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.

翻译：液态国家机器(LSM)是一种神经模型,实时计算,将不同的输入流转化为更高维的空间,LSM的概念是生物学启发性计算研究的新领域,大多数研究都致力于培训模型和找到最佳学习方法,在这次审查中,使用两种学习方法,即在线学习和离线(批次)学习方法对LSM模型的性能进行了调查,审查显示,通过在线方法记录了LSM的最佳性能,因为计算空间和批次学习的其他复杂性已经消除。