Abstract
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.
Similar content being viewed by others
References
Adair, R. (2003). Noise and stochastic resonance in voltage-gated ion channels. Proceedings of the National Academy of Sciences, 100(21), 12099–12104.
Alle, H., Roth, A., Geiger, J.R.P. (2009). Energy-efficient action potentials in hippocampal mossy fibers. Science, 325(5946), 1405–1408.
Bear, M.F., Connors, B.W., Paradiso, M.A. (2007). Neuroscience: exploring the brain. Lippincott Williams & Wilkins.
Chow, C.C., & White, J.A. (1996). Spontaneous action potentials due to channel fluctuations. Biophysical Journal, 71(6), 3013–3021.
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.
Dayan, P., & Abbott, L. (2003). Theoretical neuroscience: Computational and mathematical modeling of neural systems. Cambridge: Massachusetts Institute of Technology Press.
Guo, D.Q., & Chen, M.M. (2016). Firing regulation of fast-spiking interneurons by autaptic inhibition. Europhysics Letters, 114, 30001.
Guo, D.Q., & Wu, S.D. (2016). Regulation of irregular neuronal firing by autaptic transmission. Scientific Reports, 6, 26096.
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.
Hille, B. (2001). Ionic channels of excitable membranes, 3rd edn. Sinauer Associates: Sunderland.
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.
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.
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.
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.
McDonnell, M., & Ward, L. (2011). The benefits of noise in neural systems: bridging theory and experiment. Nature Reviews Neuroscience, 12(7), 415–426.
Moujahid, A., d’Anjou, A., Torrealdea, F. (2011). Energy and information in hodgkin-huxley neurons. Physical Review E, 83(3), 031912.
Niven, J. (2008). Energy limitation as a selective pressure on the evolution of sensory systems. Journal of Experimental Biology, 211(11), 1792–1804.
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.
Richter, D. (1957). Metabolism of the nervous system, 1st. New York: Elsevier Science and Technology Books.
Schmid, G., Goychuk, I., Hänggi, P. (2001). Stochastic resonance as a collective property of ion channel assemblies. Europhysics Letters, 56(1), 22.
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.
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.
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.
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.
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.
Shadlen, M.N., & Newsome, W.T. (1994). Noise, neural codes and cortical organization. Current Opinion in Neurobiology, 4(4), 569–579.
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.
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.
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.
Wang, L., Wang, H., Yu, L., Chen, Y. (2011). Role of axonal sodium-channel band in neuronal excitability. Physical Review E, 84(5), 052901.
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.
Ward, L.M., & Greenwood, PE. (2016). Stochastic facilitation in the brain? Journal of Statistical Mechanics: Theory and Experiment, 2016(5), 054033.
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.
Wiesenfeld, K., & Moss, F. (1995). Stochastic resonance and the benefits of noise: from ice ages to crayfish and squids. Nature, 373(6509), 33–36.
Yilmaz, E., Ozer, M., Baysal, V., Perc, M. (2016). Autapse-induced multiple coherence resonance in single neurons and neuronal networks. Scientific Reports, 6, 30914.
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.
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.
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.
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.
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interests
The authors declare that they have no conflict of interest.
Additional information
Action Editor: Susanne Schreiber
Rights and permissions
About this article
Cite this article
Liu, Y., Yue, Y., Yu, Y. et al. Effects of channel blocking on information transmission and energy efficiency in squid giant axons. J Comput Neurosci 44, 219–231 (2018). https://doi.org/10.1007/s10827-017-0676-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10827-017-0676-2