In this exercise you have to perform some experiments which will help
you in understanding the concept of liquid state machine (LSM). For
this exercise you have to use the
lsm.tar.gz (download tar.gz, download tar.zip) which
contains all the source code that you will need to perform your
experiments. You can also download the documents
which provide a tutorial on using this
code. This code is guaranteed to work only on linux machines and with
matlab 6.5 (there might be problems with matlab 7). So it is
recommended that you work on these exercises in our student lab.
In this exercise you have the following setup: You have a recurrent spiking neural network, also called as the liquid, that is composed of 135 leaky-integrate-and-fire neurons. This liquid is receiving 2 timevarying poisson spike trains as the input. Three linear readouts are connected to all the neurons in this liquid. These linear readouts are supposed to calculate three different target functions. The first readout has to find the sum of rates of the two input spike trains in the last 30 msec. The second readout has to find the spike correlations in the input channels in the last 75 msec. The third readout should calculate at time the sum of rate of the input channels in the time window msec.
lsm.tar.gztarball into your home directory. Now change your working directory to
spike_train_classificationhere. The last 3 are fully working demos which have been supplied to you so that you can become comfortable with playing around with the system. The
ex_lsmdirectory is where you shall be working for this exercise.
startup.m. If this file is not executed automatically in matlab, you should run it once at the start of your matlab session to initialize the paths.
segment_classification. Run matlab and on the command prompt type
seg_class. The simulation should run smoothly.
Run the code by typing
ex_lsm in matlab in this directory.
You can change circuit parameters in the file
lambdachanges the ammount of recurrent connection, whereas the parameter
Wscalescales the strength of connections). Describe your observations about the effects of recurrency on the performance (collect some statistics and emphasize your results with some plots). Is recurrency helpful here?