The Reinforced Liquid State Machine: A New Training Architecture for Spiking Neural Networks

This work presents a novel Spiking Neural Network training architecture based on a deepened Liquid State Machine integrating Winner-Takes-All computation and Reward-Modulated Synaptic Plasticity.The networks performance is evaluated on the Heidelberg Dataset for spoken digit recognition.A two-layer liquid configuration improves classification accuracy by 5% over a single-layer baseline, while incorporating feedback between liquid layers.This architecture demonstrates that deep liquid models, combined with feedback and reward-driven learning, can effectively capture complex spatio-temporal patterns, offering significant advantages in terms of accuracy over traditional Liquid State Machines.