Skip to main content
Log in

A new viewpoint and model of neural signal generation and transmission: Signal transmission on unmyelinated neurons

  • Research Article
  • Published:
Nano Research Aims and scope Submit manuscript

Abstract

We establish a preliminary model of neural signal generation and transmission based on our previous research, and use this model to study signal transmission on unmyelinated nerves. In our model, the characteristics of neural signals are studied both on a long-time and a short time scale. On the long-time scale, the model is consistent with the circuit model. On the short time scale, the neural system exhibits a THz and infrared electromagnetic oscillation but the energy envelope curve of the rapidly oscillating signal varies slowly. In addition, the numerical method is used to solve the equations of neural signal generation and transmission, and the effects of the temperature on signal transmission are studied. It is found that overly high and overly low temperatures are not conducive to the transmission of neural signals.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Liao, X. Q.; Song, W. T.; Zhang, X. Y.; Yan, C. Q.; Li, T. L.; Ren, H. L.; Liu, C. Z.; Wang, Y. T.; Zheng, Y. J. A bioinspired analogous nerve towards artificial intelligence. Nat. Commun. 2020, 11, 268.

    CAS  Google Scholar 

  2. Feiner, R.; Dvir, T. Tissue-electronics interfaces: From implantable devices to engineered tissues. Nat. Rev. Mater. 2018, 3, 17076.

    CAS  Google Scholar 

  3. Lozano, A. M.; Lipsman, N.; Bergman, H.; Brown, P.; Chabardes, S.; Chang, J. W.; Matthews, K.; McIntyre, C. C.; Schlaepfer, T. E.; Schulder, M. et al. Deep brain stimulation: Current challenges and future directions. Nat. Rev. Neurol. 2019, 15, 148–160.

    Google Scholar 

  4. Deisseroth, K. Optogenetics: 10 years of microbial opsins in neuroscience. Nat. Neurosci. 2015, 18, 1213–1225.

    CAS  Google Scholar 

  5. Shapiro, M. G.; Homma, K.; Villarreal, S.; Richter, C. P.; Bezanilla, F. Infrared light excites cells by changing their electrical capacitance. Nat. Commun. 2012, 3, 736.

    Google Scholar 

  6. Ghahramani, Z. Probabilistic machine learning and artificial intelligence. Nature 2015, 521, 452–459.

    CAS  Google Scholar 

  7. Xue, D. Z.; Balachandran, P. V.; Hogden, J.; Theiler, J.; Xue, D. Q.; Lookman, T. Accelerated search for materials with targeted properties by adaptive design. Nat. Commun. 2016, 7, 11241.

    CAS  Google Scholar 

  8. Moravčík, M.; Schmid, M.; Burch, N.; Lisý, V.; Morrill, D.; Bard, N.; Davis, T.; Waugh, K.; Johanson, M.; Bowling, M. DeepStack: Expert-level artificial intelligence in heads-up no-limit poker. Science 2017, 356, 508–513.

    Google Scholar 

  9. Vöröslakos, M.; Takeuchi, Y.; Brinyiczki, K.; Zombori, T.; Oliva, A.; Fernández-Ruiz, A.; Kozák, G.; Kincses, Z. T.; Iványi, B.; Buzsáki, G. et al. Direct effects of transcranial electric stimulation on brain circuits in rats and humans. Nat. Commun. 2018, 9, 483.

    Google Scholar 

  10. Rolls, E. T.; Loh, M.; Deco, G.; Winterer, G. Computational models of schizophrenia and dopamine modulation in the prefrontal cortex. Nat. Rev. Neurosci. 2008, 9, 696–709.

    CAS  Google Scholar 

  11. Hodgkin, A. L.; Huxley, A. F. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 1952, 117, 500–544.

    CAS  Google Scholar 

  12. Hodgkin, A. L.; Huxley, A. F. Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. J. Physiol. 1952, 116, 449–472.

    CAS  Google Scholar 

  13. Hodgkin, A. L.; Huxley, A. F. The dual effect of membrane potential on sodium conductance in the giant axon of Loligo. J. Physiol. 1952, 116, 497–506.

