Improving liquid state machines through iterative refinement of the reservoir

Liquid state machines (LSMs) exploit the power of recurrent spiking neural networks (SNNs) without training the SNN. Instead, LSMs randomly generate this network and then use it as a filter for a generic machine learner. Previous research has shown that LSMs can yield competitive results; however, the process can require numerous time consuming epochs before finding a viable filter. We have developed a method for iteratively refining these randomly generated networks, so that the LSM will yield a more effective filter in fewer epochs than the traditional method. We define a new metric for evaluating the quality of a filter before calculating the accuracy of the LSM. The LSM then uses this metric to drive a novel algorithm founded on principals integral to both Hebbian and reinforcement learning. We compare this new method with traditional LSMs across two artificial pattern recognition problems and two simplified problems derived from the TIMIT dataset. Depending on the problem, our method demonstrates improvements in accuracy of from 15 to almost 600%.