D-LSM: Deep Liquid State Machine with unsupervised recurrent reservoir tuning

Abstract

The Liquid State Machine (LSM) is a biologically plausible model of computation for recurrent spiking neural networks, which offers promising solutions to real-world applications in both software and hardware based systems. At the same time, deep feedforward rate-based neural networks such as convolutional neural networks (CNNs) have achieved great success in many computer vision related applications. However, a systematic exploration of deep recurrent spiking neural networks is lacking. We propose a new model of Deep Liquid State Machine (D-LSM), which simultaneously explores the powers of recurrent spiking networks and deep architectures. D-LSM consists of multiple basic LSM processing and pooling stages. Recurrent reservoir networks across different LSM stages act as nonlinear filters capable of extracting spatio-temporal features of increasingly higher levels from the input. We propose to train the D-LSM practically by adopting unsupervised training (e.g. through STDP) for recurrent reservoirs and spike-based supervised rules for the final readout stage. The perspective of realizing D-LSM based hardware processors is also presented.