1. Abbott, B. C., Hill, A. V. and Howarth, J. V. The positive and negative heat production associated with a nerve impulse. Proc. R. Soc. Lond. B, 1958, 148(931), 149–187. 
https://doi.org/10.1098/rspb.1958.0012   

2. Alvargonzález, D. Multidisciplinarity, interdisciplinarity, transdisciplinarity, and the sciences. Int. Stud. Philos. Sci., 2011, 25(4), 387–403. 
https://doi.org/10.1080/02698595.2011.623366  

3. Andersen, S. S. L., Jackson, A. D. and Heimburg, T. Towards a thermodynamic theory of nerve pulse propagation. Prog. Neurobiol., 2009, 88(2), 104–113. 
https://doi.org/10.1016/j.pneurobio.2009.03.002  

4. Bialek, W. Perspectives on theory at the interface of physics and biology. Rep. Prog. Phys., 2018, 81(1), 012601. 
https://doi.org/10.1088/1361-6633/aa995b  

5. Bressloff, P. C. Waves in Neural Media. From Single Neurons to Neural Fields. In Lecture Notes on Mathematical Modelling in the Life Sciences. Springer, New York, 2014. 
https://doi.org/10.1007/978-1-4614-8866-8  

6. Brohawn, S. G., Wang, W., Handler, A., Campbell, E. B., Schwarz, J. R. and MacKinnon, R. The mechanosensitive ion channel TRAAK is localized to the mammalian node of Ranvier. eLife, 2019, 8, e50403. 
https://doi.org/10.7554/eLife.50403  

7. Carrillo, N. and Martínez, S. Scientific inquiry: from metaphors to abstraction. Perspect. Sci., 2023, 31(2), 233–261. 
https://doi.org/10.1162/posc_a_00571  

8. Cartwright, N. How the Laws of Physics Lie. Oxford University Press, Oxford, 1983. 
https://doi.org/10.1093/0198247044.001.0001  

9. Cohen, J. E. Mathematics is biology’s next microscope, only better; biology is mathematics’ next physics, only better. PLOS Biol., 2004, 2(12), e439. 
https://doi.org/10.1371/journal.pbio.0020439  

10. Coombes, S. and Wedgwood, K. C. A. Neurodynamics. An Applied Mathematics Perspective. In Texts in Applied Math­ematics, 75. Springer, Cham, 2023. 
https://doi.org/10.1007/978-3-031-21916-0  

11. Coveney, P. V. and Fowler, P. W. Modelling biological complexity: a physical scientist’s perspective. J. R. Soc. Inter­face, 2005, 2(4), 267–280. 
https://doi.org/10.1098/rsif.2005.0045  

12. DeLanda, M. Intensive Science and Virtual Philosophy. Continuum, London, 2004.

13. Einstein, A. On the method of theoretical physics. Philos. Sci., 1934, 1(2), 163–169.
https://doi.org/10.1086/286316

14. Engelbrecht, J., Tamm, K. and Peets, T. Internal variables used for describing the signal propagation in axons. Continuum Mech. Thermodyn., 2020, 32(6), 1619–1627. 
https://doi.org/10.1007/s00161-020-00868-2  

15. Engelbrecht, J., Tamm, K. and Peets, T. Modeling of complex signals in nerve fibers. Med. Hypotheses, 2018, 120, 90–95. 
https://doi.org/10.1016/j.mehy.2018.08.021   

16. Engelbrecht, J., Tamm, K. and Peets, T. Modelling of Complex Signals in Nerves. Springer, Cham, 2021. 
https://doi.org/10.1007/978-3-030-75039-8  

17. Engelbrecht, J., Tamm, K. and Peets, T. On mathematical modelling of solitary pulses in cylindrical biomembranes. Biomech. Model. Mechanobiol., 2015, 14(1), 159–167. 
https://doi.org/10.1007/s10237-014-0596-2  

18. Engelbrecht, J., Tamm, K. and Peets, T. On the phenomenological modelling of physical phenomena. Proc. Estonian Acad. Sci., 2024, 73(3), 264‒278. 
https://doi.org/10.3176/proc.2024.3.10  

19. Ermentrout, G. B. and Terman, D. H. Mathematical Foundations of Neuroscience. In Interdisciplinary Applied Mathematics, 35. Springer, New York, 2010. 
https://doi.org/10.1007/978-0-387-87708-2  

20. National Academies. Facilitating Interdisciplinary Research. National Academies Press, Washington, D. C., 2004. 
https://doi.org/10.17226/11153  

21. Gavaghan, D., Garny, A., Maini, P. K. and Kohl, P. Mathematical models in physiology. Phil. Trans. R. Soc. A, 2006, 364(1842), 1099–1106. 
https://doi.org/10.1098/rsta.2006.1757  

22. Heimburg, T. and Jackson, A. D. On soliton propagation in biomembranes and nerves. Proc. Natl. Acad. Sci. U. S. A., 2005, 102(28), 9790–9795. 
https://doi.org/10.1073/pnas.0503823102  

