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Nano Research 2021, 14(3): 590-600 https://doi.org/10.1007/s12274-020-3016-1
Research Article | Issue | Published: 01 March 2021

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

Show Author's Information Zuoxian Xiang1,2,  Chuanxiang Tang2,  Chao Chang1( ),  Guozhi Liu2( )
Innovation Laboratory of Terahertz Biophysics, National Innovation Institute of Defense Technology, Beijing 100071, China
Department of Engineering Physics, Tsinghua University, Beijing 100084, China
Keywords:
THz, ion channel, neuron signal generation and conduction, unmyelinated neuron
Topics:
Neurons
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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 Research, 2021, 14(3): 590-600. https://doi.org/10.1007/s12274-020-3016-1
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Abstract Full text Electronic supplementary material About this article

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.


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About this article

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

Show Author's information Zuoxian Xiang1,2, Chuanxiang Tang2, Chao Chang1( ), Guozhi Liu2( )
Innovation Laboratory of Terahertz Biophysics, National Innovation Institute of Defense Technology, Beijing 100071, China
Department of Engineering Physics, Tsinghua University, Beijing 100084, China

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.

Keywords: THz, ion channel, neuron signal generation and conduction, unmyelinated neuron

References(87)

[1]
X. Q. Liao,; W. T. Song,; X. Y. Zhang,; C. Q. Yan,; T. L. Li,; H. L. Ren,; C. Z. Liu,; Y. T. Wang,; Y. J. Zheng, A bioinspired analogous nerve towards artificial intelligence. Nat. Commun. 2020, 11, 268.
DOI Google Scholar
[2]
R. Feiner,; T. Dvir, Tissue-electronics interfaces: From implantable devices to engineered tissues. Nat. Rev. Mater. 2018, 3, 17076.
DOI Google Scholar
[3]
A. M. Lozano,; N. Lipsman,; H. Bergman,; P. Brown,; S. Chabardes,; J. W. Chang,; K. Matthews,; C. C. McIntyre,; T. E. Schlaepfer,; M. Schulder, et al. Deep brain stimulation: Current challenges and future directions. Nat. Rev. Neurol. 2019, 15, 148-160.
DOI Google Scholar
[4]
K. Deisseroth, Optogenetics: 10 years of microbial opsins in neuroscience. Nat. Neurosci. 2015, 18, 1213-1225.
DOI Google Scholar
[5]
M. G. Shapiro,; K. Homma,; S. Villarreal,; C. P. Richter,; F. Bezanilla, Infrared light excites cells by changing their electrical capacitance. Nat. Commun. 2012, 3, 736.
DOI Google Scholar
[6]
Z. Ghahramani, Probabilistic machine learning and artificial intelligence. Nature 2015, 521, 452-459.
DOI Google Scholar
[7]
D. Z. Xue,; P. V. Balachandran,; J. Hogden,; J. Theiler,; D. Q. Xue,; T. Lookman, Accelerated search for materials with targeted properties by adaptive design. Nat. Commun. 2016, 7, 11241.
DOI Google Scholar
[8]
M. Moravčík,; M. Schmid,; N. Burch,; V. Lisý,; D. Morrill,; N. Bard,; T. Davis,; K. Waugh,; M. Johanson,; M. Bowling, DeepStack: Expert-level artificial intelligence in heads-up no-limit poker. Science 2017, 356, 508-513.
DOI Google Scholar
[9]
M. Vöröslakos,; Y. Takeuchi,; K. Brinyiczki,; T. Zombori,; A. Oliva,; A. Fernández-Ruiz,; G. Kozák,; Z. T. Kincses,; B. Iványi,; G. Buzsáki, et al. Direct effects of transcranial electric stimulation on brain circuits in rats and humans. Nat. Commun. 2018, 9, 483.
DOI Google Scholar
[10]