    CAS  Google Scholar 

  14. Hodgkin, A. L.; Huxley, A. F.; Katz, B. Measurement of current-voltage relations in the membrane of the giant axon of Loligo. J. Physiol. 1952, 116, 424–448.

    CAS  Google Scholar 

  15. Kumar, S.; Boone, K.; Tuszyński, J.; Barclay, P.; Simon, C. Possible existence of optical communication channels in the brain. Sci. Rep. 2016, 6, 36508.

    CAS  Google Scholar 

  16. Hameroff, S.; Penrose, R. Consciousness in the universe: A review of the ‘Orch OR’ theory. Phys. Life Rev. 2014, 11, 39–78.

    Google Scholar 

  17. Zhang, X. Q.; Jiang, L. Quantum-confined ion superfluid in nerve signal transmission. Nano Res. 2019, 12, 1219–1221.

    CAS  Google Scholar 

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

    Google Scholar 

  19. Liu, G. Z. The conjectures on physical mechanism of vertebrate nervous system. Chin. Sci. Bull. 2018, 63, 3864–3865.

    Google Scholar 

  20. Xiang, Z. X.; Tang, C. X.; Chang, C.; Liu, G. Z. A primary model of THz and far-infrared signal generation and conduction in neuron systems based on the hypothesis of the ordered phase of water molecules on the neuron surface I: Signal characteristics. Sci. Bull. 2020, 65, 308–317.

    CAS  Google Scholar 

  21. Liu, Y. S.; Wu, K. J.; Liu, C. L.; Cui, G. Q.; Chang, C.; Liu, G. Z. Amplification of terahertz/infrared field at the nodes of Ranvier for myelinated nerve. Sci. China Phys. Mech. Astron. 2020, 63, 274211.

    Google Scholar 

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

    Google Scholar 

  23. Allen, T. W.; Andersen, O. S.; Roux, B. Ion permeation through a narrow channel: Using gramicidin to ascertain all-atom molecular dynamics potential of mean force methodology and biomolecular force fields. Biophys. J. 2006, 90, 3447–3468.

    CAS  Google Scholar 

  24. Maffeo, C.; Bhattacharya, S.; Yoo, J.; Wells, D.; Aksimentiev, A. Modeling and simulation of ion channels. Chem. Rev. 2012, 112, 6250–6284.

    CAS  Google Scholar 

  25. Roux, B. Theoretical and computational models of ion channels. Curr. Opin. Struct. Biol. 2002, 12, 182–189.

    CAS  Google Scholar 

  26. Zhang, Z.; Huang, X.; Qian, Y.; Chen, W.; Wen, L.; Jiang, L. Engineering smart nanofluidic systems for artificial ion channels and ion pumps: From single-pore to multichannel membranes. Adv. Mater. 2020, 32, 1904351.

    CAS  Google Scholar 

  27. Zhu, Z. P.; Wang, D. Y.; Tian, Y.; Jiang, L. Ion/molecule transportation in nanopores and nanochannels: From critical principles to diverse functions. J. Am. Chem. Soc. 2019, 141, 8658–8669.

    CAS  Google Scholar 

  28. Xiao, K.; Tu, B.; Chen, L.; Heil, T.; Wen, L. P.; Jiang, L.; Antonietti, M. Photo-driven ion transport for a photodetector based on an asymmetric carbon nitride nanotube membrane. Angew. Chem., Int. Ed. 2019, 58, 12574–12579.

    CAS  Google Scholar 

  29. Lu, D. Y.; Li, Y.; Ravaioli, U.; Schulten, K. Ion-nanotube terahertz oscillator. Phys. Rev. Lett. 2005, 95, 246801.

    Google Scholar 

  30. Weinreb, G. E.; Magura, I. S. Physical and molecular basis of ion channel gating: Can electrostatic interactions close the ion channel? Neurophysiology 1998, 30, 325–327.

