pith. sign in

arxiv: 2605.15211 · v1 · pith:AXMGWEF6new · submitted 2026-05-07 · ⚛️ physics.bio-ph

Modeling Optical Polarization Evolution in Myelinated Axon Waveguides with Realistic Imperfections

Ethan Davies , Rishabh , Christoph Simon This is my paper

Pith reviewed 2026-05-19 16:41 UTC · model grok-4.3

classification ⚛️ physics.bio-ph
keywords myelinated axonsoptical polarizationbiophotonic signalingpolarization fidelityaxon waveguidesmyelin thickness variationnon-circular cross-sectionaxonal bending
0
0 comments X

The pith

Realistic imperfections in myelinated axons reduce polarization fidelity but permit recoveries reaching 0.8 in certain modes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper examines how three common structural features of real axons affect the polarization state of light traveling along them, modeled as a waveguide with four nodes of Ranvier. The simulations test myelin thickness changes, non-circular cross sections, and axonal bending both separately and together. Myelin variation barely affects fidelity while bending produces the largest swings and deepest dips, yet the combined case still shows repeated recoveries to roughly 0.8 in selected modes. These recoveries exceed what appears in any single-imperfection run. The results therefore suggest that polarization could survive as a usable information channel even inside biologically realistic axons.

Core claim

Incorporating myelin thickness variation, non-circular cross-sections, and axonal bending into a four-node waveguide model produces substantial overall drops in polarization fidelity, but certain modes display repeated revivals that reach values around 0.8, higher than the revivals seen when each imperfection acts alone, indicating that polarization-based signals may remain recoverable in realistic myelinated axons.

What carries the argument

A computational waveguide model of a myelinated axon segment containing four nodes of Ranvier and the three structural imperfections of myelin thickness variation, non-circular cross-section, and axonal bending, used to track polarization evolution along the length.

If this is right

  • Polarization fidelity drops substantially when myelin variation, non-circular shape, and bending act together.
  • Selected modes still exhibit repeated fidelity revivals that reach approximately 0.8.
  • These combined revivals are larger than those produced by any one imperfection in isolation.
  • Myelin thickness variation alone has minimal effect while axonal bending exerts the strongest influence.
  • The pattern supports the possibility that polarization remains usable for biophotonic information transfer in real axons.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The unexpected improvement in recovery when imperfections are combined may point to an emergent stabilizing effect that single-defect studies miss.
  • Experimental work could search for the high-fidelity modes inside living tissue to test whether the modeled recoveries occur biologically.
  • Extending the same waveguide approach to longer segments with more nodes would show whether the revivals continue or decay with distance.
  • If recoverable, polarization would add an independent information channel that could operate alongside electrical signaling without requiring new cellular machinery.

Load-bearing premise

A four-node computational waveguide that includes only the three listed structural imperfections is sufficient to represent polarization behavior inside living myelinated axons.

What would settle it

Measurement of polarization fidelity along actual myelinated axons that shows no repeated recoveries above 0.5 under combined imperfections would contradict the modeled recoverability.

Figures

Figures reproduced from arXiv: 2605.15211 by Christoph Simon, Ethan Davies, Rishabh.

Figure 1
Figure 1. Figure 1: FIG. 1. Artistic diagram of axon incorporating three [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 1
Figure 1. Figure 1: To limit the computational cost, the models do not [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Myelin thickness variation used in simulations. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6. Polarization fidelity for control axon for the two in [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. Injected modes used for the circular and non-circular [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8. Polarization fidelity for the axon with a non-circular [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9. Polarization fidelity for the bent axon with tortuosity [PITH_FULL_IMAGE:figures/full_fig_p006_9.png] view at source ↗
read the original abstract

