Walk-Through: Build a Neuron

Neurosim can replicate many published neuronal models, particularly those based on a single-compartment HH-type formalism. As an example, we will build a neuron from the description given by Butera, et al., 1999. This simulates a neuron from the region of the mammalian brain that is involved in generating the respiratory rhythm. The neuron has interesting properties because it can be silent, or show a steady spike frequency (pacemaker activity), or show rhythmic bursts of spikes, depending on the level of stimulation.

We have now got a neuron with a purely passive membrane that we can build on. It may be a good idea to save this, and to continue to save (with a new name each time) repeatedly as you build the model, so that you can retrieve an earlier version if something goes wrong.

A read-through of the paper shows that the neuron has 3 voltage-dependent channels plus the usual leakage channel. The voltage-dependent channel kinetics are specified in terms of the time-infinity open probability and time constant of the activation and inactivation gates that they contain. Since the kinetic equations do not exactly match the build-in types in Neurosim, we will specify user-defined equations for the kinetics, when we come to them.

One immediate problem is that the Butera paper gives the channel conductances and membrane capacitance as absolute values, but Neurosim expects this type of parameter as an area-specific value, i.e. per square centimetre, so some conversion will be needed.

Here is a list of the parameters that we need to covert, and their target absolute values. The right-hand column in red has been pre-filled with calculated values obtained as described next.

Parameter

Target Absolute value

Calculated Area-Specific value
(diameter = 25.85 µm)

Membrane capacitance

21 pF

1

Leak conductance

2.8 nS

0.1333 mS/cm2

Fast sodium maximum conductance

28 nS

1.333 mS/cm2

Persistent sodium maximum  conductance

2.8 nS

0.1333 mS/cm2

Potassium maximum conductance

11.2 nS

0.5335 mS/cm2


To convert to area-specific values we need to know the area, but the paper does not specify the neuron’s size. However, if we assume an area-specific capacitance of 1 µF/cm2, which is a typical value for phospholipid membrane, we can calculate the surface area and hence cell diameter (assuming the cell is spherical) from the absolute capacitance, and then scale the conductance values appropriately.

You could now sit down with a calculator or Excel and write some formulae to do the maths, but alternatively you can get Neurosim to do the work:

Surface Scaling Helper

This is a non-modal utility dialog designed to help make the sort of conversions that we need here. Note that the dialog is also available from the Options menu in the Network module.

The first task is to calculate the size (diameter) of the neuron.

This tells the dialog that we want to derive the diameter from the absolute capacitance. It sets the area-specific capacitance value to 1 µF/cm2 (because that is the generally-accepted "best-guess" value) and disables its edit box.

The Diameter box (at the top-left of the dialog) now shows a value of 25.85 µm. Note this down (or directly edit the neuron diameter, as described later).

Next we want to calculate the area-specific conductance values for the various channels using this new diameter. The paper specifies the absolute leak conductance as 2.8 nS.

The value in the Unit area column changes to 0.1333+ mS/cm2. This is the required area-specific leak conductance for this size of neuron, given the absolute leak conductance value. Note the value.

You should end up with the values that are shown in red in the right-hand column of the table.

The Scaling Helper dialog also allows conversion in the other direction. If you edit a value in the Unit area column, then the Absolute value changes appropriately. The dialog can also calculate spherical diameter from surface area or volume, and vice versa.

Passive Properties

The neuron is represented in the Setup view by the green circle in the blue-ish preparation bath, and its diameter is shown in the middle. The diameter is currently at its default value of 50 µm.

It’s probably sensible to save again (Butera 2) before starting on the voltage-dependent channels. You could also click Start just to make sure things are still working.

Voltage-Dependent Channels

For reasons that will become apparent, the first channel we will add is the delayed rectifier potassium channel.

