Reinforced Liquid State Machines -New Training Strategies for Spiking Neural Networks based on Reinforcements

Dominik Krenzer^1*Martin Bogdan^1,2,3

• ¹Leipzig University, Leipzig, Germany
• ²Center for Scalable Data Analytics and Artificial Intelligence, Leipzig, Germany
• ³School of Embedded Composite Artificial Intelligence, Leipzig, Germany

Feedback and reinforcement signals in the brain act as natures sophisticated teaching tools, guiding neural circuits to self-organization, adaptation, and the encoding of complex patterns.This study investigates the impact of two feedback mechanisms within a deep liquid state machine architecture designed for spiking neural networks. The new architecture integrates liquid layers, a winner-takes-all mechanism, a linear readout layer, and a reward-based reinforcement system to enhance learning efficacy. While traditional Liquid State Machines often employ unsupervised approaches, we introduce strict feedback to improve network performance by not only reinforcing correct predictions but also penalizing wrong ones. Specifically, we compare strict feedback to another feedback-strategy, known as forgiving feedback, that is not including punishment.Experimental results demonstrate that both feedback mechanisms significantly outperform the baseline unsupervised approach, achieving superior accuracy and adaptability in response to dynamic input patterns. This comparative analysis highlights the potential of feedback integration in deepened Liquid State Machines, offering insights into optimizing spiking neural networks through reinforcement-driven architectures.