Biophotonic signaling via axons has been proposed as a potential mode of neural communication, where information might be encoded not only in photon number and wavelength but also in polarization. Although earlier computational studies have examined how structural imperfections influence optical transmission, their effects on polarization fidelity remain unexplored; previous modeling of polarization fidelity in myelinated axons has largely focused on idealized geometries. This study incorporates three structural imperfections characteristic of axons in vivo: variation in myelin thickness, non-circular cross-sectional geometry, and axonal bending, within a model that includes four nodes of Ranvier. We find that variation in myelin thickness alone has minimal impact on fidelity, while non-circular cross-sections show strong mode dependence. Axonal bending has the most significant influence, generating large fluctuations and deep fidelity dips. When all imperfections are combined in a single axon model, the simulations show substantial drops in fidelity, yet certain modes exhibit recovery, with repeated revivals reaching values of around 0.8, which exceeds the revivals observed in the single imperfection cases. Overall, the results indicate that although structural imperfections affect polarization, polarization-based biophotonic signals might remain recoverable even in realistic axons, lending support to the plausibility of polarization-based biophotonic signaling in the brain.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript models optical polarization evolution in myelinated axons as waveguides, incorporating three realistic imperfections (myelin thickness variation, non-circular cross-section, and axonal bending) in a setup with four nodes of Ranvier. Simulations show that myelin thickness variation has minimal impact, non-circular sections exhibit mode dependence, and bending causes large fluctuations and deep dips; when combined, fidelity drops substantially but certain modes display repeated revivals reaching ~0.8, exceeding single-imperfection cases, supporting potential recoverability of polarization-based biophotonic signals.

Significance. If the numerical results are reliable, the work strengthens the case for polarization as a viable information carrier in axonal biophotonics by demonstrating robustness to combined structural imperfections typical of in vivo axons. A strength is the forward simulation approach with no fitted parameters or self-referential definitions, yielding falsifiable predictions about fidelity revivals under realistic conditions.

major comments (2)
  1. [Model Setup] Model description (four nodes of Ranvier): the central claim of repeated fidelity revivals reaching ~0.8 under combined imperfections rests on a waveguide segment limited to four nodes. Over longer realistic axon lengths, additional phase accumulation from bending, thickness gradients, and ellipticity could induce further mode coupling and depolarization not captured here, potentially rendering the revivals an artifact of the truncated length rather than intrinsic recoverability.
  2. [Methods/Results] Results and Methods: concrete fidelity values are reported from simulations, yet no details are supplied on the electromagnetic solver, mesh resolution, boundary conditions, convergence checks, or validation against analytic cases (e.g., ideal cylindrical waveguides). This absence undermines verification of the reported outcomes and their mode-dependent behavior.
minor comments (1)
  1. [Figures] Figure captions and text could more explicitly label the polarization modes (e.g., HE11, TE01) and specify the exact fidelity metric definition to aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback. We address each major comment below and indicate planned revisions.

read point-by-point responses

  1. Referee: [Model Setup] Model description (four nodes of Ranvier): the central claim of repeated fidelity revivals reaching ~0.8 under combined imperfections rests on a waveguide segment limited to four nodes. Over longer realistic axon lengths, additional phase accumulation from bending, thickness gradients, and ellipticity could induce further mode coupling and depolarization not captured here, potentially rendering the revivals an artifact of the truncated length rather than intrinsic recoverability.

    Authors: We selected the four-node segment to observe multiple periods of imperfection effects while remaining computationally feasible. The revivals to ~0.8 under combined imperfections exceed those in single-imperfection cases, suggesting an intrinsic recovery mechanism. In revision we will add discussion acknowledging the segment-length limitation and outlining how the periodic revival pattern may extrapolate to longer axons. revision: partial

  2. Referee: [Methods/Results] Results and Methods: concrete fidelity values are reported from simulations, yet no details are supplied on the electromagnetic solver, mesh resolution, boundary conditions, convergence checks, or validation against analytic cases (e.g., ideal cylindrical waveguides). This absence undermines verification of the reported outcomes and their mode-dependent behavior.