Delayed Rectifier Potassium Channel

This adds a voltage-dependent channel to the neuron, with its starting values set to those of the HH sodium channel in the squid. The Voltage-Dependent Channel Properties dialog opens automatically to show the parameter values for the new channel. We now need to edit those value.

Kinetics

The potassium channel has 4 activation gates and no inactivation gate, so we need to adjust the gates:

[Hint: If you are starting from scratch, it is easier to write equations externally in a plain-text editor like Notepad, and then copy and paste them into Neurosim. If you edit them directly and make a syntax mistake like not closing a bracket, the parser warns you, but then replaces your text with the previous text. This can be frustrating if you typed in a lot of complex text. Also, the equation parser is fast, and pre-calculates known values, so there is not much to be gained by hand-optimizing expressions.]

At this stage a reality check may be in order:

The Channel dialog will expand to show the K channel kinetics.

The Voltage-Dependent Channel Properties dialog should now look like this:

Voltage-dependent channel properties dialog

The voltage-dependent potassium channel is now complete. It is probably a good idea to save the file again (Butera 3).

Fast Sodium Channel

Now we will add the main sodium channel that makes spikes.

A new channel is made with ID 2, and again it has the properties of an exact HH sodium channel. Some of these are OK, but others need to be edited. Assuming that the Voltage-Dependent Channel Properties dialog box is still open:

Kinetics

The paper specifies that h = 1 – n. We cannot explicitly specify this internally in Neurosim, so we set an h gate whose equation mirrors those for the n gate. This is computationally less efficient (we calculate the same equation twice), but that is the penalty of a generalizable program.

Note that now there are two lines - the upward sloping line for the m activation variable, and the downward sloping line for the h inactivation variable.

You can see that the potassium channel (1) only has an activation curve, and that it activates at a slightly higher membrane potential than the sodium channel (2). Again, this fits with the equations.

Save the file again (Butera 4). At this stage it is probably a good idea to do a reality check on the Na and K channels.

The current and conductance traces should look very much like those in a standard HH-type voltage clamp experiment. HOWEVER, there is something a bit different about the sodium inactivation. Inactivation certainly occurs – the sodium conductance (bottom trace, red) increases then auto-terminates in each clamp step, as it should. However, the second clamp step produces a peak sodium conductance which is virtually identical to the first. There is little sign of persistent inactivation.

Now there is evidence for persistent sodium inactivation. Evidently de-inactivation occurs really quickly in this neuron. Why is this?

The h time constant follows a bell-shaped curve with a peak at 10 ms, but the centre is at about -30 mV. At the voltage-clamp holding potential range of -70 mV the time constant is only about 0.5 ms. This is why the sodium channel recovers from inactivation so quickly when the neuron repolarizes.

Persistent Sodium Channel

The final channel to add is the persistent sodium channel. The channel has this name because it inactivates very slowly. We could simply Add a channel as we did previously, but we can also base the new channel on the existing sodium channel.

A new channel is made with ID 3 and the channel properties dialog opens automatically, with this channel selected. At the moment it is an exact copy of the existing Na fast channel.

At this point it is worth doing another reality check.

The dialog expands to show the voltage-dependency of the various gate parameters. The default view shows the open Probability of the gates; check that activation gate probabilities increase with depolarization (slope up to the right), and inactivation (if any) probabilities decrease (slope down to the right). You can check the time constants by selecting the Time constant display option. The time constants should all have a bell curve (or be zero), but their peak values and the location of the peaks vary.

Now is a good time to test the model and see how it behaves

Your Results view should now show this:

Final Butera model

If your Results do not look like the image above, it probably means that you have made a data entry error somewhere. You can load a pre-built parameter file Butera final, and check this against your own model. Note that the Options: Configure view menu command has been used to remove some unused elements from the Setup view.

Exploring the Model

The Results above show that a brief depolarizing stimulus elicits a spike, but clearly there are some more spikes that follow on.

The stimulus elicits a burst of spikes, but this terminates after about half a second. This is an example of a plateau potential - a depolarizing response which outlasts the stimulus that initiates it.