E. T. Rolls,; M. Loh,; G. Deco,; G. Winterer, Computational models of schizophrenia and dopamine modulation in the prefrontal cortex. Nat. Rev. Neurosci. 2008, 9, 696-709.
DOI Google Scholar
[11]
A. L. Hodgkin,; A. F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 1952, 117, 500-544.
Google Scholar
[12]
A. L. Hodgkin,; A. F. Huxley, Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. J. Physiol. 1952, 116, 449-472.
Google Scholar
[13]
A. L. Hodgkin,; A. F. Huxley, The dual effect of membrane potential on sodium conductance in the giant axon of Loligo. J. Physiol. 1952, 116, 497-506.
Google Scholar
[14]
A. L. Hodgkin,; A. F. Huxley,; B. Katz, Measurement of current- voltage relations in the membrane of the giant axon of Loligo. J. Physiol. 1952, 116, 424-448.
Google Scholar
[15]
S. Kumar,; K. Boone,; J. Tuszyński,; P. Barclay,; C. Simon, Possible existence of optical communication channels in the brain. Sci. Rep. 2016, 6, 36508.
Google Scholar
[16]
S. Hameroff,; R. Penrose, Consciousness in the universe: A review of the ‘Orch OR’ theory. Phys. Life Rev. 2014, 11, 39-78.
Google Scholar
[17]
X. Q. Zhang,; L. Jiang, Quantum-confined ion superfluid in nerve signal transmission. Nano Res. 2019, 12, 1219-1221.
Google Scholar
[18]
A. Zangari,; D. Micheli,; R. Galeazzi,; A. Tozzi, Node of Ranvier as an array of bio-nanoantennas for infrared communication in nerve tissue. Sci. Rep. 2018, 8, 539.
Google Scholar
[19]
G. Z. Liu, The conjectures on physical mechanism of vertebrate nervous system. Chin. Sci. Bull. 2018, 63, 3864-3865.
Google Scholar
[20]
Z. X. Xiang,; C. X. Tang,; C. Chang,; G. Z. Liu, 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.
Google Scholar
[21]
Y. S. Liu,; K. J. Wu,; C. L. Liu,; G. Q. Cui,; C. Chang,; G. Z. Liu, Amplification of terahertz/infrared field at the nodes of Ranvier for myelinated nerve. Sci. China Phys. Mech. Astron. 2020, 63, 274211.
Google Scholar
[22]
G. Z. Liu,; C. Chang,; Z. Qiao,; K. J. Wu,; Z. Zhu,; G. Q. Cui,; W. Y. Peng,; Y. Z. Tang,; J. Li,; C. H. Fan, 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]
T. W. Allen,; O. S. Andersen,; B. Roux, 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.
Google Scholar
[24]
C. Maffeo,; S. Bhattacharya,; J. Yoo,; D. Wells,; A. Aksimentiev, Modeling and simulation of ion channels. Chem. Rev. 2012, 112, 6250-6284.
Google Scholar
[25]
B. Roux, Theoretical and computational models of ion channels. Curr. Opin. Struct. Biol. 2002, 12, 182-189.
Google Scholar
[26]
Z. Zhang,; X. Huang,; Y. Qian,; W. Chen,; L. Wen,; L. Jiang, Engineering smart nanofluidic systems for artificial ion channels and ion pumps: From single-pore to multichannel membranes. Adv. Mater. 2020, 32, 1904351.
Google Scholar
[27]
Z. P. Zhu,; D. Y. Wang,; Y. Tian,; L. Jiang, Ion/molecule transportation in nanopores and nanochannels: From critical principles to diverse functions. J. Am. Chem. Soc. 2019, 141, 8658-8669.
Google Scholar
[28]
K. Xiao,; B. Tu,; L. Chen,; T. Heil,; L. P. Wen,; L. Jiang,; M. Antonietti, Photo-driven ion transport for a photodetector based on an asymmetric carbon nitride nanotube membrane. Angew. Chem., Int. Ed. 2019, 58, 12574-12579.
Google Scholar
[29]
D. Y. Lu,; Y. Li,; U. Ravaioli,; K. Schulten, Ion-nanotube terahertz oscillator. Phys. Rev. Lett. 2005, 95, 246801.
Google Scholar
[30]
G. E. Weinreb,; I. S. Magura, Physical and molecular basis of ion channel gating: Can electrostatic interactions close the ion channel? Neurophysiology 1998, 30, 325-327.