    Google Scholar 

  31. Yu, Z.; McKnight, T. E.; Ericson, M. N.; Melechko, A. V.; Simpson, M. L.; Morrison, B. Vertically aligned carbon nanofiber as nano-neuron interface for monitoring neural function. Nanomedicine 2012, 8, 419–423.

    CAS  Google Scholar 

  32. Wang, Y. C.; Zhu, H. L.; Yang, H. R.; Argall, A. D.; Luan, L.; Xie, C.; Guo, L. Nano functional neural interfaces. Nano Res. 2018, 11, 5065–5106.

    Google Scholar 

  33. Yu, Z.; McKnight, T. E.; Ericson, M. N.; Melechko, A. V.; Simpson, M. L.; Morrison, B. Vertically aligned carbon nanofiber arrays record electrophysiological signals from hippocampal slices. Nano Lett. 2007, 7, 2188–2195.

    CAS  Google Scholar 

  34. Li, W. Q.; Liao, L.; Xiao, X. H.; Zhao, X. Y.; Dai, Z. G.; Guo, S. S.; Wu, W.; Shi, Y.; Xu, J. X.; Ren, F. et al. Modulating the threshold voltage of oxide nanowire field-effect transistors by a Ga+ ion beam. Nano Res. 2014, 7, 1691–1698.

    CAS  Google Scholar 

  35. Paoletti, P.; Ellis-Davies, G. C. R.; Mourot, A. Optical control of neuronal ion channels and receptors. Nat. Rev. Neurosci. 2019, 20, 514–532.

    CAS  Google Scholar 

  36. Gordon, D.; Chung, S. H. Permeation and block of the Kv1.2 channel examined using Brownian and molecular dynamics. Biophys. J. 2011, 101, 2671–2678.

    CAS  Google Scholar 

  37. Baştuğ, T.; Kuyucak, S. Comparative study of the energetics of ion permeation in Kv1.2 and KcsA potassium channels. Biophys. J. 2011, 100, 629–636.

    Google Scholar 

  38. Tang, P.; Xu, Y. Large-scale molecular dynamics simulations of general anesthetic effects on the ion channel in the fully hydrated membrane: The implication of molecular mechanisms of general anesthesia. Proc. Natl. Acad. Sci. USA 2002, 99, 16035–16040.

    CAS  Google Scholar 

  39. Saunders, M. G.; Voth, G. A. Coarse-graining methods for computational biology. Annu. Rev. Biophys. 2013, 42, 73–93.

    CAS  Google Scholar 

  40. Shi, Q.; Izvekov, S.; Voth, G. A. Mixed atomistic and coarse-grained molecular dynamics: Simulation of a membrane-bound ion channel. J. Phys. Chem. B 2006, 110, 15045–15048.

    CAS  Google Scholar 

  41. Summhammer, J.; Salari, V.; Bernroider, G. A quantum-mechanical description of ion motion within the confining potentials of voltage-gated ion channels. J. Integr. Neurosci. 2012, 11, 123–135.

    Google Scholar 

  42. Ganim, Z.; Tokmakoff, A.; Vaziri, A. Vibrational excitons in ionophores: Experimental probes for quantum coherence-assisted ion transport and selectivity in ion channels. New J. Phys. 2011, 13, 113030.

    Google Scholar 

  43. Bhattacharya, S.; Roy, S. Quantum thermodynamics and coherence in ion channels. In International Conference on Applied Physics, System Science and Computers. Springer: Cham, 2018; pp 3–11.

    Google Scholar 

  44. Roy, S.; Mitra, I.; Llinas, R. Non-Markovian noise mediated through anomalous diffusion within ion channels. Phys. Rev. E 2008, 78, 041920.

