Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2007, Article ID 12725, 13 pages
http://dx.doi.org/10.1155/2007/12725
Research Article

Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface

1Neuroengineering and Bio-nanotechnology Group, Department of Biophysical and Electronic Engineering (DIBE), University of Genova, Via Opera Pia 11a, Genova 16145, Italy
2NeuroLab, Department of Informatics Systems and Telematics (DIST), Via Opera Pia 13, Genova 16145, Italy
3Center for Neuroscience and Neuroengineering “Massimo Grattarola”, University of Genova, Genova, Viale Benedetto XV, 3 16132, Italy

Received 27 December 2006; Revised 4 April 2007; Accepted 18 June 2007

Academic Editor: Fabio Babiloni

Copyright © 2007 A. Novellino et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. A. Sharp, M. B. O'Neil, L. F. Abbott, and E. Marder, “The dynamic clamp: artificial conductances in biological neurons,” Trends in Neurosciences, vol. 16, no. 10, pp. 389–394, 1993. View at Publisher · View at Google Scholar
  2. G. Le Masson, S. Renaud-Le Masson, D. Debay, and T. Bal, “Feedback inhibition controls spike transfer in hybrid thalamic circuits,” Nature, vol. 417, no. 6891, pp. 854–858, 2002. View at Publisher · View at Google Scholar
  3. D. A. Wagenaar, R. Madhavan, J. Pine, and S. M. Potter, “Controlling bursting in cortical cultures with closed-loop multi-electrode stimulation,” Journal of Neuroscience, vol. 25, no. 3, pp. 680–688, 2005. View at Publisher · View at Google Scholar
  4. G. Shahaf and S. Marom, “Learning in networks of cortical neurons,” Journal of Neuroscience, vol. 21, no. 22, pp. 8782–8788, 2001. View at Google Scholar
  5. S. Marom and D. Eytan, “Learning in ex-vivo developing networks of cortical neurons,” Progress in Brain Research, vol. 147, pp. 189–199, 2005. View at Publisher · View at Google Scholar
  6. B. D. Reger, K. M. Fleming, V. Sanguineti, S. Alford, and F. A. Mussa-Ivaldi, “Connecting brains to robots: an artificial body for studying the computational properties of neural tissues,” Artificial Life, vol. 6, no. 4, pp. 307–324, 2000. View at Publisher · View at Google Scholar
  7. J. Wessberg, C. R. Stambaugh, J. D. Kralik et al., “Real-time prediction of hand trajectory by ensembles of cortical neurons in primates,” Nature, vol. 408, no. 6810, pp. 361–365, 2000. View at Publisher · View at Google Scholar
  8. M. A. L. Nicolelis, “Actions from thoughts,” Nature, vol. 409, no. 6818, pp. 403–407, 2001. View at Publisher · View at Google Scholar
  9. A. B. Schwartz, D. M. Taylor, and S. I. H. Tillery, “Extraction algorithms for cortical control of arm prosthetics,” Current Opinion in Neurobiology, vol. 11, no. 6, pp. 701–708, 2001. View at Publisher · View at Google Scholar
  10. A. Karniel, M. Kositsky, K. M. Fleming et al., “Computational analysis in vitro: dynamics and plasticity of a neuro-robotic system,” Journal of Neural Engineering, vol. 2, no. 3, pp. S250–S265, 2005. View at Publisher · View at Google Scholar
  11. T. B. DeMarse, D. A. Wagenaar, A. W. Blau, and S. M. Potter, “The neurally controlled animat: biological brains acting with simulated bodies,” Autonomous Robots, vol. 11, no. 3, pp. 305–310, 2001. View at Publisher · View at Google Scholar
  12. D. J. Bakkum, A. C. Shkolnik, G. Ben-Ary, P. Gamblen, T. B. DeMarse, and S. M. Potter, “Removing some ‘A’ from AI: embodied cultured networks,” in International Seminar on Embodied Artificial Intelligence, vol. 3139 of Lecture Notes in Artificial Intelligence, pp. 130–145, Dagstuhl Castle, Germany, July 2004. View at Publisher · View at Google Scholar
  13. S. Martinoia, V. Sanguineti, L. Cozzi et al., “Towards an embodied in vitro electrophysiology:the NeuroBIT project,” Neurocomputing, vol. 58–60, pp. 1065–1072, 2004. View at Publisher · View at Google Scholar
  14. S. M. Potter, “Chapter 4 distributed processing in cultured neuronal networks,” Progress in Brain Research, vol. 130, pp. 49–62, 2001. View at Publisher · View at Google Scholar
