Synaptic Modification Experiment

You will be using Basic Hebbing Learning for this activity. This is a simulated experiment. The goal of this experiment is to gather data that will help us understand the way it is thought that synapses can change how strongly one neuron can impact another neuron.

Screen layout

When you open the experiment, you will see the following screen in a window that will fill the screen:

Basic Hebbin Experiment

You will not need to use all the controls. The settings you will adjust will be the Number of Action Potentials slider un the upper right of the screen and the Magnitude slider under the Postsynaptic Stimulation controls in the lower left of the screen. To run the animation after you have adjusted the settings to the desired values, use the Animate button in the upper left of the screen. Reseting values use the Reset Weights button (not the Reset button). You can read all of your values you need in the Summary table in the upper right of the screen except for the Magnitude of the postsyanptic stimulation which you can read from the bottom of the slider.


Please follow the instructions below.

For each step beow, repeat 10 time.

After each animation, preset th e Reset Weights button (not the Reset button)

Data To Collect

After each run collect the following information:

Number of Action Potentials on Input Neuron A
Magnitude of Postsynaptic Stimulation (Read from Slider)
Number of Action Potention on Output Neuron
Connection Strength A after animation (Read from value at synapse or the table)

Step 1


Number of Action Potentials: 9

Postsynaptic magnitude : 0

Step 2

Number of Action Potentials: 0

Postsynaptic magnitude : 1.0

Step 3

Number of Action Potentials: 9

Postsynaptic magnitude : 1.0


Data to Submit

There are 30 runs total. Collect the data and determined the averages for each of the four variables you collected separately for each of the three steps. Report the averages to me in an excel sheet.


Also, as for all on a word page, write 2-3 sentects were you interpret what you think is going on in your data.

Here is the link to the experiment.