{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T06:18:26Z","timestamp":1783491506210,"version":"3.55.0"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:p>\n                    Computational neuron models commonly reduce subthreshold membrane dynamics to a leaky RC integrator, confining computation to the spike and treating the inter-spike trajectory as passive decay. Yet impedance measurements and channel-specific analyses show that many excitable membranes have band-pass, inductance-like impedance profiles with a tuneable resonant peak \u2014 dynamics a first-order RC model cannot represent. This paper develops a spike-as-perturbation framework in which spikes act as impulse perturbations that launch regime-dependent transient trajectories in an equivalent parallel RLC membrane. We define the biological grounding and domain of validity of the reduction, show that post-perturbation RLC ringdowns carry circuit-identity and perturbation-timing state variables not available to a single matched first-order RC element without added delays, recurrence, or extra state variables, and demonstrate a concrete primitive \u2014 phase-based temporal discrimination \u2014 together with a spike-timing readout that makes it network-visible. A closed-form comparison (\n                    <jats:italic>Q<\/jats:italic>\n                    = 2, \u03c4RC = 25 ms, f\n                    <jats:sub>0<\/jats:sub>\n                    = 10 Hz) gives an RLC sensitivity half-life of 44 ms versus 17 ms for a matched RC decay. We identify the quality factor Q \u2014 strongly shaped by I\n                    <jats:sub>h<\/jats:sub>\n                    and related slow conductances \u2014 as a candidate neuromodulatory control variable that fixes the membrane pole radius and hence its transient (short-term) memory horizon, the biological analogue of selectivity in machine state-space models. We do not claim a universal\n                    <jats:italic>in vivo<\/jats:italic>\n                    phase code: a simulation under high-conductance bombardment shows that the readout is reliable in quiescent, high input-resistance states and is suppressed as conductance loading drives the effective Q below the underdamped threshold. We derive four falsifiable predictions spanning cellular, network, decoding, and population levels, and present the argument in three tiers of decreasing evidential support.\n                  <\/jats:p>","DOI":"10.3389\/fncom.2026.1846697","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:55:16Z","timestamp":1783490116000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Spikes as perturbations of resonant neural circuits: an RLC framework with testable predictions"],"prefix":"10.3389","volume":"20","author":[{"given":"Jeremy","family":"Sender","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Technology, La Trobe University","place":["Melbourne, VIC, Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Ping Phoebe","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, La Trobe University","place":["Melbourne, VIC, Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2510.14386","article-title":"SHaRe-SSM: Spiking harmonic resonate and fire state space model.","author":"Agrawal","year":"2025","journal-title":"arXiv"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2406.02923","article-title":"Rethinking spiking neural networks as state space models.","author":"Bal","year":"2024","journal-title":"arXiv"},{"key":"B3","doi-asserted-by":"publisher","first-page":"111541","DOI":"10.1016\/j.brainresbull.2025.111541","article-title":"Biophysically constrained dynamical modelling of the brain using multimodal magnetic resonance imaging.","volume":"231","author":"Bansal","year":"2025","journal-title":"Brain Res. 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