Google Scholar
[31]
Z. Yu,; T. E. McKnight,; M. N. Ericson,; A. V. Melechko,; M. L. Simpson,; B. Morrison, Vertically aligned carbon nanofiber as nano- neuron interface for monitoring neural function. Nanomedicine 2012, 8, 419-423.
Google Scholar
[32]
Y. C. Wang,; H. L. Zhu,; H. R. Yang,; A. D. Argall,; L. Luan,; C. Xie,; L. Guo, Nano functional neural interfaces. Nano Res. 2018, 11, 5065-5106.
Google Scholar
[33]
Z. Yu,; T. E. McKnight,; M. N. Ericson,; A. V.. Melechko,; M. L. Simpson,; B. Morrison, Vertically aligned carbon nanofiber arrays record electrophysiological signals from hippocampal slices. Nano Lett. 2007, 7, 2188-2195.
Google Scholar
[34]
W. Q. Li,; L. Liao,; X. H. Xiao,; X. Y. Zhao,; Z. G. Dai,; S. S. Guo,; W. Wu,; Y. Shi,; J. X. Xu,; F. Ren, et al. Modulating the threshold voltage of oxide nanowire field-effect transistors by a Ga+ ion beam. Nano Res. 2014, 7, 1691-1698.
Google Scholar
[35]
P. Paoletti,; G. C. R. Ellis-Davies,; A. Mourot, Optical control of neuronal ion channels and receptors. Nat. Rev. Neurosci. 2019, 20, 514-532.
Google Scholar
[36]
D. Gordon,; S. H. Chung, Permeation and block of the Kv1.2 channel examined using Brownian and molecular dynamics. Biophys. J. 2011, 101, 2671-2678.
Google Scholar
[37]
T. Baştuğ,; S. Kuyucak, Comparative study of the energetics of ion permeation in Kv1.2 and KcsA potassium channels. Biophys. J. 2011, 100, 629-636.
Google Scholar
[38]
P. Tang,; Y. Xu, 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.
Google Scholar
[39]
M. G. Saunders,; G. A. Voth, Coarse-graining methods for computational biology. Annu. Rev. Biophys. 2013, 42, 73-93.
Google Scholar
[40]
Q. Shi,; S. Izvekov,; G. A. Voth, Mixed atomistic and coarse-grained molecular dynamics: Simulation of a membrane-bound ion channel. J. Phys. Chem. B 2006, 110, 15045-15048.
Google Scholar
[41]
J. Summhammer,; V. Salari,; G. Bernroider, 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]
Z. Ganim,; A. Tokmakoff,; A. Vaziri, 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]
S. Bhattacharya,; S. Roy, Quantum thermodynamics and coherence in ion channels. In International Conference on Applied Physics, System Science and Computers. Springer: Cham, 2018; pp 3-11.
[44]
S. Roy,; I. Mitra,; R. Llinas, Non-Markovian noise mediated through anomalous diffusion within ion channels. Phys. Rev. E 2008, 78, 041920.
Google Scholar
[45]
A. Vaziri,; M. B. Plenio, Quantum coherence in ion channels: Resonances, transport and verification. New J. Phys. 2010, 12, 085001.
Google Scholar
[46]
S. Y. Noskov,; W. Im,; B. Roux, 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.
Google Scholar
[47]
W. Im,; S. Seefeld,; B. Roux, A grand canonical Monte Carlo- Brownian dynamics algorithm for simulating ion channels. Biophys. J. 2000, 79, 788-801.
Google Scholar
[48]
V. Salari,; M. Sajadi,; H. Bassereh,; V. Rezania,; M. Alaei,; J. A. Tuszynski, 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.
Google Scholar
[49]
I. H. Shrivastava,; M. S. P. Sansom, Simulations of ion permeation through a potassium channel: Molecular dynamics of KcsA in a phospholipid bilayer. Biophys. J. 2000, 78, 557-570.
Google Scholar
[50]
M. Sajadi,; A. Lohrasebi,; H. Rafii-Tabar, Modelling the effect of a GHz electric field on the dynamics of K+ ions in KcsA potassium channel. Mol. Simul. 2014, 40, 399-407.
Google Scholar
[51]
