dorsal/arxiv
View SchemaNeuronal networks and controlled symmetries, a generic framework
| Authors | Léonard Gérard, Jean-Jacques Slotine |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0612049 |
| URL | https://arxiv.org/abs/q-bio/0612049 |
Abstract
The extraordinary computational power of the brain may be related in part to the fact that each of the smaller neural networks that compose it can behave transiently in many different ways, depending on its inputs. Mathematically, input continuity helps to show how a large network, constructed recursively from smaller blocks, can exhibit robust specific properties according to its input. By extending earlier work on synchrony and symmetry, we exploit input continuity of contracting systems to ensure robust control of diverse spatial and spatio-temporal symmetries of the output signal in such a network.
{
"annotation_id": "dc4ab587-c447-4c97-b977-cec4c81a6c95",
"date_created": "2026-03-02T18:01:35.819000Z",
"date_modified": "2026-03-02T18:01:35.819000Z",
"file_hash": "903e95b52a7a367f27d242d7250a5ea8eddd4d479856bd648e67c11ba7ac5219",
"private": false,
"record": {
"abstract": "The extraordinary computational power of the brain may be related in part to\nthe fact that each of the smaller neural networks that compose it can behave\ntransiently in many different ways, depending on its inputs. Mathematically,\ninput continuity helps to show how a large network, constructed recursively\nfrom smaller blocks, can exhibit robust specific properties according to its\ninput. By extending earlier work on synchrony and symmetry, we exploit input\ncontinuity of contracting systems to ensure robust control of diverse spatial\nand spatio-temporal symmetries of the output signal in such a network.",
"arxiv_id": "q-bio/0612049",
"authors": [
"L\u00e9onard G\u00e9rard",
"Jean-Jacques Slotine"
],
"categories": [
"q-bio.NC"
],
"title": "Neuronal networks and controlled symmetries, a generic framework",
"url": "https://arxiv.org/abs/q-bio/0612049"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "85563f76-8fe7-4406-bb70-c52d3b4db9f0",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}