  Liquid State Machine (LSM) is a neural model with real time computations
which transforms the time varying inputs stream to a higher dimensional space.
The concept of LSM is a novel field of research in biological inspired
computation with most research effort on training the model as well as finding
the optimum learning method. In this review, the performance of LSM model was
investigated using two learning method, online learning and offline (batch)
learning methods. The review revealed that optimal performance of LSM was
recorded through online method as computational space and other complexities
associated with batch learning is eliminated.
