Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation

ArXi:2605.00402v1 Announce Type: cross Spiking Neural Networks (SNNs) provide a promising framework for energy-efficient and biologically grounded computation; however, scalable learning in deep recurrent architectures with sparse connectivity remains a major challenge. In this work, we propose a structured multi-layer recurrent SNN architecture composed of locally dense recurrent layers augmented with sparse small-world long-range projections to a readout population.