    Authors: We agree that numerical details are required for verification. The revised manuscript will expand the Methods section to specify the electromagnetic solver, mesh resolution, boundary conditions, convergence criteria, and validation results against analytic solutions for ideal cylindrical waveguides, enabling reproduction of the mode-dependent fidelity behavior. revision: yes

Circularity Check

0 steps flagged

No circularity: forward simulation of polarization evolution with no fitted predictions or self-referential definitions

full rationale

The paper conducts a direct numerical simulation of optical polarization in a waveguide model of myelinated axons that includes four nodes of Ranvier plus three specified structural imperfections. All reported fidelity values and revival patterns are generated by propagating the electromagnetic fields through this fixed geometry; no parameters are tuned to the output fidelity data, no equations are defined in terms of the target results, and no load-bearing claims rest on self-citations. The derivation chain therefore consists entirely of independent computational steps whose outputs are not equivalent to the inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The modeling rests on standard waveguide assumptions for myelinated axons; no free parameters, invented entities, or ad-hoc axioms are explicitly introduced in the abstract.

axioms (1)
  • domain assumption Optical polarization evolution in myelinated axons can be accurately captured by a waveguide model that incorporates the listed geometric imperfections.
    This premise underlies the entire simulation approach described in the abstract.

pith-pipeline@v0.9.0 · 5749 in / 1348 out tokens · 65296 ms · 2026-05-19T16:41:08.120776+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

85 extracted references · 85 canonical work pages

  1. [1]

    McKilliam, Explanation, understanding, and the methodological problem in consciousness science, Syn- these205, 142 (2025)

    A. McKilliam, Explanation, understanding, and the methodological problem in consciousness science, Syn- these205, 142 (2025)

    work page 2025

  2. [2]

    C. Koch, M. Massimini, M. Boly, and G. Tononi, Neural correlates of consciousness: progress and problems, Nat. Rev. Neurosci.17, 307 (2016)

    work page 2016

  3. [3]

    N. P. Franks, General anaesthesia: from molecular tar- gets to neuronal pathways of sleep and arousal, Nat. Rev. Neurosci.9, 370 (2008). 8

    work page 2008

  4. [4]

    G. A. Mashour, Integrating the science of consciousness and anesthesia, Anesth. Analg.103, 975 (2006)

    work page 2006

  5. [5]

    Humeau and D

    Y. Humeau and D. Choquet, The next generation of ap- proaches to investigate the link between synaptic plastic- ity and learning, Nat. Neurosci.22, 1536 (2019)

    work page 2019

  6. [6]

    Machado, C

    S. Machado, C. E. Portella, J. G. Silva, B. Velasques, V. H. Bastos, M. Cunha, L. Basile, M. Cagy, R. A. Piedade, and P. Ribeiro, Learning and implicit mem- ory: mechanisms and neuroplasticity, Rev. Neurol.46, 543 (2008)

    work page 2008

  7. [7]

    J. J. Day and J. D. Sweatt, DNA methylation and mem- ory formation, Nat. Neurosci.13, 1319 (2010)

    work page 2010

  8. [8]

    Nadel, A

    L. Nadel, A. Hupbach, R. Gomez, and K. Newman- Smith, Memory formation, consolidation and transfor- mation, Neurosci. Biobehav. Rev.36, 1640 (2012)

    work page 2012

  9. [9]

    Tang and J

    R. Tang and J. Dai, Biophoton signal transmission and processing in the brain, J. Photochem. Photobiol. B139, 71 (2014)

    work page 2014

  10. [10]

    N. Liu, Z. Wang, and J. Dai, Intracellular simulated bio- photon stimulation and transsynaptic signal transmis- sion, Appl. Phys. Lett.121, 203701 (2022)

    work page 2022

  11. [11]

    Y. Sun, C. Wang, and J. Dai, Biophotons as neural com- munication signals demonstrated by in situ biophoton autography, Photochem. Photobiol. Sci.9, 315 (2010)

    work page 2010

  12. [12]

