Example of multimeter recording to fileΒΆ

Run this example as a Jupyter notebook:

See our guide for more information and troubleshooting.

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

First, we import the necessary modules to simulate and plot this example. The simulation kernel is put back to its initial state using ResetKernel.

import matplotlib.pyplot as plt
import nest
import numpy


Global properties of the simulation kernel can be set via attributes of the nest module. The following properties are related to writing to file:

  • overwrite_files can be set True to permit overwriting of existing files.

  • 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.overwrite_files = True
nest.data_path = ""
nest.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 postsynaptic change of conductance modeled by an alpha function.

print("iaf_cond_alpha recordables: {0}".format(nest.GetDefaults("iaf_cond_alpha")["recordables"]))

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 (tau_syn_ex, in ms) and the reset potential of the membrane (V_reset, in mV) are specified.

  • For the multimeter, the time interval for recording (interval, in ms) and the measures to record (membrane potential V_m in mV and excitatory and inhibitory synaptic conductances g_ex and`g_in` in nS) are set.

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

  • record_to indicates where to put recorded data. All possible values are available by inspecting the keys of the dictionary obtained from the kernel attribute recording_backends.

  • label specifies an arbitrary label for the device. If writing to files, it used in the file name instead of the model 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(
    params={"interval": 0.1, "record_from": ["V_m", "g_ex", "g_in"], "record_to": "ascii", "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, we connect the spike generators 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 the values are positive for excitatory stimulation and negative for inhibitor 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 file the multimeter wrote to, accessed with the filenames property of the multimeter. After three header rows, the data is formatted in columns. The first column is the ID of the sender node. The second column is the time of the recording, in ms. Subsequent rows are values of properties specified in the record_from property of the multimeter.

data = numpy.loadtxt(m.filenames[0], skiprows=3)
sender, t, v_m, g_ex, g_in = data.T

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


plt.plot(t, v_m)
plt.axis([0, 100, -75, -53])
plt.ylabel("membrane potential (mV)")

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

Gallery generated by Sphinx-Gallery