Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation

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. The long-range connectivity is largely fixed, preserving routing e

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ethics.ai (1 May 2026), “Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation,” evidence record 5130, https://ethics.ai/record/5130 (originally published by arXiv).

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