    Kumar, K

    S. Kumar, K. Boone, J. A. Tuszy´ nski, P. E. Barclay, and C. Simon, Possible existence of optical communication channels in the brain, Sci. Rep.6, 36508 (2016)

    work page 2016

  13. [13]

    Zarkeshian, S

    P. Zarkeshian, S. Kumar, J. Tuszynski, P. Barclay, and C. Simon, Are there optical communication channels in the brain?, Front. Biosci. (Landmark Ed.)23, 1407 (2018)

    work page 2018

  14. [14]

    H. Zeng, Y. Zhang, Y. Ma, and S. Li, Electromagnetic modeling and simulation of the biophoton propagation in myelinated axon waveguide, Appl. Opt.61, 4013 (2022)

    work page 2022

  15. [15]

    Frede, H

    E. Frede, H. Zadeh-Haghighi, and C. Simon, Optical po- larization evolution and transmission in multi-Ranvier- node axonal myelin-sheath waveguides, IEEE Trans. Mol. Biol. Multi-Scale Commun.10, 613 (2024)

    work page 2024

  16. [16]

    I. P. Antonov, A. V. Goroshkov, V. N. Kalyunov, I. V. Markhvida, A. S. Rubanov, and L. V. Tanin, Measure- ment of the radial distribution of the refractive index of the Schwann’s sheath and the axon of a myelinated nerve fiber in vivo, J. Appl. Spectrosc.39, 822 (1983)

    work page 1983

  17. [17]

    Maghoul, A

    A. Maghoul, A. Khaleghi, and I. Balasingham, Engineer- ing photonic transmission inside brain nerve fibers, IEEE Access9, 35399 (2021)

    work page 2021

  18. [18]

    Cifra and P

    M. Cifra and P. Posp´ ıˇ sil, Ultra-weak photon emission from biological samples: definition, mechanisms, prop- erties, detection and applications, J. Photochem. Photo- biol. B139, 2 (2014)

    work page 2014

  19. [19]

    Salari, V

    V. Salari, V. Seshan, L. Frankle, D. England, C. Simon, and D. Oblak, Imaging ultraweak photon emission from living and dead mice and from plants under stress, J. Phys. Chem. Lett.16, 4354 (2025)

    work page 2025

  20. [20]

    J. Du, T. Deng, B. Cao, Z. Wang, M. Yang, and J. Han, The application and trend of ultra-weak photon emis- sion in biology and medicine, Front. Chem.11, 1140128 (2023)

    work page 2023

  21. [21]

    Isojima, T

    Y. Isojima, T. Isoshima, K. Nagai, K. Kikuchi, and H. Nakagawa, Ultraweak biochemiluminescence detected from rat hippocampal slices, Neuroreport6, 658 (1995)

    work page 1995

  22. [22]

    Kobayashi, M

    M. Kobayashi, M. Takeda, T. Sato, Y. Yamazaki, K. Kaneko, K. Ito, H. Kato, and H. Inaba, In vivo imag- ing of spontaneous ultraweak photon emission from a rat’s brain correlated with cerebral energy metabolism and oxidative stress, Neurosci. Res.34, 103 (1999)

    work page 1999

  23. [23]

    Kataoka, Y

    Y. Kataoka, Y. Cui, A. Yamagata, M. Niigaki, T. Hi- rohata, N. Oishi, and Y. Watanabe, Activity-dependent neural tissue oxidation emits intrinsic ultraweak photons, Biochem. Biophys. Res. Commun.285, 1007 (2001)

    work page 2001

  24. [24]

    Tang and J

    R. Tang and J. Dai, Spatiotemporal imaging of glutamate-induced biophotonic activities and transmis- sion in neural circuits, PLoS One9, e85643 (2014)

    work page 2014

  25. [25]

    Simon, Can quantum physics help solve the hard prob- lem of consciousness?, J

    C. Simon, Can quantum physics help solve the hard prob- lem of consciousness?, J. Consciousness Stud.26, 204 (2019)

    work page 2019

  26. [26]

