{
  "annotation_id": "77e9c6b0-d877-4e85-8016-445d533fcc0c",
  "date_created": "2026-03-02T18:01:31.541000Z",
  "date_modified": "2026-03-02T18:01:31.541000Z",
  "file_hash": "56e0a59c3f02bd37d1e6c8b6733c0d1980194bcb57adb0f591fde016ca6f32e3",
  "private": false,
  "record": {
    "abstract": "We investigate the performance of sparsely-connected networks of\nintegrate-and-fire neurons for ultra-short term information processing. We\nexploit the fact that the population activity of networks with balanced\nexcitation and inhibition can switch from an oscillatory firing regime to a\nstate of asynchronous irregular firing or quiescence depending on the rate of\nexternal background spikes.\n  We find that in terms of information buffering the network performs best for\na moderate, non-zero, amount of noise. Analogous to the phenomenon of\nstochastic resonance the performance decreases for higher and lower noise\nlevels. The optimal amount of noise corresponds to the transition zone between\na quiescent state and a regime of stochastic dynamics. This provides a\npotential explanation on the role of non-oscillatory population activity in a\nsimplified model of cortical micro-circuits.",
    "arxiv_id": "q-bio/0502030",
    "authors": [
      "Julien Mayor",
      "Wulfram Gerstner"
    ],
    "categories": [
      "q-bio.NC"
    ],
    "title": "Transient Information Flow in a Network of Excitatory and Inhibitory   Model Neurons: Role of Noise and Signal Autocorrelation",
    "url": "https://arxiv.org/abs/q-bio/0502030"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "1e71418d-71cc-4cac-aa02-cc8b9cae0527",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}