Time-Varying Signals Classification Using a Liquid State Machine

The liquid state machine is a novel computation paradigm based on the transient dynamics of recurrent neural circuitry. In this paper it is shown that this systems can be used to recognize complex stimuli composed by non-periodic signals and to classify them in a very short time. Even if the network is trained over a segment of the signal the classification task is completed in a time interval significantly shorter than the time-window used for the training. Stimuli composed by many complex signals are recognized and classified even if some signals are absent.