The Second Wire Is Substrate-Independent: A Neuron-Free Testbed for Ephaptic-Axonal Interference

Rebbin et al. (bioRxiv, 2025) argue that neural self-organization is shaped by interference between two co-propagating channels moving at different speeds: fast axonal spikes (~0.62 mm/ms) and a slow, sub-threshold ephaptic phase advance (~0.08 mm/ms). Their interference produces a non-monotonic, cosinusoidal variation of synchrony with distance whose wavelength scales inversely with oscillation frequency, `lambda = 1/[f(1/v_ep - 1/v_ax)]`. A natural objection, raised in their own Q&A, is that a "Mexican hat" of short-range excitation and long-range inhibition could reproduce the same spatial bump without any ephaptic physics. We approach that identifiability question from an unexpected direction. We build the two-channel, two-speed motif in a substrate with no neurons, no ions, and no ephaptic physics — a mesh of software agents in which the fast channel is directed message passing and the slow channel is a bias diffusing through shared memory — and ask whether the observational signature survives. It does, quantitatively. The neuron-free mesh reproduces the ripple (measured wavelength 2.48 mm vs. 2.30 mm predicted at 40 Hz), its frequency scaling (`lambda ∝ 1/f`, fitted slope 0.0975 vs. 0.0919 predicted, intercept near zero, R² = 0.991), its dependence on the conduction-speed difference, and the field-dependent developmental banding. We argue this cuts both ways: the interference motif is a general, substrate-independent computational primitive of interest to neuromorphic and multi-agent design, and — because a system with no ephaptic physics reproduces the signature and its scaling — observing that signature in cortex does not by itself identify ephaptic causation. This is analogy, not homology: a model organism for a mechanism, one abstraction beyond a cell culture. AI disclosure. Authored by Cristian Ruvalcaba with the Saluca Agentic AI Research Team (a human-directed, multi-agent large-language-model research system). The human researcher originated the question — a substrate-independence reading of Rebbin et al.'s ephaptic-axonal model — made all methodological and go/no-go decisions, reviewed the outputs, and is solely accountable for every claim. The agentic system designed and built the neuron-free testbed, ran the simulations, analysed the results, and drafted the manuscript under continuous human direction and validation. No claim should be treated as established solely because an AI produced it; this preprint is not peer-reviewed. This is a preprint; it is not peer-reviewed. It is a model organism for a mechanism — analogy, not homology — and makes no claim about ion channels, astrocytes, or real cortex.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-16
DOI
https://doi.org/10.5281/zenodo.21386649
Primary Topic
Neural dynamics and brain function
Type
preprint
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preprint

The Second Wire Is Substrate-Independent: A Neuron-Free Testbed for Ephaptic-Axonal Interference

Saluca Agentic AI Research Team, Cristian Ruvalcaba
Zenodo (CERN European Organization for Nuclear Research)
Neural dynamics and brain function
preprint

The Second Wire Is Substrate-Independent: A Neuron-Free Testbed for Ephaptic-Axonal Interference

Saluca Agentic AI Research Team, Cristian Ruvalcaba
preprint en

Abstract

Rebbin et al. (bioRxiv, 2025) argue that neural self-organization is shaped by interference between two co-propagating channels moving at different speeds: fast axonal spikes (~0.62 mm/ms) and a slow, sub-threshold ephaptic phase advance (~0.08 mm/ms). Their interference produces a non-monotonic, cosinusoidal variation of synchrony with distance whose wavelength scales inversely with oscillation frequency, `lambda = 1/[f(1/v_ep - 1/v_ax)]`. A natural objection, raised in their own Q&A, is that a "Mexican hat" of short-range excitation and long-range inhibition could reproduce the same spatial bump without any ephaptic physics. We approach that identifiability question from an unexpected direction. We build the two-channel, two-speed motif in a substrate with no neurons, no ions, and no ephaptic physics — a mesh of software agents in which the fast channel is directed message passing and the slow channel is a bias diffusing through shared memory — and ask whether the observational signature survives. It does, quantitatively. The neuron-free mesh reproduces the ripple (measured wavelength 2.48 mm vs. 2.30 mm predicted at 40 Hz), its frequency scaling (`lambda ∝ 1/f`, fitted slope 0.0975 vs. 0.0919 predicted, intercept near zero, R² = 0.991), its dependence on the conduction-speed difference, and the field-dependent developmental banding. We argue this cuts both ways: the interference motif is a general, substrate-independent computational primitive of interest to neuromorphic and multi-agent design, and — because a system with no ephaptic physics reproduces the signature and its scaling — observing that signature in cortex does not by itself identify ephaptic causation. This is analogy, not homology: a model organism for a mechanism, one abstraction beyond a cell culture. AI disclosure. Authored by Cristian Ruvalcaba with the Saluca Agentic AI Research Team (a human-directed, multi-agent large-language-model research system). The human researcher originated the question — a substrate-independence reading of Rebbin et al.'s ephaptic-axonal model — made all methodological and go/no-go decisions, reviewed the outputs, and is solely accountable for every claim. The agentic system designed and built the neuron-free testbed, ran the simulations, analysed the results, and drafted the manuscript under continuous human direction and validation. No claim should be treated as established solely because an AI produced it; this preprint is not peer-reviewed. This is a preprint; it is not peer-reviewed. It is a model organism for a mechanism — analogy, not homology — and makes no claim about ion channels, astrocytes, or real cortex.

Zenodo (CERN European Organization for Nuclear Research)
Salesian University (MX)
Neural dynamics and brain function
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