Sensory Neuroprostheses: From Signal Processing and Coding to Neural Plasticity in the Central Nervous System

  • Fivos Panetsos
  • Abel Sanchez-Jimenez
  • Celia Herrera-RinconEmail author
Part of the Fields Institute Communications book series (FIC, volume 63)


To develop neuroprostheses that will provide the nervous system with artificial sensory input through the sensory nerves to which they will be connected, on one hand we have to determine how external stimuli are represented, coded and transmitted by the Nervous System, how neurons and neuronal ensembles process, encode and transmit perceptual information. On the other we need to know how the central nervous system reacts to the implanted neuroprostheses and quantify its anatomic and functional alterations due to the artificial input it receives from our devices. Here we present mathematical and electrophysiological methods for signal acquisition, analysis, and information coding in the tactile sensory system that include a wavelet and principal component analysis-based method for neural signal analysis and different types of frequency-based signal processing and coding performed simultaneously by the sensory neurons. Finally we present a quantitative morphological study of the effects of the neuroprosthetic stimulation using a stereological approach.


Somatosensory Cortex Neural Response Stimulation Frequency Relay Station Vector Strength 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    E. Ahissar, R. Sosnik, S. Haidarliu, Transformation from temporal to rate coding in a somatosensory thalamocortical pathway. Nature 406(6793), 302–306 (2000)CrossRefGoogle Scholar
  2. 2.
    S.L.G. Andino, C. Herrera-Rincon, F. Panetsos, R.G. De Peralta, Frontiers: Combining bmi stimulation and mathematical modeling for acute stroke recovery and neural repair. Front. Neuroprosthetics 5 Google Scholar
  3. 3.
    B. Cavalieri, Geometria indivisibilibus continuorum. Bononi: Typis Clemetis Feronij (1635)Google Scholar
  4. 4.
    W.G. Cohran, Sampling Techniques (Wiley, NY, 1977)Google Scholar
  5. 5.
    L.M. Cruz-Orive, Precision of cavalieri sections and slices with local errors. J. Microsc. 193(3), 182–198 (1999)CrossRefGoogle Scholar
  6. 6.
    A.E.O.J. Delesse, Procédé mécanique pour déterminer la composition des roches. F. Savy (1866)Google Scholar
  7. 7.
    M.E. Diamond, M. Von Heimendahl, E. Arabzadeh, Whisker-mediated texture discrimination. PLoS Biol. 6(8), e220 (2008)Google Scholar
  8. 8.
    M.S. Fee, P.P. Mitra, D. Kleinfeld, Automatic sorting of multiple unit neuronal signals in the presence of anisotropic and non-gaussian variability. J. Neurosci. Meth. 69(2), 175–188 (1996)CrossRefGoogle Scholar
  9. 9.
    M.S. Fee, P.P. Mitra, D. Kleinfeld, Variability of extracellular spike waveforms of cortical neurons. J. Neurophysiol. 76(6), 3823 (1996)Google Scholar
  10. 10.
    G.L. Gerstein, M.J. Bloom, I.E. Espinosa, S. Evanczuk, M.R. Turner, Design of a laboratory for multineuron studies. IEEE Trans. Syst. Man Cybern. 13(5), 668–676 (1983)Google Scholar
  11. 11.
    E.M. Glaser, Separation of neuronal activity by waveform analysis. Adv. Biomed. Eng. 1, 77–136 (1971)Google Scholar
  12. 12.
    J.M. Goldberg, P.B. Brown, Response of binaural neurons of dog superior olivary complex to dichotic tonal stimuli: Some physiological mechanisms of sound localization. J. Neurophysiol. 32(4), 613 (1969)MathSciNetGoogle Scholar
  13. 13.
    H.J. Gundersen, E.B. Jensen, The efficiency of systematic sampling in stereology and its prediction. J. Microsc. 147(Pt 3), 229 (1987)Google Scholar
  14. 14.
    K.D. Harris, D.A. Henze, J. Csicsvari, H. Hirase, G. Buzsáki, Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J. Neurophysiol. 84(1), 401 (2000)Google Scholar
  15. 15.
    C. Herrera-Rincon, C. Torets, A. Sanchez-Jimenez, C. Avendaño, P. Guillen, F. Panetsos, in Structural Preservation of Deafferented Cortex Induced by Electrical Stimulation of a Sensory Peripheral Nerve. Engineering in Medicine and Biology Society (EMBC), 2010. Annual International Conference of the IEEE (IEEE, NY, 2010), pp. 5066–5069Google Scholar
  16. 16.
    E. Hulata, R. Segev, E. Ben-Jacob, A method for spike sorting and detection based on wavelet packets and shannon’s mutual information. J. Neurosci. Meth. 117(1), 1–12 (2002)CrossRefGoogle Scholar
  17. 17.
    Y. Karklin, M.S. Lewicki, A hierarchical bayesian model for learning nonlinear statistical regularities in nonstationary natural signals. Neural Comput. 17(2), 397–423 (2005)zbMATHCrossRefGoogle Scholar
  18. 18.