B. Eisenberg, Multiple scales in the simulation of ion channels and proteins. J. Phys. Chem. C 2010, 114, 20719-20733.
Google Scholar
[52]
Q. F. Zhong,; Q. Jiang,; P. B. Moore,; D. M. Newns,; M. L. Klein, Molecular dynamics simulation of a synthetic ion channel. Biophys. J. 1998, 74, 3-10.
Google Scholar
[53]
J. Breed,; R. Sankararamakrishnan,; I. D. Kerr,; M. S. Sansom, Molecular dynamics simulations of water within models of ion channels. Biophys. J. 1996, 70, 1643-1661.
Google Scholar
[54]
P. C. Biggin,; G. R. Smith,; I. Shrivastava,; S. Choe,; M. S. P. Sansom, Potassium and sodium ions in a potassium channel studied by molecular dynamics simulations. Biochim. Biophys. Acta 2001, 1510, 1-9.
Google Scholar
[55]
J. Horbach,; W. Kob,; K. Binder, Dynamics of sodium in sodium disilicate: Channel relaxation and sodium diffusion. Phys. Rev. Lett. 2002, 88, 125502.
Google Scholar
[56]
C. D. Bruce,; S. Senapati,; M. L. Berkowitz,; L. Perera,; M. D. E. Forbes, Molecular dynamics simulations of sodium dodecyl sulfate micelle in water: The behavior of water. J. Phys. Chem. B 2002, 106, 10902-10907.
Google Scholar
[57]
C. Kutzner,; H. Grubmüller,; B. L. De Groot,; U. Zachariae, Computational electrophysiology: The molecular dynamics of ion channel permeation and selectivity in atomistic detail. Biophys. J. 2011, 101, 809-817.
Google Scholar
[58]
C. Kutzner,; D. A. Köpfer,; J. P. Machtens,; B. L. de Groot,; C. Song,; U. Zachariae, Insights into the function of ion channels by computational electrophysiology simulations. Biochim. Biophys. Acta 2016, 1858, 1741-1752.
Google Scholar
[59]
S. Bernèche,; B. Roux, Molecular dynamics of the KcsA K+ channel in a bilayer membrane. Biophys. J. 2000, 78, 2900-2917.
Google Scholar
[60]
F. Conti,; L. J. De Felice,; E. Wanke, Potassium and sodium ion current noise in the membrane of the squid giant axon. J. Physiol. 1975, 248, 45-82.
Google Scholar
[61]
A. Longtin, Stochastic resonance in neuron models. J. Stat. Phys. 1993, 70, 309-327.
Google Scholar
[62]
B. J. Gluckman,; T. I. Netoff,; E. J. Neel,; W. L. Ditto,; M. L. Spano,; S. J. Schiff, Stochastic resonance in a neuronal network from mammalian brain. Phys. Rev. Lett. 1996, 77, 4098-4101.
Google Scholar
[63]
A. L. Hodgkin, The ionic basis of electrical activity in nerve and muscle. Biol. Rev. 1951, 26, 339-409.
Google Scholar
[64]
A. L. Hodgkin,; A. F. Huxley, The components of membrane conductance in the giant axon of Loligo. J. Physiol. 1952, 116, 473-496.
Google Scholar
[65]
P. Debye, Reaction rates in ionic solutions. Trans. Electrochem. Soc. 1942, 82, 265-272.
Google Scholar
[66]
J. H. Jiang,; D. L. Wu, Ice and water permittivities for millimeter and sub-millimeter remote sensing applications. Atmos. Sci. Lett. 2004, 5, 146-151.
Google Scholar
[67]
R. De Col,; K. Messlinger,; R. W. Carr, Conduction velocity is regulated by sodium channel inactivation in unmyelinated axons innervating the rat cranial meninges. J. Physiol. 2008, 586, 1089-1103.
Google Scholar
[68]
R. A. Chapman, Dependence on temperature of the conduction velocity of the action potential of the squid giant axon. Nature 1967, 213, 1143-1144.
Google Scholar
[69]
J. J. Rosenthal,; F. Bezanilla, Seasonal variation in conduction velocity of action potentials in squid giant axons. Biol. Bull. 2000, 199, 135-143.
Google Scholar
[70]
K. Lowitzsch,; H. C. Hopf,; J. Galland, Changes of sensory conduction velocity and refractory periods with decreasing tissue temperature in man. J. Neurol. 1977, 216, 181-188.
