",
                                         "threshold_voltage",
                                         "ASCurrents_sum"]})
nest.Connect(mm, neurons)

A spike_recorder is created and connected to the neurons record the spikes generated by the glif_psc neurons.

sr = nest.Create("spike_recorder")
nest.Connect(neurons, sr)

Run the simulation for 1000 ms and retrieve recorded data from the multimeter and spike recorder.

nest.Simulate(1000.)

data = mm.events
senders = data["senders"]

spike_data = sr.events
spike_senders = spike_data["senders"]
spikes = spike_data["times"]

We plot the time traces of the membrane potential (in blue) and the overall threshold (in green), and the spikes (as red dots) in one panel; the spike component of threshold (in yellow) and the voltage component of threshold (in black) in another panel; the injected currents (in strong blue), the sum of after spike currents (in cyan), and the synaptic currents (in magenta) in responding to the spike inputs to the neurons in the third panel. We plot all these three panels for each level of GLIF model in a separated figure.

glif_models = ["lif", "lif_r", "lif_asc", "lif_r_asc", "lif_r_asc_a"]
for i in range(len(glif_models)):

    glif_model = glif_models[i]
    node_id = neurons[i].global_id
    plt.figure(glif_model)
    gs = gridspec.GridSpec(3, 1, height_ratios=[2, 1, 1])
    t = data["times"][senders == 1]

    ax1 = plt.subplot(gs[0])
    plt.plot(t, data["V_m"][senders == node_id], "b")
    plt.plot(t, data["threshold"][senders == node_id], "g--")
    plt.plot(spikes[spike_senders == node_id],
             [max(data["threshold"][senders == node_id]) * 0.95] *
             len(spikes[spike_senders == node_id]), "r.")
    plt.legend(["V_m", "threshold", "spike"])
    plt.ylabel("V (mV)")
    plt.title("Simulation of glif_psc neuron of " + glif_model)

    ax2 = plt.subplot(gs[1])
    plt.plot(t, data["threshold_spike"][senders == node_id], "y")
    plt.plot(t, data["threshold_voltage"][senders == node_id], "k--")
    plt.legend(["threshold_spike", "threshold_voltage"])
    plt.ylabel("V (mV)")

    ax3 = plt.subplot(gs[2])
    plt.plot(t, data["I"][senders == node_id], "--")
    plt.plot(t, data["ASCurrents_sum"][senders == node_id], "c-.")
    plt.plot(t, data["I_syn"][senders == node_id], "m")
    plt.legend(["I_e", "ASCurrents_sum", "I_syn"])
    plt.ylabel("I (pA)")
    plt.xlabel("t (ms)")

plt.show()