{
  "annotation_id": "18b19fee-f8ee-487c-ba97-4bc3b1abd3b0",
  "date_created": "2026-03-02T18:01:31.151000Z",
  "date_modified": "2026-03-02T18:01:31.151000Z",
  "file_hash": "e7cac7c2909e31d630693454867d06c656cd30c410981e3e1b4085c285eaf44d",
  "private": false,
  "record": {
    "abstract": "In randomly connected networks of pulse-coupled elements a time-dependent\ninput signal can be buffered over a short time. We studied the signal buffering\nproperties in simulated networks as a function of the networks state,\ncharacterized by both the Lyapunov exponent of the microscopic dynamics and the\nmacroscopic activity derived from mean-field theory. If all network elements\nreceive the same signal, signal buffering over delays comparable to the\nintrinsic time constant of the network elements can be explained by macroscopic\nproperties and works best at the phase transition to chaos. However, if only 20\npercent of the network units receive a common time-dependent signal, signal\nbuffering properties improve and can no longer be attributed to the macroscopic\ndynamics.",
    "arxiv_id": "q-bio/0502031",
    "authors": [
      "Julien Mayor",
      "Wulfram Gerstner"
    ],
    "categories": [
      "q-bio.NC"
    ],
    "doi": "10.1103/PhysRevE.72.051906",
    "title": "Signal buffering in random networks of spiking neurons: microscopic vs.   macroscopic phenomena",
    "url": "https://arxiv.org/abs/q-bio/0502031"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "9a17755a-9103-4989-9c09-a437cab93c4c",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}