Encoding of Stimuli in Embodied Neuronal Networks

  • Jacopo Tessadori
  • Daniele Venuta
  • Valentina Pasquale
  • Sreedhar S. Kumar
  • Michela Chiappalone
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8064)

Abstract

Information coding in the central nervous system is still, under many aspects, a mystery. In this work, we made use of cortical and hippocampal cultures plated on micro-electrode arrays and embedded in a hybrid neuro-robotic platform to investigate the basis of “sensory” coding in neuronal cell assemblies. First, we asked which features of the observed spike trains (e.g. spikes, bursts, doublets) may be used to reconstruct significant portions of the input signal, coded as a low-frequency train of stimulations, through optimal linear reconstruction techniques. We also wondered whether preparations of cortical or hippocampal cells might present different coding representations. Our results pointed out that identifying specific signal structures within the spike train does not improve reconstruction performance. We found, instead, differences tied to the cell type of the preparation, with cortical cultures showing less segregated responses than hippocampal ones.

Keywords

spike train burst coding performance micro-electrode array in vitro networks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Potter, S.M., DeMarse, T.B.: A new approach to neural cell culture for long-term studies. J. Neurosci. Methods 110, 17–24 (2001)CrossRefGoogle Scholar
  2. 2.
    Kriegstein, A.R., Dichter, M.A.: Morphological classification of rat cortical neurons in cell culture. J. Neurosci. 3, 1634–1647 (1983)Google Scholar
  3. 3.
    Bakkum, D.J., Shkolnik, A.C., Ben-Ary, G., Gamblen, P., DeMarse, T.B., Potter, S.M.: Removing Some ‘A’ from AI: Embodied Cultured Networks. In: Iida, F., Pfeifer, R., Steels, L., Kuniyoshi, Y. (eds.) Embodied Artificial Intelligence. LNCS (LNAI), vol. 3139, pp. 130–145. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Mulas, M., Massobrio, P., Martinoia, S., Chiappalone, M.: A simulated neuro-robotic environment for bi-directional closed-loop experiments. Paladyn. Journal of Behavioral Robotics 1, 179–186 (2010)CrossRefGoogle Scholar
  5. 5.
    Warwick, D., Xydas, S.J., Nasuto, V.M., Becerra, M.W., Hammond, J.H., Downes, S., Marshall, B.J.: Controlling a Mobile Robot with a Biological Brain. Defence Sci. J. 60, 5–14 (2010)Google Scholar
  6. 6.
    Metzner, W., Koch, C., Wessel, R., Gabbiani, F.: Feature extraction by burst-like spike patterns in multiple sensory maps. J. Neurosci. 18, 2283–2300 (1998)Google Scholar
  7. 7.
    Reinagel, P., Godwin, D., Sherman, S.M., Koch, C.: Encoding of visual information by LGN bursts. Journal Neurophys. 81, 2558–2569 (1999)Google Scholar
  8. 8.
    Martinez-Conde, S., Macknik, S.L., Hubel, D.H.: The function of bursts of spikes during visual fixation in the awake primate lateral geniculate nucleus and primary visual cortex. PNAS 99, 13920–13925 (2002)CrossRefGoogle Scholar
  9. 9.
    Krahe, R., Gabbiani, F.: Burst firing in sensory systems. Nat. Rev. Neurosci. 5, 13–23 (2004)CrossRefGoogle Scholar
  10. 10.
    Tessadori, J., Bisio, M., Martinoia, S., Chiappalone, M.: Modular neuronal assemblies embodied in a closed-loop environment: toward future integration of brains and machines. Front Neural Circuits 6, 99 (2012)CrossRefGoogle Scholar
  11. 11.
    Frega, M., Pasquale, V., Tedesco, M., Marcoli, M., Contestabile, A., Nanni, M., Bonzano, L., Maura, G., Chiappalone, M.: Cortical cultures coupled to Micro-Electrode Arrays: a novel approach to perform in vitro excitotoxicity testing. Neurotoxicol Teratol 34, 116–127 (2012)CrossRefGoogle Scholar
  12. 12.
    Cozzi, L., D’Angelo, P., Sanguineti, V.: Encoding of time-varying stimuli in populations of cultured neurons. Biol. Cybern. 94, 335–349 (2006)MATHCrossRefGoogle Scholar
  13. 13.
    Braitenberg, V.: Vehicles - Experiments in synthetic psychology. The MIT Press, Cambridge (1984)Google Scholar
  14. 14.
    Maccione, A., Gandolfo, M., Massobrio, P., Novellino, A., Martinoia, S., Chiappalone, M.: A novel algorithm for precise identification of spikes in extracellularly recorded neuronal signals. J. Neurosci. Methods 177, 241–249 (2009)CrossRefGoogle Scholar
  15. 15.
    Rolston, J.D., Wagenaar, D.A., Potter, S.M.: Precisely timed spatiotemporal patterns of neural activity in dissociated cortical cultures. Neuroscience 148, 294–303 (2007)CrossRefGoogle Scholar
  16. 16.
    Shahaf, G., Marom, S.: Learning in networks of cortical neurons. J. Neurosci. 21, 8782–8788 (2001)Google Scholar
  17. 17.
    Eytan, D., Marom, S.: Dynamics and effective topology underlying synchronization in networks of cortical neurons. J. Neurosci. 26, 8465–8476 (2006)CrossRefGoogle Scholar
  18. 18.
    Chiappalone, M., Novellino, A., Vajda, I., Vato, A., Martinoia, S., Van Pelt, J.: Burst detection algorithms for the analysis of spatio-temporal patterns in cortical networks of neurons. Neurocomputing 65, 653–662 (2005)CrossRefGoogle Scholar
  19. 19.
    Turnbull, L., Dian, E., Gross, G.: The string method of burst identification in neuronal spike trains. J. Neurosci. Methods 145, 23–35 (2005)CrossRefGoogle Scholar
  20. 20.
    Gabbiani, F., Metzner, W., Wessel, R., Koch, C.: From stimulus encoding to feature extraction in weakly electric fish. Nature 384, 564–567 (1996)CrossRefGoogle Scholar
  21. 21.
    Rieke, F., Warland, D., De Ruyter van Steveninck, R., Spikes, W.B.: Exploring the neural code. MIT press, Cambridge (1997)Google Scholar
  22. 22.
    Welch, P.: The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics 15, 70–73 (1967)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Leondopulos, S.S., Boehler, M.D., Wheeler, B.C., Brewer, G.J.: Chronic stimulation of cultured neuronal networks boosts low-frequency oscillatory activity at theta and gamma with spikes phase-locked to gamma frequencies. J. Neural Eng. 9, 026015 (2012)Google Scholar
  24. 24.
    Levy, O., Ziv, N.E., Marom, S.: Enhancement of neural representation capacity by modular architecture in networks of cortical neurons. Eu. J. Neurosci. 35, 1753–1760 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jacopo Tessadori
    • 1
  • Daniele Venuta
    • 1
    • 2
  • Valentina Pasquale
    • 1
  • Sreedhar S. Kumar
    • 1
    • 3
  • Michela Chiappalone
    • 1
  1. 1.Department of Neuroscience and Brain TechnologiesIstituto Italiano di TecnologiaGenovaItaly
  2. 2.University of PadovaPadovaItaly
  3. 3.University of FreiburgFreiburgGermany

Personalised recommendations