    K. X. Zhanget al., Violet-light suppression of thermoge- nesis by opsin 5 hypothalamic neurons, Nature585, 420 (2020)

    work page 2020

  27. [27]

    I. S. Buyanova and M. Arsalidou, Cerebral white mat- ter myelination and relations to age, gender, and cog- nition: a selective review, Front. Hum. Neurosci.15, 662031 (2021)

    work page 2021

  28. [28]

    J. Li, L. Zhang, Y. Chu, M. Namaka, B. Deng, J. Kong, and X. Bi, Astrocytes in oligodendrocyte lineage develop- ment and white matter pathology, Front. Cell. Neurosci. 10, 119 (2016)

    work page 2016

  29. [29]

    Sampaio-Baptista and H

    C. Sampaio-Baptista and H. Johansen-Berg, White mat- ter plasticity in the adult brain, Neuron96, 1239 (2017)

    work page 2017

  30. [30]

    H. Wang, J. Wang, G. Cai, Y. Liu, Y. Qu, and T. Wu, A physical perspective to the inductive function of myelin—a missing piece of neuroscience, Front. Neural Circuits14, 562005 (2021)

    work page 2021

  31. [31]

    Poitelon, A

    Y. Poitelon, A. M. Kopec, and S. Belin, Myelin fat facts: an overview of lipids and fatty acid metabolism, Cells9, 812 (2020)

    work page 2020

  32. [32]

    Q. Yu, T. Guan, Y. Guo, and J. Kong, The initial myeli- nation in the central nervous system, ASN Neuro15, 17590914231163039 (2023)

    work page 2023

  33. [33]

    Franze, J

    K. Franze, J. Grosche, S. N. Skatchkov, S. Schinkinger, C. Foja, D. Schild, O. Uckermann, K. Travis, A. Reichen- bach, and J. Guck, M¨ uller cells are living optical fibers in the vertebrate retina, Proc. Natl. Acad. Sci. U.S.A.104, 8287 (2007)

    work page 2007

  34. [34]

    A. M. Labin, S. K. Safuri, E. N. Ribak, and I. Perlman, M¨ uller cells separate between wavelengths to improve day vision with minimal effect upon night vision, Nat. Com- mun.5, 4319 (2014)

    work page 2014

  35. [35]

    E. Pini, D. Di Meo, I. Costantini, M. Sorelli, S. Bradley, D. S. Wiersma, F. S. Pavone, and L. Pattelli, Anisotropic light propagation in human brain white matter, Neu- rophotonics12, 045003 (2025)

    work page 2025

  36. [36]

    DePaoli, A

    D. DePaoli, A. Gasecka, M. Bahdine, J. M. Deschenes, L. Goetz, J. Perez-Sanchez, R. P. Bonin, Y. De Koninck, M. Parent, and D. C. Cˆ ot´ e, Anisotropic light scattering from myelinated axons in the spinal cord, Neurophotonics 7, 015011 (2020)

    work page 2020

  37. [37]

    Zarkeshian, T

    P. Zarkeshian, T. Kergan, R. Ghobadi, W. Nicola, and C. Simon, Photons guided by axons may enable backpropagation-based learning in the brain, Sci. Rep. 12, 20720 (2022)

    work page 2022

  38. [38]

    Liu, Y.-C

    Z. Liu, Y.-C. Chen, and P. Ao, Entangled biphoton gen- eration in the myelin sheath, Phys. Rev. E110, 024402 (2024)

    work page 2024

  39. [39]

    Omidi, M

    M. Omidi, M. I. Zibaii, and N. Granpayeh, Simulation of nerve fiber based on anti-resonant reflecting optical waveguide, Sci. Rep.12, 19356 (2022). 9

    work page 2022

  40. [40]