23. Hodgkin, A. L. and Huxley, A. F. A quantitative description of membrane current and its application to conduction and ex­citation in nerve. J. Physiol., 1952, 117(4), 500–544. 
https://doi.org/10.1113/jphysiol.1952.sp004764  

24. Hodgkin, A. L. The Conduction of the Nervous Impulse. Liverpool University Press, 1964.

25. Hopfield, J. J. Two cultures? Experiences at the physics-biology interface. Phys. Biol., 2014, 11(5), 053002. 
https://doi.org/10.1088/1478-3975/11/5/053002  

26. Izhikevich, E. M. Dynamical Systems in Neuroscience. The Geometry of Excitability and Bursting. The MIT Press, London, 2006. 
https://doi.org/10.7551/mitpress/2526.001.0001  

27. Kaplan, D. M. and Craver, C. F. The explanatory force of dynamical and mathematical models in neuroscience: a mech­anistic perspective. Philos. Sci., 2011, 78(4), 601–627. 
https://doi.org/10.1086/661755  

28. Kohl, P., Crampin, E. J., Quinn, T. A. and Noble, D. Systems biology: an approach. Clin. Pharmacol. Ther., 2010, 88(1), 25–33. 
https://doi.org/10.1038/clpt.2010.92  

29. Lieberstein, H. M. On the Hodgkin-Huxley partial differential equation. Math. Biosci., 1967, 1(1), 45–69. 
https://doi.org/10.1016/0025-5564(67)90026-0  

30. Ling, T., Boyle, K. C., Zuckerman, V., Flores, T., Ramakrishnan, C., Deisseroth, K. et al. High-speed interferometric imaging reveals dynamics of neuronal deformation during the action potential. Proc. Natl. Acad. Sci. U. S. A., 2020, 117(19), 10278–10285. 
https://doi.org/10.1073/pnas.1920039117  

31. Maugin, G. A. Internal variables and dissipative structures. J. Non-Equilib. Thermodyn., 1990, 15(2), 173‒192. 
https://doi.org/10.1515/jnet.1990.15.2.173   

32. McCulloch, A. D. and Huber, G. Integrative biological modelling in silico. In ‘In Silico’ Simulation of Biological Processes: Novartis Foundation Symposium (Bock, G. and Goode, J. A., eds), 247, 4–25. John Wiley & Sons, Chichester, 2002. 
https://doi.org/10.1002/0470857897.ch2  

33. Mueller, J. K. and Tyler, W. J. A quantitative overview of biophysical forces impinging on neural function. Phys. Biol., 2014, 11(5), 051001. 
https://doi.org/10.1088/1478-3975/11/5/051001   

34. Nagumo, J., Arimoto, S. and Yoshizawa, S. An active pulse transmission line simulating nerve axon. Proc. IRE, 1962, 50(10), 2061–2070. 
https://doi.org/10.1109/JRPROC.1962.288235  

35. National Research Council. Catalyzing Inquiry at the Interface of Computing and Biology. National Academies Press, Washington, D. C., 2005. 
https://doi.org/10.17226/11480  

36. Nelson, P. Biological Physics: Energy, Information, Life. W. H. Freeman, New York, 2003.

37. Noble, D. The rise of computational biology. Nat. Rev. Mol. Cell Biol., 2002, 3(6), 459–463. 
https://doi.org/10.1038/nrm810  

38. Nolte, D. D. Introduction to Modern Dynamics: Chaos, Networks, Space and Time. Oxford University Press, Oxford, 2015.

39. Porubov, A. V. Amplification of Nonlinear Strain Waves in Solids. World Scientific, Singapore, 2003.
https://doi.org/10.1142/9789812794291

40. Rovelli, C. Helgoland. Penguin Random House, London, 2022.

41. Scott, A. Neuroscience: A Mathematical Primer. Springer, New York, 2002.

42. Shrivastava, S. and Schneider, M. F. Evidence for two-dimensional solitary sound waves in a lipid controlled interface and its implications for biological signalling. J. R. Soc. Interface, 2014, 11(97), 20140098. 
https://doi.org/10.1098/rsif.2014.0098  

43. Slattery, J. C. Momentum, Energy, and Mass Transfer in Continua. McGraw-Hill, New York, 1971.

44. Tasaki, I. A macromolecular approach to excitation phenomena: mechanical and thermal changes in nerve during excitation. Physiol. Chem. Phys. Med. NMR, 1988, 20(4), 251–268.

45. Terakawa, S. Potential-dependent variations of the intracellular pressure in the intracellularly perfused squid giant axon. J. Physiol., 1985, 369(1), 229–248. 
https://doi.org/10.1113/jphysiol.1985.sp015898  

46. Torres, N. V. and Santos, G. The (mathematical) modeling process in biosciences. Front. Genet., 2015, 6, 1–9. 
https://doi.org/10.3389/fgene.2015.00354