Here are some stimulus regimes that produce a variety of results:

Note that Scroll has been selected as the Trigger mode in the Results view.

As the filename implies, the neuron produces regular bursts of spikes. This is exactly the same neuron, but a tonic stimulus is applied to depolarize the neuron (Pulse 1, Amplitude 0.016, Duration huge!). The initial resting potential (in the passive properties) has been set to -50 mV, so that the simulation stabilizes to its depolarized state more quickly. Also, note that the timebase is set to display in seconds rather than milliseconds (using the View: Time units menu command).

The top trace is the membrane potential, the middle trace is the stimulus, and the bottom trace is the activation and inactivation gate open probabilities of the persistent sodium channel. The oscillations are primarily driven by oscillations in this channel.

The membrane potential still oscillates! The activation and inactivation of the persistent sodium channel can be seen more clearly now that the neuron no longer generates fast HH-type spikes.

Note that the oscillations are now considerably faster. There is an extra large burst at the beginning before the system stabilizes into a steady state, but this could be avoided by setting a slightly depolarized initial resting potential of -48 mV.

After a few seconds of simulation time to achieve a steady state, the neuron produces a steady constant-frequency train of spikes. This is known as pacemaker activity.
You can observe the gamut of activity with a ramp stimulus.

The neuron goes from silent, to bursting, to pacemaker activity, as the stimulus strength gradually increases.

You should now see something like this:

Butera ramp stimulus

 

Incorporating the Neuron into a Network

Neurons which have been designed and built in the Advanced HH module can be incorporated into circuits in the Network module if desired.

We can either use the Neuron: Load menu command to load a new neuron and place it in the circuit, or we can take an existing neuron and modify its properties. We will do the latter, but starting from a minimum circuit.

Note that the neuron now has the diameter and passive properties as previously specified, and the three voltage-dependent channels show in the channel list.

To replicate the bursting activity of the previous simulation, we can add some tonic current.

There are two problems with the Results display. The first is that we have left-over traces from the default start-up condition. The second is that output from our neuron looks rather "ragged", with varying spike heights and burst durations. The former is easily fixed:

The second problem is caused by the integration step still being at the default value for integrate-and-fire neurons in Network module. This is too big for a full HH model.

The Results display looks better. Choosing the integration time step in Neurosim is largely a matter of trial-and-error. If you suspect the step is too large, you can make successive reductions until there is not much change in the display - at that point you have probably achieved a satisfactory value.

There is also a cosmetic problem with the Setup view - the yellow circle representing the neuron is rather small. This reflects the reduced diameter of the neuron, but since there are no other neurons for comparison, it would be nice if it were bigger.

This has not changed the physiology at all - the neuron's functional diameter is still 25.85 µm, it is just drawn as a bigger circle in the view.

Mix Integrate-and-Fire and HH

The neuron currently uses the persistent Na channel to generate the oscillations, and HH-type Na and K channels to generate spikes. We could replace the latter with the integrate-and-fire mechanism. This should be faster to compute, which might be important if we were building a network with a lot of neurons.

We will generate another neuron, and replace the Butera HH-type spike mechanism with the integrate-and-fire process.

You should see a single non-spiking oscillation (without the voltage-dependent spikes, the rhythm slows down somewhat due to reduced activation of the persistent sodium channel).

You should now see tonic spikes occurring, which is not quite what we expected. However, the integrate-and-fire mechanism does have a voltage-dependent after-hyperpolarization, and maybe this is pushing the neuron into pacemaker (tonic firing) mode.

Now we have a burst of spikes which is about the same duration as the underlying oscillation. The dynamics of the oscillations and spikes are not the same, but this is not surprising given the difference in the generation mechanisms. There are several facilities within the integrate-and-fire properties that can be adjusted to modify spike frequency.

The parameter file butera network is supplied in case you want to check your own working.

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