    O. M. Ostafiychuk, V. A. Es’kin, A. V. Kudrin, and A. A. Popova, Electromagnetic waves guided by a myelinated axon in the optical and infrared ranges, in2019 Pho- tonIcs & Electromagnetics Research Symposium – Spring (PIERS-Spring)(2019) p. 1180

    work page 2019

  41. [41]

    Zangari, D

    A. Zangari, D. Micheli, R. Galeazzi, and A. Tozzi, Node of Ranvier as an array of bio-nanoantennas for infrared communication in nerve tissue, Sci. Rep.8, 539 (2018)

    work page 2018

  42. [42]

    G. Liu, C. Chang, Z. Qiao, K. Wu, Z. Zhu, G. Cui, W. Peng, Y. Tang, J. Li, and C. Fan, Myelin sheath as a dielectric waveguide for signal propagation in the mid- infrared to terahertz spectral range, Adv. Funct. Mater. 29, 1807862 (2019)

    work page 2019

  43. [43]

    L. Guo, D. Xu, K. Wang, Y. Sun, Q. Zhang, H. Ning, C. Lu, S. Wang, and Y. Gong, Electromagnetic character- istics of in vivo nerve fibers at the terahertz–far-infrared band, Front. Bioeng. Biotechnol.10, 1055232 (2022)

    work page 2022

  44. [44]

    Nilsson, J

    M. Nilsson, J. L¨ att, F. St˚ ahlberg, D. van Westen, and H. Hagsl¨ att, The importance of axonal undulation in diffusion MR measurements: a Monte Carlo simulation study, NMR Biomed.25, 795 (2012)

    work page 2012

  45. [45]

    H. H. Lee, K. Yaros, J. Veraart, J. L. Pathan, F. X. Liang, S. G. Kim, D. S. Novikov, and E. Fieremans, Along- axon diameter variation and axonal orientation disper- sion revealed with 3D electron microscopy: implications for quantifying brain white matter microstructure with histology and diffusion MRI, Brain Struct. Funct.224, 1469 (2019)

    work page 2019

  46. [46]

    Abdollahzadeh, I

    A. Abdollahzadeh, I. Belevich, E. Jokitalo, A. Sierra, and J. Tohka, DeepACSON automated segmentation of white matter in 3D electron microscopy, Commun. Biol.4, 179 (2021)

    work page 2021

  47. [47]

    J. P. Fraher, Quantitative studies on the maturation of central and peripheral parts of individual ventral mo- toneuron axons. I. Myelin sheath and axon calibre, J. Anat.126, 509 (1978)

    work page 1978

  48. [48]

    P. M. Bartmeyer, N. P. Biscola, and L. A. Havton, A shape-adjusted ellipse approach corrects for varied axonal dispersion angles and myelination in primate nerve roots, Sci. Rep.11, 3150 (2021)

    work page 2021

  49. [49]

    P. Chen, L. Zhou, Z. Liu, and S. Liu, Measurement and analysis of optical transmission characteristics of the hu- man skull, J. Biophotonics18, e202400414 (2025)

    work page 2025

  50. [50]

    Z. Wang, I. S. Chun, X. Li, Z. Y. Ong, E. Pop, L. Mil- let, M. Gillette, and G. Popescu, Topography and refrac- tometry of nanostructures using spatial light interference microscopy, Opt. Lett.35, 208 (2010)

    work page 2010

  51. [51]

    V. V. Tuchin, I. L. Maksimova, D. A. Zimnyakov, I. L. Kon, A. H. Mavlyutov, and A. A. Mishin, Light prop- agation in tissues with controlled optical properties, J. Biomed. Opt.2, 401 (1997)

    work page 1997

  52. [52]

    R. L. van Veen, H. J. Sterenborg, A. Pifferi, A. Torri- celli, E. Chikoidze, and R. Cubeddu, Determination of visible near-ir absorption coefficients of mammalian fat using time- and spatially resolved diffuse reflectance and transmission spectroscopy, J. Biomed. Opt.10, 054004 (2005)

    work page 2005

  53. [53]

