{
  "annotation_id": "818d1dbd-a7e3-47bf-9c98-ee531fbc1616",
  "date_created": "2026-03-02T18:01:34.395000Z",
  "date_modified": "2026-03-02T18:01:34.395000Z",
  "file_hash": "1122b85c2682f643953a63d03a8f5b1b61dcb4e0fe2543d648f1e0fb671dda40",
  "private": false,
  "record": {
    "abstract": "Precise timing of spikes and temporal locking are key elements of neural\ncomputation. Here we demonstrate how even strongly heterogeneous, deterministic\nneural networks with delayed interactions and complex topology can exhibit\nperiodic patterns of spikes that are precisely timed. We develop an analytical\nmethod to find the set of all networks exhibiting a predefined pattern\ndynamics. Such patterns may be arbitrarily long and of complicated temporal\nstructure. We point out that the same pattern can exist in very different\nnetworks and have different stability properties.",
    "arxiv_id": "q-bio/0601003",
    "authors": [
      "Raoul-Martin Memmesheimer",
      "Marc Timme"
    ],
    "categories": [
      "q-bio.NC",
      "cond-mat.dis-nn"
    ],
    "doi": "10.1103/PhysRevLett.97.188101",
    "title": "Designing the Dynamics of Spiking Neural Networks",
    "url": "https://arxiv.org/abs/q-bio/0601003"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "6a74d168-978e-4857-9211-d8e5f3a5906e",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}