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Tutorial #3. Changing Neuronal Parameters Before and During a Simulation

This tutorial assumes that you have completed Tutorial #2.

Changing Neuronal Parameters Before a Simulation

It is trivial to change an individual neuron's parameters or initial conditions in the .net file with a text editor. However, when networks contain many neurons this can become tedious. Especially, if you want to change the parameter to a random value from a Gaussian distribution.

In the .setup file, we can add any number of Change directives to alter parameter values of all the neurons with the network. See the .setup file documentation for what operations are allowed here.

zca.setup:

Network zca.net
Dt 0.05
Print Step Size 1
Output V hhs nns bbs zzs s_AMPA
Experiment control 1
Change g_NaP = 0 # Sets the g_NaP value for each neuron to zero
Change g_A *= 2.0 # Doubles whatever the current g_A value is for that neuron
Change g_syn = gauss(1.0, 0.25)   # Changes all the neurons in the network so that g_syn is a value pulled
      # from a Gaussian distribution with mean of 1.0 and standard
      # deviation of 0.25.

These operations are not computed until immediately before running the simulation, so the original values in the .net file are preserved. This means that it is possible to maintain one copy of a .net file and create a series of .setup files that use that .net file but block specific currents. Since the simulation's data goes into a sub-directory mapped to the .setup filename, running each simulation with a different .setup file results in a different set of results.

For example, we might want to block g_A, g_NaP, and g_Z in separate simulations.

gA_0.setup:

Network zca.net
Dt 0.05
Print Step Size 1
Output V hhs nns bbs zzs s_AMPA
Experiment control 1
Change g_A = 0

gNaP_0.setup:

Network zca.net
Dt 0.05
Print Step Size 1
Output V hhs nns bbs zzs s_AMPA
Experiment control 1
Change g_NaP = 0

gZ_0.setup:

Network zca.net
Dt 0.05
Print Step Size 1
Output V hhs nns bbs zzs s_AMPA
Experiment control 1
Change g_Z = 0

Then, running each simulation:

> cd simulations/ca1
> nne_run gA_0.setup
> nne_run gNaP_0.setup
> nne_run gZ_0.setup

The following three sets of data are generated (in the gA_0/, gNaP_0/, and gZ_0/ directories) and you can plot them like this:

> nne_plotPair gA_0.setup 1 2

We can confirm that the appropriate changes took effect because in each data directory is a file called final.net that contains the network and all the final parameters and state conditions for each neuron for that run.

Much more fine grain control over which neurons are changed can be achieved as indicated in the .setup file documentation.

Changing Neuronal Parameters During a Simulation

Suppose you want to run your simulation for one second with the neurons unconnected and instantaneously connect the neurons to see what effect this might have. This is where the importance of the .exp files come into play.

First, we edit the original zca.setup file to use an additional file in the Experiment directive:

zca_unconnected_control.setup:

Network zca.net
Dt 0.05
Print Step Size 1
Output V hhs nns bbs zzs s_AMPA
Experiment unconnected 2 control 2

Now, we need to create the new file, called unconnected.exp, in the same directory as control.exp with the following contents:

unconnected.exp:

Time: 500
Changes
g_syn = 0

and edit control.exp to have a comparable time:

control.exp:

Time: 500

This sets each neuron's g_syn value to 0 for the first second of the simulation (i.e., 2 iterations of the 500 ms long 'unconnected' experiment) and then switches back to the original g_syn for the rest of the simulation. The results look like this:

zca_unconnected_control

Like in the previous set of examples that blocked currents before running separate simulations, we can sequentially block currents during one simulation by defining multiple .exp files.

>> Tutorial #4. Creating Larger Networks and Introducing Parameter Heterogeneity and Variable Connectivity