{
  "annotation_id": "76a31c81-78cb-43d3-9b03-8163fd26940c",
  "date_created": "2026-02-17T05:53:07.836000Z",
  "date_modified": "2026-02-17T05:53:07.836000Z",
  "file_hash": "7cc4786b1ab236fca8a3bd2e49b5288d136bb69234c8668a0f94c58f6e62d291",
  "private": false,
  "record": {
    "abstract": "Feedback-rich neural architectures can regenerate earlier representations and inject temporal context, making them a natural setting for strictly local synaptic plasticity. We ask whether a minimal, backpropagation-free feedback--Hebbian system can already express interpretable continual-learning--relevant behaviors under controlled training schedules. We introduce a compact prediction--reconstruction architecture with two feedforward layers for supervised association learning and two dedicated feedback layers trained to reconstruct earlier activity and re-inject it as additive temporal context. All synapses are updated by a unified local rule combining centered Hebbian covariance, Oja-style stabilization, and a local supervised drive where targets are available, requiring no weight transport or global error backpropagation. On a small two-pair association task, we characterize learning through layer-wise activity snapshots, connectivity trajectories (row/column means of learned weights), and a normalized retention index across phases. Under sequential A-\u003eB training, forward output connectivity exhibits a long-term depression (LTD)-like suppression of the earlier association while feedback connectivity preserves an A-related trace during acquisition of B. Under deterministic interleaving A,B,A,B,..., both associations are concurrently maintained rather than sequentially suppressed. Architectural controls and rule-term ablations isolate the role of dedicated feedback in regeneration and co-maintenance, and the role of the local supervised term in output selectivity and unlearning. Together, the results show that a compact feedback pathway trained with local plasticity can support regeneration and continual-learning--relevant dynamics in a minimal, mechanistically transparent setting.",
    "arxiv_id": "2601.06758",
    "authors": [
      "Josh Li"
    ],
    "categories": [
      "cs.NE",
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "A Backpropagation-Free Feedback-Hebbian Network for Continual Learning Dynamics",
    "url": "https://arxiv.org/abs/2601.06758",
    "version": "v2"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "510c42ef-2777-4d49-b3cd-0d0ab7abda4e",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}