    Google Scholar 

  45. Vaziri, A.; Plenio, M. B. Quantum coherence in ion channels: Resonances, transport and verification. New J. Phys. 2010, 12, 085001.

    Google Scholar 

  46. Noskov, S. Y.; Im, W.; Roux, B. Ion permeation through the α-hemolysin channel: Theoretical studies based on Brownian dynamics and Poisson-Nernst-Plank electrodiffusion theory. Biophys. J. 2004, 87, 2299–2309.

    CAS  Google Scholar 

  47. Im, W.; Seefeld, S.; Roux, B. A grand canonical Monte Carlo-Brownian dynamics algorithm for simulating ion channels. Biophys. J. 2000, 79, 788–801.

    CAS  Google Scholar 

  48. Salari, V.; Sajadi, M.; Bassereh, H.; Rezania, V.; Alaei, M.; Tuszynski, J. A. On the classical vibrational coherence of carbonyl groups in the selectivity filter backbone of the KcsA ion channel. J. Integr. Neurosci. 2015, 14, 195–206.

    CAS  Google Scholar 

  49. Shrivastava, I. H.; Sansom, M. S. P. Simulations of ion permeation through a potassium channel: Molecular dynamics of KcsA in a phospholipid bilayer. Biophys. J. 2000, 78, 557–570.

    CAS  Google Scholar 

  50. Sajadi, M.; Lohrasebi, A.; Rafii-Tabar, H. Modelling the effect of a GHz electric field on the dynamics of K+ ions in KcsA potassium channel. Mol. Simul. 2014, 40, 399–407.

    CAS  Google Scholar 

  51. Eisenberg, B. Multiple scales in the simulation of ion channels and proteins. J. Phys. Chem. C 2010, 114, 20719–20733.

    CAS  Google Scholar 

  52. Zhong, Q. F.; Jiang, Q.; Moore, P. B.; Newns, D. M.; Klein, M. L. Molecular dynamics simulation of a synthetic ion channel. Biophys. J. 1998, 74, 3–10.

    CAS  Google Scholar 

  53. Breed, J.; Sankararamakrishnan, R.; Kerr, I. D.; Sansom, M. S. Molecular dynamics simulations of water within models of ion channels. Biophys. J. 1996, 70, 1643–1661.

    CAS  Google Scholar 

  54. Biggin, P. C.; Smith, G. R.; Shrivastava, I.; Choe, S.; Sansom, M. S. P. Potassium and sodium ions in a potassium channel studied by molecular dynamics simulations. Biochim. Biophys. Acta 2001, 1510, 1–9.

    CAS  Google Scholar 

  55. Horbach, J.; Kob, W.; Binder, K. Dynamics of sodium in sodium disilicate: Channel relaxation and sodium diffusion. Phys. Rev. Lett. 2002, 88, 125502.

    Google Scholar 

  56. Bruce, C. D.; Senapati, S.; Berkowitz, M. L.; Perera, L.; Forbes, M. D. E. Molecular dynamics simulations of sodium dodecyl sulfate micelle in water: The behavior of water. J. Phys. Chem. B 2002, 106, 10902–10907.

    CAS  Google Scholar 

  57. Kutzner, C.; Grubmüller, H.; De Groot, B. L.; Zachariae, U. Computational electrophysiology: The molecular dynamics of ion channel permeation and selectivity in atomistic detail. Biophys. J. 2011, 101, 809–817.

    CAS  Google Scholar 

  58. Kutzner, C.; Köpfer, D. A.; Machtens, J. P.; de Groot, B. L.; Song, C.; Zachariae, U. Insights into the function of ion channels by computational electrophysiology simulations. Biochim. Biophys. Acta 2016, 1858, 1741–1752.

    CAS  Google Scholar 

  59. Bernèche, S.; Roux, B. Molecular dynamics of the KcsA K+ channel in a bilayer membrane. Biophys. J. 2000, 78, 2900–2917.

