Simulation control: a family of simulations

Modeling projects often involve executing a series of simulation runs, analyzing the output of each run, and summarizing the results. This exercise will have you examine how the location of an excitatory synapse affects the peak depolarization at the soma. In doing this, you will learn:

Conceptual Model

The idea behind this exercise is a thought experiment that explores the relationship between synaptic location and the size of the EPSP observed at the soma. In this experiment you attach an excitatory synapse to the cell at various locations along a particular dendrite, activate the synapse, and record the peak amplitude observed at the soma. After collecting these data, you plot somatic EPSP amplitude vs. position of the synapse along the dendrite.

Simulation

Computational model

This exercise uses the ball and stick model you have already seen.

In the exercises/simulation_families directory, start NEURON by running init.py (python -i init.py). This particular init file
from neuron import h, gui

# load the ballstk cellbuilder
h.load_file("ballstk.ses")

# eventually becomes a custom GUI
h.load_file("rig.ses")

makes NEURON read the session file for a CellBuilder that contains the specification of the model. You will use this CellBuilder to adjust the model parameters.
Note: Make sure the CellBuilder's Continuous Create button is checked. Otherwise the sections of the ball and stick model will not exist.

Managing the spatial grid with the CellBuilder

1. Geometry/Specify Strategy : select d_lambda for the all subset. Also make sure that no section has an overriding strategy.
2. toggle Specify Strategy off
3. make sure that d_lambda = 0.1 space constant at 100 Hz

How many segments were created?

for sec in h.allsec():
    print('%s nseg = %d' % (sec, sec.nseg))

Where are the nodes located?
for seg in dend:
    print('%g %g' % (seg.x, seg.x * dend.L))

If these locations aren't particularly "esthetic," you can assign nseg a new (larger) value manually (odd multiples of 5 are nice). You could do this with a Python statement like
dend.nseg=25
but this would be a potential cause of confusion, so you should specify nseg through the CellBuilder instead.

Remember, you are using the CellBuilder with Continuous Create on. This means that, if you change the model specification in the CellBuilder, or even just toggle Continuous Create off and on, any changes to cell properties that you made outside of the CellBuilder (e.g. by executing Python calls at the >>> prompt) could be overridden.

Using the computational model

1. BUILD THE GUI

Set up a graphical interface that lets you apply an AlphaSynapse to the model (time constant 1.0 ms, peak conductance 0.005 umho) while observing Vm at the middle of the soma.

2. TEST THE MODEL

Put the synapse at the proximal end of the dendrite, turn on Keep Lines in the voltage graph, and run a simulation. Then reduce the peak synaptic conductance to 0 and run another. Use View = plot to get a better look at somatic Vm.

What's wrong with these responses? (hint: increase Tstop to 50 ms and run another simulation)
Change dendritic e_pas to -65 mV (use the CellBuilder's Biophysics page!) and try another run. Does this help? Why?

3. INITIAL EXPLORATION OF THE MODEL

Place the synapse at several different positions along the dendrite. Find and plot the peak amplitude of the somatic EPSP vs. synaptic location.

You will need a procedure that moves the synapse to a specified location. I have provided putsyn.py, which contains a function (putsyn()) that takes a single numeric argument in the range [0, 1] (i.e. the desired synaptic location).

putsyn() does these things:

  1. Verifies that the requested location is actually in the range [0, 1].
  2. Places the synapse in the section (uses the Point Process .loc() function).
  3. Since point processes are always placed at the nearest node, and nodes are located at 0, 1, and the center of each segment, putsyn() must determine the actual location of the synapse (uses .get_segment().x). This is assigned to a global variable called h.synloc.
  4. Executes the statement h.run() (equivalent to clicking the Init & Run button).
Load putsyn (from putsyn import putsyn), and then invoke putsyn() with a couple of different arguments to see what happens. Use the voltage axis graph's crosshairs to find the peak amplitude of the epsp at the soma.

You might also want to append the statement from putsyn import putsyn to the end of init.py for future use.

4. SWITCHING TO PRODUCTION MODE

In principle, you could type putsyn() many times, with different numerical arguments, measure the epsp peaks manually, write down the synaptic location and the corresponding peak depolarization, and then plot the results by hand, but that would be a poor use of your time. It's much better to learn how to automate repetitive tasks.

Here's an outline of one approach to automating this particular modeling experiment:

For each node along dend
move the synapse to that node
run a simulation
determine the most depolarized somatic Vm produced by a synapse at that location
save this value and the synaptic location in a pair of vectors
Plot the vector of peak values vs. the vector of synaptic locations
Use a text editor to create a function called profile that implements this approach. Put it in a file called myprofile.py, and then use the command
from myprofile import profile
to make it available to your model.

You already have putsyn(), which takes care of the second and third items in this outline.
It may be helpful to know about :
the Vector class in general    Vector record()    Vector max()    Vector append()    Vector plot()    the Graph class

Here is a skeleton of one possible implementation of such a function.

5. THINGS TO TRY

1. Compute the actual EPSP amplitudes by subtracting the -65 mV baseline from the soma responses and plot the results.

2. Plot peak EPSP amplitude as a function of anatomical distance along dend in microns.

2. What would happen if the somatic HH currents were blocked? Use the CellBuilder to reduce gnabar_hh and gkbar_hh to 0. Make sure to change el_hh to -65 mV before running a new series of simulations (why or why not? and what if you don't?).

Compare these results with what you saw when HH currents were not blocked. Do spike currents in the soma enhance all EPSPs, or does the nature of the effect depend on synaptic location?


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