System for Nature-inspired Signal Processing: Principles and Practice



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Submitted Nov 25, 2019
Published Dec 23, 2019
10.24018/ejece.2019.3.6.154

Abstract viewed = 358 times

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This paper establishes the foundational principles and practice for a unified theory of arbitrary information management by disclosing systems, devices and methods for the management of substrates or biological substrates. In this context, a substrate is any aspect of any entity that is capable of responding to or emitting stimuli irrespective of whether the stimuli actually emanate from any aspect of the entity or not. Management of substrates could be achieved through the management of stimuli that modulate or moderate or influence any aspect of the substrate as well as through the management of any stimuli emanating from the substrate. The results enable a wide range of novel applications in a variety of fields with far-reaching implications. For example, the functional organization of many regions of the brain including the superior temporal cortex which is believed to play a critical role in the hierarchical processing of human visual and auditory stimuli is poorly understood. It is not known precisely which layer within which region of the brain is responsible for which aspect of visual or auditory processing. Simultaneous non-invasive acquisition of bio-signals representing contributions from multiple layers of neuronal populations within the brain could provide new insights leading to the resolution of many of these outstanding issues and provide a deeper understanding of the underlying physiological processes.

Keywords: Nature-inspired signal processing, Noninvasive Brain-Computer Interfaces, Ampullae of Lorenzini