    Google Scholar 

  60. Conti, F.; De Felice, L. J.; Wanke, E. Potassium and sodium ion current noise in the membrane of the squid giant axon. J. Physiol. 1975, 248, 45–82.

    CAS  Google Scholar 

  61. Longtin, A. Stochastic resonance in neuron models. J. Stat. Phys. 1993, 70, 309–327.

    Google Scholar 

  62. Gluckman, B. J.; Netoff, T. I.; Neel, E. J.; Ditto, W. L.; Spano, M. L.; Schiff, S. J. Stochastic resonance in a neuronal network from mammalian brain. Phys. Rev. Lett. 1996, 77, 4098–4101.

    CAS  Google Scholar 

  63. Hodgkin, A. L. The ionic basis of electrical activity in nerve and muscle. Biol. Rev. 1951, 26, 339–409.

    CAS  Google Scholar 

  64. Hodgkin, A. L.; Huxley, A. F. The components of membrane conductance in the giant axon of Loligo. J. Physiol. 1952, 116, 473–496.

    CAS  Google Scholar 

  65. Debye, P. Reaction rates in ionic solutions. Trans. Electrochem. Soc. 1942, 82, 265–272.

    Google Scholar 

  66. Jiang, J. H.; Wu, D. L. Ice and water permittivities for millimeter and sub-millimeter remote sensing applications. Atmos. Sci. Lett. 2004, 5, 146–151.

    Google Scholar 

  67. De Col, R.; Messlinger, K.; Carr, R. W. Conduction velocity is regulated by sodium channel inactivation in unmyelinated axons innervating the rat cranial meninges. J. Physiol. 2008, 586, 1089–1103.

    CAS  Google Scholar 

  68. Chapman, R. A. Dependence on temperature of the conduction velocity of the action potential of the squid giant axon. Nature 1967, 213, 1143–1144.

    CAS  Google Scholar 

  69. Rosenthal, J. J.; Bezanilla, F. Seasonal variation in conduction velocity of action potentials in squid giant axons. Biol. Bull. 2000, 199, 135–143.

    CAS  Google Scholar 

  70. Lowitzsch, K.; Hopf, H. C.; Galland, J. Changes of sensory conduction velocity and refractory periods with decreasing tissue temperature in man. J. Neurol. 1977, 216, 181–188.

    CAS  Google Scholar 

  71. Waxman, S. G.; Bennett, M. V. L. Relative conduction velocities of small myelinated and non-myelinated fibres in the central nervous system. Nat. New Biol. 1972, 238, 217–219.

    CAS  Google Scholar 

  72. Hursh, J. B. Conduction velocity and diameter of nerve fibers. Am. J. Physiol. 1939, 127, 131–139.

    Google Scholar 

  73. Ritchie, J. M. On the relation between fibre diameter and conduction velocity in myelinated nerve fibres. Proc. R. Soc. Lond. B 1982, 217, 29–35.

    CAS  Google Scholar 

  74. McIntyre, C. C.; Richardson, A. G.; Grill, W. M. Modeling the excitability of mammalian nerve fibers: Influence of afterpotentials on the recovery cycle. J. Neurophysiol. 2002, 87, 995–1006.

    Google Scholar 

  75. Dominguez, G.; Fozzard, H. A. Influence of extracellular K+ concentration on cable properties and excitability of sheep cardiac Purkinje fibers. Circ. Res. 1970, 26, 565–574.

    CAS  Google Scholar 

  76. Tsuboi, N.; Kodama, I.; Toyama, J.; Yamada, K. Anisotropic conduction properties of canine ventricular muscles: Influence of high extracellular K+ concentration and stimulation frequency. Jpn. Circ. J. 1985, 49, 487–498.