    Facci, P

    P. Facci, P. Cavatorta, L. Cristofolini, M. P. Fontana, A. Fasano, and P. Riccio, Kinetic and structural study of the interaction of myelin basic protein with dipalmi- toylphosphatidylglycerol layers, Biophys. J.78, 1413 (2000)

    work page 2000

  54. [54]

    A. N. Yaroslavsky, P. C. Schulze, I. V. Yaroslavsky, R. Schober, F. Ulrich, and H. J. Schwarzmaier, Optical properties of selected native and coagulated human brain tissues in vitro in the visible and near infrared spectral range, Phys. Med. Biol.47, 2059 (2002)

    work page 2059

  55. [55]

    W. F. Cheong, S. A. Prahl, and A. J. Welch, A review of the optical properties of biological tissues, IEEE J. Quantum Electron.26, 2166 (1990)

    work page 1990

  56. [56]

    I. L. Arancibia-C´ arcamo, M. C. Ford, L. Cossell, K. Ishida, K. Tohyama, and D. Attwell, Node of Ran- vier length as a potential regulator of myelinated axon conduction speed, eLife6, e23329 (2017)

    work page 2017

  57. [57]

    E. R. Kandel, J. H. Schwartz, and T. M. Jessell,Prin- ciples of Neural Science, 4th ed. (McGraw-Hill Health Professions Division, New York, 2000)

    work page 2000

  58. [58]

    Berman, K

    S. Berman, K. L. West, M. D. Does, J. D. Yeatman, and A. A. Mezer, Evaluating g-ratio weighted changes in the corpus callosum as a function of age and sex, NeuroImage 182, 304 (2018)

    work page 2018

  59. [59]

    Abdollahzadeh, I

    A. Abdollahzadeh, I. Belevich, E. Jokitalo, J. Tohka, and A. Sierra, Automated 3D axonal morphometry of white matter, Sci. Rep.9, 6084 (2019)

    work page 2019

  60. [60]

    Liewald, R

    D. Liewald, R. Miller, N. Logothetis, H. J. Wagner, and A. Sch¨ uz, Distribution of axon diameters in cortical white matter: an electron-microscopic study on three human brains and a macaque, Biol. Cybern.108, 541 (2014)

    work page 2014

  61. [61]

    H. M. Kjer, M. Andersson, Y. He, A. Pacureanu, A. Da- ducci, M. Pizzolato, T. Salditt, A.-L. Robisch, M. Ecker- mann, M. T¨ opperwien, A. B. Dahl, M. L. Elkjær, Z. Illes, M. Ptito, V. A. Dahl, and T. B. Dyrby, Bridging the 3D geometrical organisation of white matter pathways across anatomical length scales and species, eLife13, RP94917 (2025)

    work page 2025

  62. [62]

    Albrecht-Buehler, Cellular infrared detector appears to be contained in the centrosome, Cell Motil

    G. Albrecht-Buehler, Cellular infrared detector appears to be contained in the centrosome, Cell Motil. Cytoskele- ton27, 262 (1994)

    work page 1994

  63. [63]

    M. Kato, K. Shinzawa, and S. Yoshikawa, Cytochrome oxidase is a possible photoreceptor in mitochondria, Pho- tobiochem. Photobiophys.2, 263 (1981)

    work page 1981

  64. [64]

    A. I. Zhuravlev, O. P. Tsvylev, and S. M. Zubkova, Spontaneous endogenous ultraweak luminescence of rat liver mitochondria under normal metabolic conditions, Biofizika18, 1037 (1973)

    work page 1973

  65. [65]

    J. A. Tuszy´ nski and J. M. Dixon, Quantitative analysis of the frequency spectrum of the radiation emitted by cytochrome oxidase enzymes, Phys. Rev. E64, 051915 (2001)

    work page 2001

  66. [66]