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References

  1. Baldwin, I. T. & Schultz, J. C., ?Rapid Changes in Tree Leaf Chemistry Induced by Damage: Evidence for Communication Between Plants,? Science 221, 277-279 (1983).
     Google Scholar
  2. Karban,R et. al., ?Communication Between Plants: induced resistance in wild tobacco plants following clipping of neighboring sagebrush,? Oecologia 125, 66-71 (Springer-Verlag 2000).
     Google Scholar
  3. Pye, J. D., ?A theory of echolocation by bats,? J. Laryng. Otol. 74, 718-729 (1960).
     Google Scholar
  4. Pye, J. D., ?Perception of distance in animal echolocation,? Nature Lond. 190, 362-363 (1961).
     Google Scholar
  5. Pye, J. D., ? A. Correlated orientation sounds and ear movements of horseshoe bats,? Nature Lond. 196, 1185-1188 (1963).
     Google Scholar
  6. Moss, C. F., ?Acoustic information available to bats using frequency modulated sounds for the perception of prey,? J. Acoust. Soc. Am. 95, 2745?2756 (1994).
     Google Scholar
  7. Jensen, M. E., ?Detection of Prey in s Cluttered Environment by the Northern Bat Eptesicus Nilssonii,? J. Exp. Biol. 204, 199?208 (2001).
     Google Scholar
  8. Murray, R. W., ?The Response of Ampullae of Lorenzini of Elasmobranchs to Electrical Stimulation,? J. Exp. Biol. 39, 119?128 (1962).
     Google Scholar
  9. Murray, R. W., ?Electrical Sensitivity of the Ampull? of Lorenzini,? Nature 187, 957 (1960).
     Google Scholar
  10. Kalmijn, A. J., ?Electro-perception in Sharks and Rays,? Nature 212, 1232-1233 (1966).
     Google Scholar
  11. Kalmijn, A. J., ?Electric and magnetic field detection in elasmobranch fishes," Science 26, 916-918 (1982).
     Google Scholar
  12. Brown, B. R., ?Modeling an electro-sensory landscape: behavioral and morphological optimization in elasmobranch prey capture,? J. Exp. Biol. 205, 999?1007 (2002).
     Google Scholar
  13. Jenkins, M. W. et. al., ?Optical pacing of the embryonic heart,? Nature Photonics 4, 623?626 (2010).
     Google Scholar
  14. Furnan, S. & Schwedel, J. B., ?An Intracardiac Pacemaker for Stokes?Adams Seizures,? Pac. Clin. Electrophysiol. 29, 453?458 (2006).
     Google Scholar
  15. Bohm, A et. al., ?Clinical Observations with Long-term Atrial Pacing,? Pac. Clin. Electrophysiol. 21, 246?249 (1998).
     Google Scholar
  16. Halperin, D. et. al., ?Pacemakers and Implantable Cardiac Defibrillators: Software Radio Attacks and Zero-Power Defenses,? IEEE Symp. Sec. Priv. 129?142 (2008).
     Google Scholar
  17. Yatani, A. et. al., ?Heart rate regulation by G proteins acting on the cardiac pacemaker channel,? Science 249, 1163-1166 (1990).
     Google Scholar
  18. Hoshi, T., Zagotta, W. N. & Aldrich, R. W., ?Biophysical and molecular mechanisms of shaker potassium channel inactivation,? Science 250, 533-538 (1990).
     Google Scholar
  19. Hodgkin, A. L. & Huxley, A. F., ?A quantitative description of membrane current and its application to conduction and excitation in nerve,? J. Physiol. (Lond.) 117, 500-544 (1952).
     Google Scholar
  20. Hodgkin, A. L., Huxley, A. F. & Katz, B., ?Measurement of current-voltage relations in the membrane of the giant axon of Loligo,? J. Physiol. (Lond.) 116, 424-448 (1952).
     Google Scholar
  21. Noble, D., ?Application of Hodgkin-Huxley equations to excitable tissues,? Physiol. Rev. 46, 1-50 (1966).
     Google Scholar
  22. Neher, E., Sakmann, B. & Katz, B., ?Single-channel currents recorded from membrane of denervated frog muscle fibers,? Nature 260, 799-802 (1976).
     Google Scholar
  23. Neher, E., Sakmann, B. & Katz, B., ?The patch clamp technique,? Sci. Am. 266, 28-35 (1992).
     Google Scholar
  24. Hamill, O. P. et. al., ?Improved patch clamp techniques for high resolution current recording from cells and cell-free membranes,? Pfl?ger Arch. ges. Physiol. 391, 85-100 (1981).
     Google Scholar
  25. Sakmann, B. & Neher, E., ?Patch clamp techniques for studying ionic channels in excitable membrane,? Annu. Rev. Physiol. 46, 455-472 (1984).
     Google Scholar
  26. Cole, K. S. & Moore, J. W., ?Potassium ion current in the squid giant axon: Dynamic characteristics,? Biophys. J. 1, 1-14 (1960).
     Google Scholar
  27. Armstrong, C. W. & Hille, B., ?The inner quaternary ammonium ion receptor in potassium channels of the node of Ranvier,? J. Gen. Physiol. 59, 388-400 (1972).
     Google Scholar
