{
  "id": 5130,
  "url": "https://arxiv.org/abs/2605.00402v1",
  "title": "Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation",
  "summary": "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",
  "authors": "Bo Tang, Weiwei Xie",
  "category": "research",
  "topics": "environment,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-01T04:45:21.000Z",
  "fetched_at": "2026-07-14T16:31:31.212Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5130",
  "original_url": "https://arxiv.org/abs/2605.00402v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}