{
  "abstract": "Physical constraints in the brain favor short, distance-dependent connections. However, short-range projections are not merely a structural compromise; they also serve essential functional roles. Here, we explore networks with balanced excitation and inhibition, demonstrating that short excitatory projections increase the number of common presynaptic inputs, thereby enhancing the correlations of neural responses. Strengthened recurrent correlations can lead to bursting states, amplifying postsynaptic neuronal activity and supporting signal propagation over long distances within the network. Moreover, even without an explicitly imposed modular structure, short excitatory connections can give rise to functional clustering and support a broad spectrum of modular functions. In this model, short excitatory projections are associated with the emergence of column-like functional organization and dynamic burst propagation from a single structural principle, offering a candidate mechanism by which the brain could transform structural wiring constraints into opportunities for complex computation and distributed processing. These findings motivate further work on how such structural limitations may more generally be turned into computational opportunities in biological circuits.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Computational Neuroscience, Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf, Hamburg University, Hamburg, Germany",
    "School of Life Science and Technology, ShanghaiTech University, Shanghai, China",
    "School of Electrical Engineering and Automation, Nantong University, Nantong, China",
    "Department of Health Science, Boston University, Boston, MA, United States"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "M. Sharif Hussainyar"
    },
    {
      "affs": [
        1
      ],
      "name": "Di Yun"
    },
    {
      "affs": [
        0
      ],
      "name": "Dong Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Dong Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Ji-Song Guan"
    },
    {
      "affs": [
        0
      ],
      "name": "Claus C. Hilgetag"
    },
    {
      "affs": [
        3
      ],
      "name": "Claus C. Hilgetag"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cxzvqfrg6dq52.bib",
  "cited_by": 0,
  "doi": "10.3389/fncom.2026.1965686",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fncom.2026.1965686/full",
  "id": "cxzvqfrg6dq52",
  "issns": [
    "1662-5188"
  ],
  "landing_url": "https://doi.org/10.3389/fncom.2026.1965686",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RC321-571"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": "Frontiers Media S.A.",
  "ris": "https://research.draconitic.com/paper/cxzvqfrg6dq52.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T14:21:27Z",
      "native_id": "0df92593a3114a959420c665fb9ea054",
      "source": "doaj",
      "url": "https://doi.org/10.3389/fncom.2026.1965686"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "journal-openalex:2805|Neural dynamics and brain function",
      "subfield": "neuroscience"
    },
    {
      "field": "medicine",
      "origin": "lcc:RC321-571",
      "subfield": "general"
    }
  ],
  "title": "Short excitatory projections promote burst activity and modular functional organization in neural networks",
  "title_text": "Short excitatory projections promote burst activity and modular functional organization in neural networks",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T14:20:24Z",
  "url": "https://research.draconitic.com/paper/cxzvqfrg6dq52",
  "venue": "Frontiers in Computational Neuroscience",
  "versions": []
}