NeuroML exporter

This is a short overview of the nmlexport package, providing functionality to export Brian 2 models to NeuroML2.

NeuroML is a XML-based description that provides a common data format for defining and exchanging descriptions of neuronal cell and network models (NML project website).

Working example

As a demonstration, we use a simple unconnected Integrate & Fire neuron model with refractoriness and given initial values.

from brian2 import *
import brian2tools.nmlexport

set_device('neuroml2', filename="nml2model.xml")

n = 100
duration = 1*second
tau = 10*ms

eqs = '''
dv/dt = (v0 - v) / tau : volt (unless refractory)
v0 : volt
'''
group = NeuronGroup(n, eqs, threshold='v > 10*mV', reset='v = 0*mV',
                    refractory=5*ms, method='linear')
group.v = 0*mV
group.v0 = '20*mV * i / (N-1)'

rec_idx = [2, 63]
statemonitor = StateMonitor(group, 'v', record=rec_idx)
spikemonitor = SpikeMonitor(group, record=rec_idx)

run(duration)

The use of the exporter requires only a few changes to an existing Brian 2 script. In addition to the standard brian2 import at the beginning of your script, you need to import the brian2tools.nmlexport package. You can then set a “device” called neuroml2 which will generate NeuroML2/LEMS code instead of executing your model. You will also have to specify a keyword argument filename with the desired name of the output file.

The above code will result in a file nml2model.xml and an additional file LEMSUnitsConstants.xml with units definitions in form of constants (necessary due to the way units are handled in LEMS equations).

The file nml2model.xml will look like this:

<Lems>
  <Include file="NeuroML2CoreTypes.xml"/>
  <Include file="Simulation.xml"/>
  <Include file="LEMSUnitsConstants.xml"/>
  <ComponentType extends="baseCell" name="neuron1">
    <Property dimension="voltage" name="v0"/>
    <Property dimension="time" name="tau"/>
    <EventPort direction="out" name="spike"/>
    <Exposure dimension="voltage" name="v"/>
    <Dynamics>
      <StateVariable dimension="voltage" exposure="v" name="v"/>
      <OnStart>
        <StateAssignment value="0" variable="v"/>
      </OnStart>
      <Regime name="refractory">
        <StateVariable dimension="time" name="lastspike"/>
        <OnEntry>
          <StateAssignment value="t" variable="lastspike"/>
        </OnEntry>
        <OnCondition test="t .gt. ( lastspike + 5.*ms )">
          <Transition regime="integrating"/>
        </OnCondition>
      </Regime>
      <Regime initial="true" name="integrating">
        <TimeDerivative value="(v0 - v) / tau" variable="v"/>
        <OnCondition test="v .gt. (10 * mV)">
          <EventOut port="spike"/>
          <StateAssignment value="0*mV" variable="v"/>
          <Transition regime="refractory"/>
        </OnCondition>
      </Regime>
    </Dynamics>
  </ComponentType>
  <ComponentType extends="basePopulation" name="neuron1Multi">
    <Parameter dimension="time" name="tau_p"/>
    <Parameter dimension="none" name="N"/>
    <Constant dimension="voltage" name="mVconst" symbol="mVconst" value="1mV"/>
    <Structure>
      <MultiInstantiate componentType="neuron1" number="N">
        <Assign property="v0" value="20*mVconst * index /  ( N-1 ) "/>
        <Assign property="tau" value="tau_p"/>
      </MultiInstantiate>
    </Structure>
  </ComponentType>
  <network id="neuron1MultiNet">
    <Component N="100" id="neuron1Multipop" tau_p="10. ms" type="neuron1Multi"/>
  </network>
  <Simulation id="sim1" length="1s" step="0.1 ms" target="neuron1MultiNet">
    <Display id="disp0" timeScale="1ms" title="v" xmax="1000" xmin="0" ymax="11" ymin="0">
      <Line id="line3" quantity="neuron1Multipop[3]/v" scale="1mV" timeScale="1ms"/>
      <Line id="line64" quantity="neuron1Multipop[64]/v" scale="1mV" timeScale="1ms"/>
    </Display>
    <OutputFile fileName="recording_nml2model.dat" id="of0">
      <OutputColumn id="3" quantity="neuron1Multipop[3]/v"/>
      <OutputColumn id="64" quantity="neuron1Multipop[64]/v"/>
    </OutputFile>
    <EventOutputFile fileName="recording_nml2model.spikes" format="TIME_ID" id="eof">
      <EventSelection eventPort="spike" id="line3" select="neuron1Multipop[3]"/>
      <EventSelection eventPort="spike" id="line64" select="neuron1Multipop[64]"/>
    </EventOutputFile>
  </Simulation>
  <Target component="sim1"/>
</Lems>

The exporting device creates a new ComponentType for each cell definition implemented as a Brian 2 NeuronGroup. Later that particular ComponentType is bundled with the initial value assignment into a a new ComponentType (here called neuron1Multi) by MultiInstantiate and eventually a network (neuron1MultiNet) is created out of a defined Component (neuron1Multipop).

Note that the integration method does not matter for the NeuroML export, as NeuroML/LEMS only describes the model not how it is numerically integrated.

To validate the output, you can use the tool jNeuroML. Make sure that jnml has access to the NeuroML2CoreTypes folder by setting the JNML_HOME environment variable.

With jnml installed you can run the simulation as follows:

jnml nml2model.xml

Supported Features

Currently, the NeuroML2 export is restricted to simple neural models and only supports the following classes (and a single run statement per script):

• NeuronGroup - The definition of a neuronal model. Mechanisms like threshold, reset and refractoriness are taken into account. Moreover, you may set the initial values of the model parameters (like v0 above).
• StateMonitor - If your script uses a StateMonitor to record variables, each recorded variable is transformed into to a Line tag of the Display in the NeuroML2 simulation and an OutputFile tag is added to the model. The name of the output file is recording_<<filename>>.dat.
• SpikeMonitor - A SpikeMonitor is transformed into an EventOutputFile tag, storing the spikes to a file named recording_<<filename>>.spikes.

Limitations

As stated above, the NeuroML2 export is currently quite limited. In particular, none of the following Brian 2 features are supported:

• Synapses
• Network input (PoissonGroup, SpikeGeneratorGroup, etc.)
• Multicompartmental neurons (SpatialNeuronGroup)
• Non-standard simulation protocols (multiple runs, store/restore mechanism, etc.).