    CAS  Google Scholar 

  77. Waxman, S. G. Determinants of conduction velocity in myelinated nerve fibers. Muscle Nerve 1980, 3, 141–150.

    CAS  Google Scholar 

  78. Sivasubramanian S.; Widom A.; Srivastava Y. N. The Clausius-Mossotti phase transition in polar liquids. Physica A. 2005, 345(3–4), 356–366.

    Google Scholar 

  79. Cai, P. Q.; Leow, W. R.; Wang, X. Y.; Wu, Y. L.; Chen, X. D. Programmable nano-bio interfaces for functional biointegrated devices. Adv. Mater. 2017, 29, 1605529.

    Google Scholar 

  80. Xie, C.; Hanson, L.; Xie, W. J.; Lin, Z. L.; Cui, B. X.; Cui, Y. Noninvasive neuron pinning with nanopillar arrays. Nano Lett. 2010, 10, 4020–4024.

    CAS  Google Scholar 

  81. Zhu, W.; O’Brien, C.; O’Brien, J. R.; Zhang, L. G. 3D nano/microfabrication techniques and nanobiomaterials for neural tissue regeneration. Nanomedicine 2014, 9, 859–875.

    CAS  Google Scholar 

  82. Wang, K.; Fishman, H. A.; Dai, H. J.; Harris, J. S. Neural stimulation with a carbon nanotube microelectrode array. Nano Lett. 2006, 6, 2043–2048.

    CAS  Google Scholar 

  83. Eom, K.; Kim, J.; Choi, J. M.; Kang, T.; Chang, J. W.; Byun, K. M.; Jun, S. B.; Kim, S. J. Enhanced infrared neural stimulation using localized surface plasmon resonance of gold nanorods. Small 2014, 10, 3853–3857.

    CAS  Google Scholar 

  84. Li, J.; Andrews, R. J. Trimodal nanoelectrode array for precise deep brain stimulation: Prospects of a new technology based on carbon nanofiber arrays. Acta Neurochir. Suppl. 2007, 97, 537–545.

    CAS  Google Scholar 

  85. Song, B.; Shu, Y. S. Cell vibron polariton resonantly self-confined in the myelin sheath of nerve. Nano Res. 2020, 13, 38–44.

    CAS  Google Scholar 

  86. Miller, E. W.; Lin, J. Y.; Frady, E. P.; Steinbach, P. A.; Kristan, W. B.; Tsien, R. Y. Optically monitoring voltage in neurons by photo-induced electron transfer through molecular wires. Proc. Natl. Acad. Sci. USA 2012, 109, 2114–2119.

    CAS  Google Scholar 

  87. Zou, Y.; Liu, Q.; Yang, X.; Huang, H. C.; Li, J.; Du, L. H.; Li, Z. R.; Zhao J. H.; Zhu, L. G. Label-free monitoring of cell death induced by oxidative stress in living human cells using terahertz ATR spectroscopy, Biomed. Opt. Express 2018, 9, 14–24.

    CAS  Google Scholar 

Download references

Acknowledgements

The authors thank professor Wang Qing from Tsinghua University for helpful discussions. This work was supported in part by the National Defense Technology Innovation Special Zone and the National Natural Science Foundation of China (Nos. 51677145 and 11622542).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Chao Chang or Guozhi Liu.

Additional information

Author contributions

Professor Guozhi Liu provided the main ideas. Z. X. X. conducted a preliminary physical model and wrote the paper. G. Z. L., C. X. T. and C. C. provided guidance for the modeling process and the revision of the paper.

Competing financial interests

The authors declare no competing interests as defined by Research, or other interests that might be perceived to influence the results and/or discussion reported in this paper.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiang, Z., Tang, C., Chang, C. et al. A new viewpoint and model of neural signal generation and transmission: Signal transmission on unmyelinated neurons. Nano Res. 14, 590–600 (2021). https://doi.org/10.1007/s12274-020-3016-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12274-020-3016-1

Keywords

Navigation