Journal of Computational Neuroscience

, Volume 44, Issue 2, pp 219–231 | Cite as

Effects of channel blocking on information transmission and energy efficiency in squid giant axons

  • Yujiang Liu
  • Yuan Yue
  • Yuguo Yu
  • Liwei Liu
  • Lianchun YuEmail author


Action potentials are the information carriers of neural systems. The generation of action potentials involves the cooperative opening and closing of sodium and potassium channels. This process is metabolically expensive because the ions flowing through open channels need to be restored to maintain concentration gradients of these ions. Toxins like tetraethylammonium can block working ion channels, thus affecting the function and energy cost of neurons. In this paper, by computer simulation of the Hodgkin-Huxley neuron model, we studied the effects of channel blocking with toxins on the information transmission and energy efficiency in squid giant axons. We found that gradually blocking sodium channels will sequentially maximize the information transmission and energy efficiency of the axons, whereas moderate blocking of potassium channels will have little impact on the information transmission and will decrease the energy efficiency. Heavy blocking of potassium channels will cause self-sustained oscillation of membrane potentials. Simultaneously blocking sodium and potassium channels with the same ratio increases both information transmission and energy efficiency. Our results are in line with previous studies suggesting that information processing capacity and energy efficiency can be maximized by regulating the number of active ion channels, and this indicates a viable avenue for future experimentation.


Squid giant axon Ion channel blocking Information rate Energy efficiency 



This work was supported by the National Natural Science Foundation of China under Grant Nos. 11564034, 11105062, the Fundamental Research Funds for the Central Universities under Grant No. lzujbky-2015-119, 31920130008.

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.


