{
  "abstract": "Spike-frequency adaptation (SFA) regulates neuronal excitability according to recent spiking activity, yet conventional neuromorphic models typically describe adaptation using single-timescale exponential recovery. To the best of our knowledge, most existing fractional spiking-neuron formulations introduce memory primarily through membrane or input-related dynamics rather than through the recovery of a spike-triggered adaptation current. Here, we propose fractional spike-frequency adaptation (F-SFA), in which an integer-order LIF membrane is coupled with a spike-triggered adaptation current undergoing fractional-order recovery. A compact multi-exponential embedding enables recursive implementation without explicit history convolution. Single-neuron experiments show firing rate adaptation, progressive inter-spike-interval (ISI) elongation, and post-stimulus recovery. F-SFA is further incorporated into an event-driven Lobula Giant Movement Detector 2 (LGMD2)-inspired model without feed-forward inhibition (FFI). The resulting model retains a pronounced response to dark looming while providing additional history-dependent attenuation of sustained translation and post-looming activity. These results support the feasibility of F-SFA as a compact mechanism for introducing multi-timescale spike-history-dependent adaptation into event-driven neuromorphic models.",
  "abstract_shown": true,
  "affiliations": [
    "School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China",
    "School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China",
    "College of Information Science & Engineering, Huaqiao University, Xiamen 361021, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yabin Deng",
      "orcid": "0000-0003-2414-9750"
    },
    {
      "affs": [
        1
      ],
      "name": "Pengzhou Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Bijing Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Yijin Wu"
    },
    {
      "affs": [
        0
      ],
      "name": "Junqi Huang"
    },
    {
      "affs": [
        2
      ],
      "name": "Zenan Huang",
      "orcid": "0000-0002-4119-4185"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/757sszcpxhhad.bib",
  "cited_by": 0,
  "doi": "10.3390/fractalfract10100713",
  "fulltext_url": null,
  "id": "757sszcpxhhad",
  "issns": [
    "2504-3110"
  ],
  "landing_url": "https://www.mdpi.com/2504-3110/10/10/713",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2208|Advanced Memory and Neural Computing"
    ]
  ],
  "openalex": "W7221536477",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-10",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/757sszcpxhhad.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:32Z",
      "native_id": "10.3390/fractalfract10100713",
      "source": "crossref",
      "url": "https://www.mdpi.com/2504-3110/10/10/713"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2504-3110",
      "subfield": "analysis-pdes"
    }
  ],
  "title": "Fractional Spike-Frequency Adaptation for Multi-Timescale Negative Feedback in an LGMD2 Inspired Neuromorphic Model",
  "title_text": "Fractional Spike-Frequency Adaptation for Multi-Timescale Negative Feedback in an LGMD2 Inspired Neuromorphic Model",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:32Z",
  "url": "https://research.draconitic.com/paper/757sszcpxhhad",
  "venue": "Fractal and Fractional",
  "versions": []
}