{
  "abstract": "Living neuronal networks transform inputs through recurrent cellular and population dynamics, yet it is unknown which network architecture supports which computation. Neurons-on-a-chip turn this question into a design problem because microchannels guide axonal growth and set the network architecture. We introduce IC$^3$, an Integrated Characterisation of Communication-Driven Computation, which characterizes network state through neuronal dynamics, functional communication, and structural support. We implemented nine architectures \\textit{in silico} as conductance-based spiking networks and tested each on frequency decoding, temporal-order discrimination, and fading memory. Predominantly feedforward circuits decoded best. Sequential Chain and Microchannel Diode had the lowest IC$^3$ and recruited a third of reachable neurons, yet achieved the two highest scores on both classification tasks. Across architectures, higher IC$^3$ went with lower classification scores. We term this new direction \\emph{neurotopomorphic computing}, in which the physical organisation of neuronal connectivity is engineered as part of the computing substrate.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.06065",
  "author_total": 1,
  "authors": [
    {
      "name": "Michael Taynnan Barros"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8sztwtpn4f2pf.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.06065",
  "fulltext_url": null,
  "id": "8sztwtpn4f2pf",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.06065",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.ET"
    ],
    [
      "arxiv",
      "cs.NE"
    ],
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "openalex",
      "2208|Advanced Memory and Neural Computing"
    ]
  ],
  "openalex": "W7220738962",
  "pdf_url": "https://arxiv.org/pdf/2610.06065",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8sztwtpn4f2pf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:11Z",
      "native_id": "2610.06065",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.06065"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "From communication to computation in neurons-on-a-chip: an in silico study of neurotopomorphic computing",
  "title_text": "From communication to computation in neurons-on-a-chip: an in silico study of neurotopomorphic computing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:11Z",
  "url": "https://research.draconitic.com/paper/8sztwtpn4f2pf",
  "venue": "arXiv",
  "versions": []
}