  1. Adair, R. (2003). Noise and stochastic resonance in voltage-gated ion channels. Proceedings of the National Academy of Sciences, 100(21), 12099–12104.CrossRefGoogle Scholar
  2. Alle, H., Roth, A., Geiger, J.R.P. (2009). Energy-efficient action potentials in hippocampal mossy fibers. Science, 325(5946), 1405–1408.CrossRefPubMedGoogle Scholar
  3. Bear, M.F., Connors, B.W., Paradiso, M.A. (2007). Neuroscience: exploring the brain. Lippincott Williams & Wilkins.Google Scholar
  4. Chow, C.C., & White, J.A. (1996). Spontaneous action potentials due to channel fluctuations. Biophysical Journal, 71(6), 3013–3021.CrossRefPubMedPubMedCentralGoogle Scholar
  5. Clarke, D., & Sokoloff, L. (1999). Circulation and energy metabolism of the brain. In Siegel, G J, Agranoff, B W, Albers, R W, Fisher, S K, & Uhler, M D (Eds.) Basic neurochemistry: Molecular, cellular and medical aspects. New York: Lippincott-Raven.Google Scholar
  6. Dayan, P., & Abbott, L. (2003). Theoretical neuroscience: Computational and mathematical modeling of neural systems. Cambridge: Massachusetts Institute of Technology Press.Google Scholar
  7. Guo, D.Q., & Chen, M.M. (2016). Firing regulation of fast-spiking interneurons by autaptic inhibition. Europhysics Letters, 114, 30001.CrossRefGoogle Scholar
  8. Guo, D.Q., & Wu, S.D. (2016). Regulation of irregular neuronal firing by autaptic transmission. Scientific Reports, 6, 26096.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Hänggi, P. (2002). Stochastic resonance in biology how noise can enhance detection of weak signals and help improve biological information processing. ChemPhysChem, 3(3), 285–290.CrossRefPubMedGoogle Scholar
  10. Hille, B. (2001). Ionic channels of excitable membranes, 3rd edn. Sinauer Associates: Sunderland.Google Scholar
  11. Hodgkin, A. (1975). The optimum density of sodium channels in an unmyelinated nerve. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 270(908), 297–300.CrossRefPubMedGoogle Scholar
  12. Hodgkin, A., & Huxley, A. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500–544.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Laughlin, S.B., De Ruyter van Steveninck, R.R., Anderson, J.C. (1998). The metabolic cost of neural information. Nature Neuroscience, 1(1), 36–41.CrossRefPubMedGoogle Scholar
  14. Lecar, H., & Nossal, R. (1971). Theory of threshold fluctuations in nerves: I. relationships between electrical noise and fluctuations in axon firing. Biophysical Journal, 11(12), 1048–1067.CrossRefPubMedPubMedCentralGoogle Scholar
  15. McDonnell, M., & Ward, L. (2011). The benefits of noise in neural systems: bridging theory and experiment. Nature Reviews Neuroscience, 12(7), 415–426.CrossRefPubMedGoogle Scholar
  16. Moujahid, A., d’Anjou, A., Torrealdea, F. (2011). Energy and information in hodgkin-huxley neurons. Physical Review E, 83(3), 031912.CrossRefGoogle Scholar
  17. Niven, J. (2008). Energy limitation as a selective pressure on the evolution of sensory systems. Journal of Experimental Biology, 211(11), 1792–1804.CrossRefPubMedGoogle Scholar
  18. Ohiorhenuan, I.E., Mechler, F., Purpura, K.P., Schmid, A.M., Victor, J.D. (2010). Sparse coding and high-order correlations in fine-scale cortical networks. Nature, 466, 617–621.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Richter, D. (1957). Metabolism of the nervous system, 1st. New York: Elsevier Science and Technology Books.Google Scholar
  20. Schmid, G., Goychuk, I., Hänggi, P. (2001). Stochastic resonance as a collective property of ion channel assemblies. Europhysics Letters, 56(1), 22.CrossRefGoogle Scholar
  21. Schmid, G., Goychuk, I., Hänggi, P. (2004). Effect of channel block on the spiking activity of excitable membranes in a stochastic hodgkin–huxley model. Physical Biology, 1(2), 61–66.CrossRefPubMedGoogle Scholar
  22. Schneidman, E., Freedman, B., Segev, I. (1998). Ion channel stochasticity may be critical in determining the reliability and precision of spike timing. Neural Computation, 10(7), 1679–1703.CrossRefPubMedGoogle Scholar
  23. Schreiber, S., Machens, C.K., Herz, A.V.M., Laughlin, S.B. (2002). Energy-efficient coding with discrete stochastic events. Neural Computation, 14(6), 1323–1346.CrossRefPubMedGoogle Scholar
  24. Sengupta, B., Faisal, A.A., Laughlin, S.B., Niven, J.E. (2013). The effect of cell size and channel density on neuronal information encoding and energy efficiency. Journal of Cerebral Blood Flow & Metabolism, 33(9), 1465–1473.CrossRefGoogle Scholar
  25. Sengupta, B., Stemmler, M., Laughlin, S.B., Niven, J.E. (2010). Action potential energy efficency varies among neuron types in vertebrates and invertebrates. PLOS Computational Biology, 6(7), e1000840.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Shadlen, M.N., & Newsome, W.T. (1994). Noise, neural codes and cortical organization. Current Opinion in Neurobiology, 4(4), 569–579.CrossRefPubMedGoogle Scholar
  27. Steinmetz, P.N., Manwani, A., Koch, C., London, M., Segev, I. (2000). Subthreshold voltage noise due to channel fluctuations in active neuronal membranes. Journal of Computational Neuroscience, 9(2), 133–148.CrossRefPubMedGoogle Scholar
  28. Strong, S., Koberle, R., De Ruyter van Steveninck, R.R., Bialek, W. (1998). Entropy and information in neural spike trains. Physical Review Letters, 80(1), 197–200.CrossRefGoogle Scholar
  29. Van Rullen, R., & Thorpe, S.J. (2001). Rate coding versus temporal order coding: what the retinal ganglion cells tell the visual cortex. Neural Computation, 13, 1255–1283.CrossRefPubMedGoogle Scholar
  30. Wang, L., Wang, H., Yu, L., Chen, Y. (2011). Role of axonal sodium-channel band in neuronal excitability. Physical Review E, 84(5), 052901.CrossRefGoogle Scholar
  31. Wang, L.F., Jia, F., Liu, X.Z., Song, Y.L., Yu, L.C. (2015). Temperature effects on information capacity and energy efficiency of hodgkin–huxley neuron. Chinese Physics Letters, 32(10), 108701.CrossRefGoogle Scholar
  32. Ward, L.M., & Greenwood, PE. (2016). Stochastic facilitation in the brain? Journal of Statistical Mechanics: Theory and Experiment, 2016(5), 054033.CrossRefGoogle Scholar
  33. White, J.A., Klink, R., Alonso, A., Kay, A.R. (1998). Noise from voltage-gated ion channels may influence neuronal dynamics in the entorhinal cortex. Journal of Neurophysiology, 80(1), 262–269.CrossRefPubMedGoogle Scholar
  34. Wiesenfeld, K., & Moss, F. (1995). Stochastic resonance and the benefits of noise: from ice ages to crayfish and squids. Nature, 373(6509), 33–36.CrossRefPubMedGoogle Scholar
  35. Yilmaz, E., Ozer, M., Baysal, V., Perc, M. (2016). Autapse-induced multiple coherence resonance in single neurons and neuronal networks. Scientific Reports, 6, 30914.CrossRefPubMedPubMedCentralGoogle Scholar
  36. Yu, L.C., & Liu, L.W. (2014). Optimal size of stochastic hodgkin-huxley neuronal systems for maximal energy efficiency in coding pulse signals. Physical Review E, 89(3), 032725.CrossRefGoogle Scholar
  37. Yu, L.C., Zhang, C., Liu, L.W., Yu, Y.G. (2016). Energy-efficient population coding constrains network size of a neuronal array system. Scientific Reports, 6, 19369.CrossRefPubMedPubMedCentralGoogle Scholar
  38. Yu, L.C., & Yu, Y.G. (2017). Energy-efficient neural information processing in individual neurons and neuronal networks. Journal of Neuroscience Research, 95(11), 2253.CrossRefPubMedGoogle Scholar
  39. Yu, Y.G., Hill, A.P., McCormick, D.A. (2012). Warm body temperature facilitates energy efficient cortical action potentials. PLOS Computational Biology, 8(4), 1–16.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Yujiang Liu
    • 1
  • Yuan Yue
    • 1
    • 2
  • Yuguo Yu
    • 3
  • Liwei Liu
    • 2
  • Lianchun Yu
    • 1
    Email author
  1. 1.Institute of Theoretical PhysicsLanzhou UniversityLanzhouChina
  2. 2.College of Electrical EngineeringNorthwest University for NationalitiesLanzhouChina
  3. 3.School of Life Science and the Collaborative Innovation Center for Brain Science, Center for Computational Systems BiologyFudan UniversityShanghai ShiChina

Personalised recommendations