    Rahnama, J

    M. Rahnama, J. A. Tuszynski, I. B´ okkon, M. Cifra, P. Sardar, and V. Salari, Emission of mitochondrial bio- photons and their effect on electrical activity of mem- brane via microtubules, J. Integr. Neurosci.10, 65 (2011)

    work page 2011

  67. [67]

    V. M. Mazhul’ and D. G. Shcherbin, Phosphorescence analysis of lipid peroxidation products in liposomes, Bio- physics44, 656 (1999)

    work page 1999

  68. [68]

    Stoler, A

    O. Stoler, A. Stavsky, Y. Khrapunsky, I. Melamed, G. Stutzmann, D. Gitler, I. Sekler, and I. Fleidervish, Frequency- and spike-timing-dependent mitochondrial Ca2+ signaling regulates the metabolic rate and synaptic efficacy in cortical neurons, eLife11, e74606 (2022)

    work page 2022

  69. [69]

    Caminiti, F

    R. Caminiti, F. Carducci, C. Piervincenzi, A. Battaglia- Mayer, G. Confalone, F. Visco-Comandini, P. Pantano, and G. M. Innocenti, Diameter, length, speed, and con- duction delay of callosal axons in macaque monkeys and humans: Comparing data from histology and magnetic resonance imaging diffusion tractography, J. Neurosci. 10 33, 14501 (2013)

    work page 2013

  70. [70]

    F. O. Schmitt and R. S. Bear, The ultrastructure of the nerve axon sheath, Biol. Rev.14, 27 (1939)

    work page 1939

  71. [71]

    F. O. Schmitt and R. S. Bear, The optical properties of vertebrate nerve axons as related to fiber size, J. Cell. Comp. Physiol.9, 261 (1937)

    work page 1937

  72. [72]

    Watson, N

    D. Watson, N. Hagen, J. Diver, P. Marchand, and M. Chachisvilis, Elastic light scattering from single cells: orientational dynamics in optical trap, Biophys. J.87, 1298 (2004)

    work page 2004

  73. [73]

    J. Kwon, M. Kim, H. Park, B. M. Kang, Y. Jo, J. H. Kim, O. James, S. H. Yun, S. G. Kim, M. Suh, and M. Choi, Label-free nanoscale optical metrology on myelinated ax- ons in vivo, Nat. Commun.8, 1832 (2017)

    work page 2017

  74. [74]

    P. D. Wade, J. Taylor, and P. Siekevitz, Mammalian cere- bral cortical tissue responds to low-intensity visible light, Proc. Natl. Acad. Sci. U.S.A.85, 9322 (1988)

    work page 1988

  75. [75]

    D. N. Leszkiewicz, K. Kandler, and E. Aizenman, En- hancement of NMDA receptor-mediated currents by light in rat neurones in vitro, J. Physiol.524, 365 (2000)

    work page 2000

  76. [76]

    Vandewalle, P

    G. Vandewalle, P. Maquet, and D.-J. Dijk, Light as a modulator of cognitive brain function, Trends Cogn. Sci. 13, 429 (2009)

    work page 2009

  77. [77]

    Starck, J

    T. Starck, J. Nissil¨ a, A. Aunio, A. Abou-Elseoud, J. Remes, J. Nikkinen, M. Timonen, T. Takala, O. Ter- vonen, and V. Kiviniemi, Stimulating brain tissue with bright light alters functional connectivity in brain at the resting state, World J. Neurosci.2, 81 (2012)

    work page 2012

  78. [78]

    Adams and F

    B. Adams and F. Petruccione, Quantum effects in the brain: A review, AVS Quantum Sci.2, 022901 (2020)

    work page 2020

  79. [79]

    M. P. A. Fisher, Quantum cognition: The possibility of processing with nuclear spins in the brain, Ann. Phys. 362, 593 (2015)

    work page 2015

  80. [80]

    A. K. Fedorov, N. Gisin, S. M. Beloussov, and A. I. Lvovsky, arXiv:2203.17181

    work page arXiv

Showing first 80 references.