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For networks with fixed delays, we derive self-consistency conditions for phase-locked states and an associated linear stability theory formulated directly in terms of spike-time perturbations. The framework is illustrated for a delayed autapse, a reciprocally coupled two-cell network, and spatially structured rings with distance-dependent coupling and conduction delays, where circulant symmetry allows stability to be decomposed into Fourier modes. We then introduce an activity-dependent white matter plasticity rule in which myelination modulates axonal conduction speed and hence communication delay. This leads naturally to a slow\u2013fast system with state-dependent delays, in which frozen phase-locked branches organise the adaptive dynamics. The plasticity rule selects commensurate delay\u2013period relationships, providing a mechanism for the emergence of synchrony, other frequency-locked states, slow switching between competing phase-locked patterns, and the organisation of heterogeneous delays into discrete delay\u2013period classes. Direct simulations of the event-driven network support the analytical predictions and illustrate how adaptive conduction can reshape the attractor structure of a delayed spiking network and generate long-timescale transitions. These results provide a tractable mathematical framework for studying how activity-dependent myelination may regulate temporal coordination, synchrony, and communication through coherence in spiking neural systems.<\/jats:p>","DOI":"10.1007\/s00422-026-01064-2","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T19:17:28Z","timestamp":1790018248000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive conduction delays and phase locking in spiking Haken Lighthouse networks"],"prefix":"10.1007","volume":"120","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1610-7665","authenticated-orcid":false,"given":"Stephen","family":"Coombes","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R\u00fcdiger","family":"Thul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Ruschel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachel","family":"Nicks","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"1064_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.conb.2023.102818","volume":"84","author":"P Ashwin","year":"2024","unstructured":"Ashwin P, Fadera M, Postlethwaite C (2024) Network attractors and nonlinear dynamics of neural computation. 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