Computational capability of liquid state machines with spike-timing-dependent plasticity

Abstract

Liquid state machine (LSM) is a recently developed computational model with a reservoir of recurrent spiking neural network (RSNN). This model has shown to be beneficial for performing computational tasks. In this paper, we present a novel type of LSM with self-organized RSNN instead of the traditional RSNN with random structure. Here, the spike-timing-dependent plasticity (STDP) which has been broadly observed in neurophysiological experiments is employed for the learning update of RSNN. Our computational results show that this model can carry out a class of biologically relevant real-time computational tasks with high accuracy. By evaluating the average mean squared error (MSE), we find that LSM with STDP learning is able to lead to a better performance than LSM with random reservoir, especially for the case of partial synaptic connections.