Advertisement

Single LFP Sorting for High-Resolution Brain-Chip Interfacing

  • Mufti Mahmud
  • Davide Travalin
  • Amir Hussain
  • Stefano Girardi
  • Marta Maschietto
  • Florian Felderer
  • Stefano Vassanelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7366)

Abstract

Understanding cognition has fascinated many neuroscientists and made them put their efforts in deciphering the brain’s information processing capabilities for cognition. Rodents perceive the environment through whisking during which tactile information is processed at the barrel columns of the somatosensory cortex (S1). The intra– and trans–columnar microcircuits in the barrel cortex segregate and integrate information during activation of this pathway. Local Field Potentials (LFPs) recorded from these barrel columns provide information about the microcircuits and the shape of the LFPs provide the fingerprint of the underlying neuronal network. Through a contour based sorting method, we could sort neuronal evoked LFPs recorded using high–resolution Electrolyte–Oxide–Semiconductor Field Effect Transistor (EOSFET) based neuronal probes. We also report that the latencies and amplitudes of the individual LFPs’ shapes vary among the different clusters generated by the method. The shape specific information of the single LFPs thus can be used in commenting on the underlying neuronal network generating those signals.

Keywords

Neuronal probe whisker stimulation evoked brain activity neuronal signal neuronal signal analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Maschietto, M., Mahmud, M., Girardi, S., Vassanelli, S.: A High Resolution Bi–Directional Communication through a Brain–Chip Interface. In: 2009 ECSIS Symposium on Advanced Technologies for Enhanced Quality of Life (AT-EQUAL 2009), pp. 32–35. IEEE Press, New York (2009)CrossRefGoogle Scholar
  2. 2.
    Legatt, A., Arezzo, J., Vaughan, H.G.: Averaged multiple unit activity as an estimate of phasic changes in local neuronal activity: effects of volume–conducted potentials. J. Neurosci. Meth. 2(2), 203–217 (1980)CrossRefGoogle Scholar
  3. 3.
    van Hemmen, J., Ritz, R.: Neural Coding: A Theoretical Vista of Mechanisms, Techniques, and Applications. In: Andersson, S.I. (ed.) Summer University of Southern Stockholm 1993. LNCS, vol. 888, pp. 75–119. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  4. 4.
    Okun, M., Naim, A., Lampl, I.: The subthreshold relation between cortical local field potential and neuronal firing unveiled by intracellular recordings in awake rats. J. Neurosci. 30(12), 4440–4448 (2010)CrossRefGoogle Scholar
  5. 5.
    Ahrens, K.F., Kleinfeld, D.: Current flow in vibrissa motor cortex can phase–lock with exploratory rhythmic whisking in rat. J. Neurophysiol. 92, 1700–1707 (2004)CrossRefGoogle Scholar
  6. 6.
    Kublik, E.: Contextual impact on sensory processing at the barrel cortex of awake rat. Acta. Neurobiol. Exp. 64, 229–238 (2004)Google Scholar
  7. 7.
    Mahmud, M., Bertoldo, A., Girardi, S., Maschietto, M., Vassanelli, S.: Automatic detection of layer activation order in information processing pathways of rat barrel cortex under mechanical whisker stimulation. In: 32nd Intl. Conf. of IEEE EMBS, pp. 6095–6098. IEEE Press, New York (2010)Google Scholar
  8. 8.
    Mahmud, M., et al.: An Automated Method for the Detection of Layer Activation Order in Information Processing Pathways of Rat Barrel Cortex under Mechanical Whisker Stimulation. J. Neurosci. Meth. 196, 141–150 (2011)CrossRefGoogle Scholar
  9. 9.
    Mahmud, M., et al.: A Contour Based Automatic Method to Classify Local Field Potentials Recorded from Rat Barrel Cortex. In: 5th Cairo Intl. Biomed. Eng. Conf., pp. 163–166. IEEE Press, New York (2010)CrossRefGoogle Scholar
  10. 10.
    Mahmud, M., et al.: An Automated Method for Clustering Single Sweep Local Field Potentials Recorded from Rat Barrel Cortex. In: 2011 ISSNIP Biosignals and Biorobotics Conf., pp. 1–5. IEEE Press, New York (2011)CrossRefGoogle Scholar
  11. 11.
    Madsen, K., Nielsen, H.B., Tingleff, O.: Methods for Non–Linear Least Squares Problems, 2nd edn. Technical University of Denmark (DTU), Kgs, Lyngby (2004)Google Scholar
  12. 12.
    Macqueen, J.: Some methods for classification and analysis of multivariate observations. In: Fifth Berkeley Symp. on Math. Statist. and Prob., vol. 1, pp. 281–297. University of California Press, Berkeley California (1967)Google Scholar
  13. 13.
    Bock, H.H.: Clustering Methods: a History of K–Means Algorithms. In: Brito, P., Cucumel, G., Bertrand, P., Carvalho, F. (eds.) Selected Contributions in Data Analysis and Classification, pp. 161–172. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Chiang, M.M.T., Mirkin, B.: Intelligent choice of the number of clusters in K-Means clustering: an experimental study with different cluster spreads. J. Classif. 27(1), 3–40 (2010)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Felderer, F., Fromherz, P.: Transistor needle chip for recording in brain tissue. App. Phys. A. 104, 1–6 (2011)CrossRefGoogle Scholar
  16. 16.
    Swanson, L.W.: Brain Maps: Structure of the Rat Brain. Academic, London (2003)Google Scholar
  17. 17.
    Mahmud, M., Bertoldo, A., Girardi, S., Maschietto, M., Vassanelli, S.: An Automated Method to Determine Angular Preferentiality using LFPs Recorded from Rat Barrel Cortex by Brain-Chip Interface under Mechanical Whisker Stimulation. In: 33rd Intl. Conf. of IEEE EMBS, pp. 2307–2310. IEEE Press, New York (2010)Google Scholar
  18. 18.
    Mahmud, M., Bertoldo, A., Girardi, S., Maschietto, M., Vassanelli, S.: SigMate: a Matlab–based neuronal signal processing tool. In: 32nd Intl. Conf. of IEEE EMBS, pp. 1352–1355. IEEE Press, New York (2010)Google Scholar
  19. 19.
    Mahmud, M., et al.: SigMate: A Comprehensive Software Package for Extracellular Neuronal Signal Processing and Analysis. In: 5th Intl. Conf. on Neural Eng., pp. 88–91. IEEE Press, New York (2011)Google Scholar
  20. 20.
    Mahmud, M., Bertoldo, A., Girardi, S., Maschietto, M., Vassanelli, S.: SigMate: A MATLAB-based automated tool for extracellular neuronal signal processing and analysis. J. Neurosci. Meth. 207(1), 97–112 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mufti Mahmud
    • 1
    • 2
  • Davide Travalin
    • 3
  • Amir Hussain
    • 4
  • Stefano Girardi
    • 1
  • Marta Maschietto
    • 1
  • Florian Felderer
    • 5
  • Stefano Vassanelli
    • 1
  1. 1.NeuroChip LaboratoryUniversity of PadovaPadovaItaly
  2. 2.Institute of Information TechnologyJahangirnagar UniversityDhakaBangladesh
  3. 3.Centro Direzionale ColleoniSt. Jude Medical Italia S.p.AAgrate BrianzaItaly
  4. 4.Centre for Cognitive & Computational NeuroscienceUniversity of StirlingStirlingUK
  5. 5.Max Planck Institute of BiochemistryMartinsriedGermany

Personalised recommendations