Auditory DMS simulation

The following tutorial will show you how to install LSNM and perform a visual Delayed Match-to-Sample (DMS) simulation. As shown in the figure below, the DMS experiment to be simulated consists of a visual stimulus (S1) being presented to a subject, followed by a delay period during which time no stimulus is presented. At the end of the delay period, a second visual stimulus (S2) is presented to the subject, after which the subject must respond whether S1 and S2 are the same or different (Response).

NOTE: If you already have a working copy of LSNM, go directly to STEP 2

1. Download a copy of LSNM to your local workstation

• Launch Firefox or another web browser available on your local Unix workstation and copy/paste the following internet address (or just click on the highlighted link if you are in a browser already): https://github.com/NIDCD/lsnm_in_python.

• When the LSNM Github page displays, click on the button Download ZIP located on the right sidebar. Click on Save File to download the zip file containing the LSNM code to your Downloads folder.

• Inspect your downloads folder to make sure you have downloaded the LSNM files. There should be a file called lsnm-master.zip in it.

• Unzip the file in your preferred location:

    $ unzip lsnm-master.zip
  

• Change the name of the lsnm directory (to keep the sim executable from having problems with the special character -:

    $ mv lsnm-master lsnm
  

• NOTE: If you do not have access to an internet browser you can grab a copy of the LSNM repository by typing the following command on your local Unix workstation. Please note that at a directory called sim will be created in the directory where you are currently located:

    $ git clone https://github.com/NIDCD/lsnm_in_python.git
  

2. Now execute the simulator:

    $ cd simulator
    $ python2.7 sim.py

A GUI window will be displayed on the screen. Please select your model as auditory_model/model.txt, your weights as auditory_model/weightslist.txt, and your script as auditory_model/script.txt

3. Plot your output:

    $ cd ../visualization
    $ python2.7 plotAuditory.py