    A. Lak, E. Arabzadeh, M.E. Diamond, Enhanced response of neurons in rat somatosensory cortex to stimuli containing temporal noise. Cerebr. Cortex 18(5), 1085 (2008)Google Scholar
  19. 19.
    J.C. Letelier, P.P. Weber, Spike sorting based on discrete wavelet transform coefficients. J. Neurosci. Meth. 101(2), 93–106 (2000)CrossRefGoogle Scholar
  20. 20.
    M.S. Lewicki, A review of methods for spike sorting: the detection and classification of neural action potentials. Netw. Comput. Neural Syst. 9(4), 53–78 (1998)MathSciNetCrossRefGoogle Scholar
  21. 21.
    B.L. McNaughton, J. O’Keefe, C.A. Barnes, The stereotrode: A new technique for simultaneous isolation of several single units in the central nervous system from multiple unit records. J. Neurosci. Meth. 8(4), 391–397 (1983)CrossRefGoogle Scholar
  22. 22.
    P.R. Mouton, Principles and Practices of Unbiased Stereology: An Introduction for Bioscientists (Johns Hopkins University Press, MD, 2002)Google Scholar
  23. 23.
    F. Panetsos, A. Sanchez-Jimenez, Single unit oscillations in rat trigeminal nuclei and their control by the sensorimotor cortex. Neuroscience 169(2), 893 – 905 (2010)CrossRefGoogle Scholar
  24. 24.
    A. Pavlov, V.A. Makarov, I. Makarova, F. Panetsos, Sorting of neural spikes: When wavelet based methods outperform principal component analysis. Nat. Comput. 6(3), 269–281 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    G. Paxinos, C. Watson, The Rat Brain in Stereotaxic Coordinates (Academic, NY, 2007)Google Scholar
  26. 26.
    M.C. Quirk, M.A. Wilson, Interaction between spike waveform classification and temporal sequence detection. J. Neurosci. Meth. 94(1), 41–52 (1999)CrossRefGoogle Scholar
  27. 27.
    Q. Quiroga, Z. Nadasdy, Y. Ben-Shaul, Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 16, 1661–1687 (2004)zbMATHCrossRefGoogle Scholar
  28. 28.
    R. Romo, A. Hernández, A. Zainos, C. Brody, E. Salinas, Exploring the cortical evidence of a sensory–discrimination process. Phil. Trans. Roy. Soc. Lond. B Biol. Sci. 357(1424), 1039 (2002)Google Scholar
  29. 29.
    E. Salinas, A. Hernández, A. Zainos, R. Romo, Periodicity and firing rate as candidate neural codes for the frequency of vibrotactile stimuli. J. Neurosci. 20(14), 5503 (2000)Google Scholar
  30. 30.
    A. Sanchez-Jimenez, F. Panetsos, A. Murciano, Early frequency-dependent information processing and cortical control in the whisker pathway of the rat: Electrophysiological study of brainstem nuclei principalis and interpolaris. Neuroscience 160(1), 212–226 (2009)CrossRefGoogle Scholar
  31. 31.
    E.M. Schmidt, Computer separation of multi-unit neuroelectric data: A review. J. Neurosci. Meth. 12(2), 95–111 (1984)CrossRefGoogle Scholar
  32. 32.
    R.K. Snider, A.B. Bonds, Classification of non-stationary neural signals. J. Neurosci. Meth. 84(1–2), 155–166 (1998)CrossRefGoogle Scholar
  33. 33.
    P.M.E. Waite, D.J. Tracey, Trigeminal sensory system. Rat Nervous Syst. 705–724 (1995)Google Scholar
  34. 34.
    M. Wong-Riley, Changes in the visual system of monocularly sutured or enucleated cats demonstrable with cytochrome oxidase histochemistry. Brain Res. 171(1), 11–28 (1979)CrossRefGoogle Scholar
  35. 35.
    M.T.T. Wong-Riley, Cytochrome oxidase: An endogenous metabolic marker for neuronal activity. Trends Neurosci. 12(3), 94–101 (1989)CrossRefGoogle Scholar
  36. 36.
    T.A. Woolsey, The structural organization of layer iv in the somatosensory region (si) of the mouse cerebral cortex: The description of a cortical field composed of discrete cytoarchitectonic units. Brain Res. 17, 205–242 (1970)CrossRefGoogle Scholar
  37. 37.
    T.A. Woolsey, C. Welker, R.H. Schwartz, Comparative anatomical studies of the sml face cortex with special reference to the occurrence of “barrels” in layer iv. J. Comp. Neurol. 164(1), 79–94 (1975)CrossRefGoogle Scholar
  38. 38.
    T.A. Woolsey, M.L. Dierker, D.F. Wann, Mouse smi cortex: Qualitative and quantitative classification of golgi-impregnated barrel neurons. Proc. Natl. Acad. Sci. U.S.A. 72(6), 2165 (1975)Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Fivos Panetsos
    • 1
    • 2
  • Abel Sanchez-Jimenez
    • 1
    • 3
  • Celia Herrera-Rincon
    • 1
    • 2
    Email author
  1. 1.Neurocomputing and Neurorobotics Research Group and Department of Applied Mathematics (Biomathematics)Complutense University of MadridMadridSpain
  2. 2.School of OpticsComplutense University of MadridMadridSpain
  3. 3.Faculty of BiologyComplutense University of MadridMadridSpain

Personalised recommendations