Multimeter to file example

This file demonstrates recording from an iaf_cond_alpha neuron using a multimeter and writing data to file.

First, the necessary modules for simulation and plotting are imported.

The simulation kernel is put back to its initial state using ResetKernel.

import nest
import numpy
import pylab


With SetKernelStatus, global properties of the simulation kernel can be specified. The following properties are related to writing to file:

  • overwrite_files is set to True to permit overwriting of an existing file.
  • data_path is the path to which all data is written. It is given relative to the current working directory.
  • 'data_prefix' allows to specify a common prefix for all data files.

    nest.SetKernelStatus({"overwrite_files": True, "data_path": "", "data_prefix": ""})

For illustration, the recordables of the iaf_cond_alpha neuron model are displayed. This model is an implementation of a spiking neuron using integrate-and-fire dynamics with conductance-based synapses. Incoming spike events induce a post-synaptic change of conductance modelled by an alpha function.

print("iaf_cond_alpha recordables: {0}".format(

A neuron, a multimeter as recording device and two spike generators for excitatory and inhibitory stimulation are instantiated. The command Create expects a model type and, optionally, the desired number of nodes and a dictionary of parameters to overwrite the default values of the model.

  • For the neuron, the rise time of the excitatory synaptic alpha function in ms tau_syn_ex and the reset potential of the membrane in mV V_reset are specified.
  • For the multimeter, the time interval for recording in ms interval and a selection of measures to record (the membrane voltage in mV V_m and the excitatory g_ex and inhibitoy g_in synaptic conductances in nS) are set.

In addition, more parameters can be modified for writing to file:

  • withgid is set to True to record the global id of the observed node(s). (default: False).
  • to_file indicates whether to write the recordings to file and is set to True.
  • label specifies an arbitrary label for the device. It is used instead of the name of the model in the output file name.

  • For the spike generators, the spike times in ms spike_times are given explicitly.

    n = nest.Create("iaf_cond_alpha", params={"tau_syn_ex": 1.0, "V_reset": -70.0})

    m = nest.Create("multimeter", params={"interval": 0.1, "record_from": ["V_m", "g_ex", "g_in"], "withgid": True, "to_file": True, "label": "my_multimeter"})

    s_ex = nest.Create("spike_generator", params={"spike_times": numpy.array([10.0, 20.0, 50.0])}) s_in = nest.Create("spike_generator", params={"spike_times": numpy.array([15.0, 25.0, 55.0])})

Next, the spike generators are connected to the neuron with Connect. Synapse specifications can be provided in a dictionary. In this example of a conductance-based neuron, the synaptic weight weight is given in nS. Note that it is positive for excitatory and negative for inhibitory connections.

nest.Connect(s_ex, n, syn_spec={"weight": 40.0})
nest.Connect(s_in, n, syn_spec={"weight": -20.0})
nest.Connect(m, n)

A network simulation with a duration of 100 ms is started with Simulate.


After the simulation, the recordings are obtained from the multimeter via the key events of the status dictionary accessed by GetStatus. times indicates the recording times stored for each data point. They are recorded if the parameter withtime of the multimeter is set to True which is the default case.

events = nest.GetStatus(m)[0]["events"]
t = events["times"]

Finally, the time courses of the membrane voltage and the synaptic conductance are displayed.


pylab.plot(t, events["V_m"])
pylab.axis([0, 100, -75, -53])
pylab.ylabel("membrane potential (mV)")

pylab.plot(t, events["g_ex"], t, events["g_in"])
pylab.axis([0, 100, 0, 45])
pylab.xlabel("time (ms)")
pylab.ylabel("synaptic conductance (nS)")
pylab.legend(("g_exc", "g_inh"))