Brain-Inspired Modular Reservoir based on Spiking Neurons for Multi-Task Learning

Brain-Inspired computing has attracted growing interest in neuromorphic engineering for replicating brain mechanisms to improve artificial neural networks. Although multi-task learning (MTL) outperforms single-task approaches, existing brain-inspired models struggle with knowledge transfer between tasks. This paper introduces B-MRML, a bio-inspired modular reservoir architecture for liquid state machines (LSMs), which enables multi-task learning through specialized blocks inter-connected through recurrent pathways to facilitate cross-task knowledge sharing. To optimize the reservoir structure efficiently, an evolutionary neural architecture search (ENAS) strategy is employed with surrogate models to reduce computational costs. Experimental results confirm that B-MRML significantly enhances MTL performance in brain-inspired systems, offering a novel paradigm for advancing neuromorphic computing research.