  28. Neher, E. & Marty, A., ?Discrete changes of cell membrane capacitance observed under conditions of enhanced secretion in bovine adrenal chromaffin cells,? Proc. Nat. Acad. Sci. USA 79, 6712- 6716 (1982).
     Google Scholar
  29. Unwin, P. N. T. & Zampighi, G., ?Structure of the junctions between communicating cells,? Nature 283, 545-549 (1980).
     Google Scholar
  30. Toyoshima, C. & Unwin, N., ?Ion channel of acetylcholine receptor reconstructed from images of postsynaptic membranes,? Nature 336, 247-250 (1988).
     Google Scholar
  31. Patlak, J. B. & Ortiz, M., ?Two modes of gating during late Na+ channel currents in frog sartorius muscle,? J. Gen. Physiol. 87, 305-326 (1986).
     Google Scholar
  32. Phelps, M. E. & Mazziotta, J. C., ?Positron emission tomography: human brain function and biochemistry,? Science 228, 799-809 (1985).
     Google Scholar
  33. Cohen, D., Edelsack, E. A. & Zimmerman, J. E., ?Magnetocardiograms taken inside a shielded room with a superconducting point contact magnetometer,? Appl. Phys. Lett. 16, 278-280 (1970).
     Google Scholar
  34. Cohen, D., ?Magnetoencephalography: detection of the brain's electrical activity with a superconducting magnetometer,? Science 175, 664-666 (1972).
     Google Scholar
  35. Zimmerman, J. E., Theine, P. & Harding, J. T., ?Design and operation of stable rf-biased superconducting point-contact quantum devices, etc.,? Appl. Phys. Lett. 41, 1572-1580 (1970).
     Google Scholar
  36. Malmivuo, J., Suihko, V. & Eskola, H., ?Sensitivity distributions of EEG and MEG measurements,? IEEE Trans. Biomed. Eng. 44, 196?208 (1997).
     Google Scholar
  37. Oostendorp, T. F., Delbecke, J. & Stegman, D. F., ?The conductivity of the human skull: Results in vivo and in vitro measurements,? IEEE Trans. Biomed. Eng. 47, 1487?1492 (2000).
     Google Scholar
  38. Rush, S. & Driscoll, D. A., ?EEG-electrode sensitivity?An application of reciprocity,? IEEE Trans. Biomed. Eng. 16, 15?22 (1969).
     Google Scholar
  39. Junhofer, M. et. al., ?Statistical control of artifacts in dense array EEG/MEG studies,? Psychophysiology 37, 523-532 (2000).
     Google Scholar
  40. Freeman, W. J. et. al., ?Spatial spectra of scalp EEG and EMG from awake humans,? Clin. Neurophysiol. 114, 1053-1068 (2003).
     Google Scholar
  41. V?is?nen, O. & Malmivuo, J., ?Improving the SNR of EEG generated by deep sources with weighted multielectrode leads,? J. Physiol.-Paris 103, 306-314 (2009).
     Google Scholar
  42. Cohen, D. et. al., ?MEG versus EEG localization test using implanted sources in the human brain,? Ann. of Neurol. 28, 811?817 (1990).
     Google Scholar
  43. Hallez, H. et. al., ?Review on solving the forward problem in EEG source analysis,? J. NeuroEng. Rehab. 4:46 (2007) doi:10.1186/1743-0003-4-46.
     Google Scholar
  44. Jungh?fer, M. et. al., ?Mapping EEG-potentials on the surface of the brain: a strategy for uncovering cortical sources,? Brain Topogr. 9, 203-217 (1997).
     Google Scholar
  45. Wolpaw, J. R. & McFarland, D. J., ?Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans,? Proc. Natl. Acad. Sci. USA 101, 17849?17854 (2004).
     Google Scholar
  46. Freeman, W. J., ?Use of Spatial Deconvolution to Compensate for Distortions of EEG by Volume Conduction,? IEEE Trans. Biomed. Eng. 27, 421?429 (1980).
     Google Scholar
  47. Ekpar, F. E., ?Method and Apparatus for Creating Interactive Virtual Tours,? United States Patent 7567274 (2009).
     Google Scholar
  48. Greischar, L. L. et. al., ?Effects of electrode density and electrolyte spreading in dense array electroencephalographic recording,? Clin. Neurophysiol. 115, 710?720 (2004).
     Google Scholar
  49. Tenke, C. E. & Kayser, J., ?A convenient method for detecting electrolyte bridges in multichannel electroencephalogram and event-related potential recordings,? Clin. Neurophysiol. 112, 545-550. (2001).
     Google Scholar
  50. Chang, E. F. et. al., ?Categorical speech representation in human superior temporal gyrus,? Nature Neuroscience 13, 1428-1432. (2010).
     Google Scholar
  51. B. Hjorth, ?Source derivation simplifies topographical EEG interpretation, Am J EEG Technol 20: pp. 121-132 (1980). American Journal Of Eeg Technology (1980)
     Google Scholar
  52. C. E. Tenke, J. Kayser, ?A convenient method for detecting electrolyte bridges in multichannel electroencephalogram and event-related potential recordings.?, Clinical Neurophysiology 112, pp. 545-550. (2001).
     Google Scholar