Google Scholar
[71]
S. G. Waxman,; M. V. L. Bennett, Relative conduction velocities of small myelinated and non-myelinated fibres in the central nervous system. Nat. New Biol. 1972, 238, 217-219.
Google Scholar
[72]
J. B. Hursh, Conduction velocity and diameter of nerve fibers. Am. J. Physiol. 1939, 127, 131-139.
Google Scholar
[73]
J. M. Ritchie, On the relation between fibre diameter and conduction velocity in myelinated nerve fibres. Proc. R. Soc. Lond. B 1982, 217, 29-35.
Google Scholar
[74]
C. C. McIntyre,; A. G. Richardson,; W. M. Grill, Modeling the excitability of mammalian nerve fibers: Influence of afterpotentials on the recovery cycle. J. Neurophysiol. 2002, 87, 995-1006.
Google Scholar
[75]
G. Dominguez,; H. A. Fozzard, Influence of extracellular K+ concentration on cable properties and excitability of sheep cardiac Purkinje fibers. Circ. Res. 1970, 26, 565-574.
Google Scholar
[76]
N. Tsuboi,; I. Kodama,; J. Toyama,; K. Yamada, Anisotropic conduction properties of canine ventricular muscles: Influence of high extracellular K+ concentration and stimulation frequency. Jpn. Circ. J. 1985, 49, 487-498.
Google Scholar
[77]
S. G. Waxman, Determinants of conduction velocity in myelinated nerve fibers. Muscle Nerve 1980, 3, 141-150.
Google Scholar
[78]
S. Sivasubramanian; A. Widom; Y. N. Srivastava The Clausius- Mossotti phase transition in polar liquids. Physica A. 2005, 345(3-4), 356-366.
Google Scholar
[79]
P. Q. Cai,; W. R. Leow,; X. Y. Wang,; Y. L. Wu,; X. D. Chen, Programmable nano-bio interfaces for functional biointegrated devices. Adv. Mater. 2017, 29, 1605529.
Google Scholar
[80]
C. Xie,; L. Hanson,; W. J. Xie,; Z. L. Lin,; B. X. Cui,; Y. Cui, Noninvasive neuron pinning with nanopillar arrays. Nano Lett. 2010, 10, 4020-4024.
Google Scholar
[81]
W. Zhu,; C. O’Brien,; J. R. O’Brien,; L. G. Zhang, 3D nano/ microfabrication techniques and nanobiomaterials for neural tissue regeneration. Nanomedicine 2014, 9, 859-875.
Google Scholar
[82]
K. Wang,; H. A. Fishman,; H. J. Dai,; J. S. Harris, Neural stimulation with a carbon nanotube microelectrode array. Nano Lett. 2006, 6, 2043-2048.
Google Scholar
[83]
K. Eom,; J. Kim,; J. M. Choi,; T. Kang,; J. W. Chang,; K. M. Byun,; S. B. Jun,; S. J. Kim, Enhanced infrared neural stimulation using localized surface plasmon resonance of gold nanorods. Small 2014, 10, 3853-3857.
Google Scholar
[84]
J. Li,; R. J. Andrews, 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.
Google Scholar
[85]
B. Song,; Y. S. Shu, Cell vibron polariton resonantly self-confined in the myelin sheath of nerve. Nano Res. 2020, 13, 38-44.
Google Scholar
[86]
E. W. Miller,; J. Y. Lin,; E. P. Frady,; P. A. Steinbach,; W. B. Kristan,; R. Y. Tsien, Optically monitoring voltage in neurons by photo- induced electron transfer through molecular wires. Proc. Natl. Acad. Sci. USA 2012, 109, 2114-2119.
Google Scholar
[87]
Y. Zou,; Q. Liu,; X. Yang,; H. C. Huang,; J. Li,; L. H. Du,; Z. R. Li,; J. H. Zhao; L. G. Zhu, 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.
Google Scholar
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Acknowledgements

Publication history

Received: 31 May 2020
Revised: 21 July 2020
Accepted: 27 July 2020
Published: 01 March 2021
Issue date: March 2021

Copyright

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature

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).

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