  15. F. A. Mussa-Ivaldi and L. E. Miller, “Brain-machine interfaces: computational demands and clinical needs meet basic neuroscience,” Trends in Neurosciences, vol. 26, no. 6, pp. 329–334, 2003. View at Publisher · View at Google Scholar
  16. M. A. L. Nicolelis, “Brain-machine interfaces to restore motor function and probe neural circuits,” Nature Reviews Neuroscience, vol. 4, no. 5, pp. 417–422, 2003. View at Publisher · View at Google Scholar
  17. L. Cozzi, P. D'Angelo, M. Chiappalone et al., “Coding and decoding of information in a bi-directional neural interface,” Neurocomputing, vol. 65-66, pp. 783–792, 2005. View at Publisher · View at Google Scholar
  18. P. Maes, “Modeling adaptive autonomous agents,” Artificial Life, vol. 1, no. 1-2, pp. 135–162, 1994. View at Google Scholar
  19. R. A. Brooks, Robot: The Future of Flesh and Machines, Allen Lane, The Penguin Press, London, UK, 2002.
  20. V. Braitenberg, Vehicles: Experiments in Synthetic Psychology, The MIT Press, Cambridge, Mass, USA, 1984.
  21. L. Cozzi, P. D'Angelo, and V. Sanguineti, “Encoding of time-varying stimuli in populations of cultured neurons,” Biological Cybernetics, vol. 94, no. 5, pp. 335–349, 2006. View at Publisher · View at Google Scholar
  22. D. A. Wagenaar, J. Pine, and S. M. Potter, “Effective parameters for stimulation of dissociated cultures using multi-electrode arrays,” Journal of Neuroscience Methods, vol. 138, no. 1-2, pp. 27–37, 2004. View at Publisher · View at Google Scholar
  23. M. Grattarola, M. Chiappalone, F. Davide et al., “Burst analysis of chemically stimulated spinal cord neuronal networks cultured on microelectrode arrays,” in Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, pp. 729–732, Istanbul, Turkey, October 2001.
  24. J. K. Chapin, K. A. Moxon, R. S. Markowitz, and M. A. L. Nicolelis, “Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex,” Nature Neuroscience, vol. 2, no. 7, pp. 664–670, 1999. View at Publisher · View at Google Scholar
  25. J. M. Carmena, M. A. Lebedev, R. E. Crist et al., “Learning to control a brain-machine interface for reaching and grasping by primates,” PLoS Biology, vol. 1, no. 2, pp. 193–208, 2003. View at Publisher · View at Google Scholar
  26. J. Wessberg and M. A. L. Nicolelis, “Optimizing a linear algorithm for real-time robotic control using chronic cortical ensemble recordings in monkeys,” Journal of Cognitive Neuroscience, vol. 16, no. 6, pp. 1022–1035, 2004. View at Publisher · View at Google Scholar
  27. E. E. Fetz, “Real-time control of a robotic arm by neuronal ensembles,” Nature Neuroscience, vol. 2, no. 7, pp. 583–584, 1999. View at Publisher · View at Google Scholar
  28. M. A. Lebedev and M. A. L. Nicolelis, “Brain-machine interfaces: past, present and future,” Trends in Neurosciences, vol. 29, no. 9, pp. 536–546, 2006. View at Publisher · View at Google Scholar
  29. A. P. Georgopoulos, A. B. Schwartz, and R. E. Kettner, “Neuronal population coding on movement direction,” Science, vol. 233, no. 4771, pp. 1416–1419, 1986. View at Publisher · View at Google Scholar
  30. E. Maeda, Y. Kuroda, H. P. C. Robinson, and A. Kawana, “Modification of parallel activity elicited by propagating bursts in developing networks of rat cortical neurones,” European Journal of Neuroscience, vol. 10, no. 2, pp. 488–496, 1998. View at Publisher · View at Google Scholar
  31. Y. Jimbo, A. Kawana, P. Parodi, and V. Torre, “The dynamics of a neuronal culture of dissociated cortical neurons of neonatal rats,” Biological Cybernetics, vol. 83, no. 1, pp. 1–20, 2000. View at Publisher · View at Google Scholar
  32. S. Marom and G. Shahaf, “Development, learning and memory in large random networks of cortical neurons: lessons beyond anatomy,” Quarterly Reviews of Biophysics, vol. 35, no. 1, pp. 63–87, 2002. View at Publisher · View at Google Scholar
  33. S. Martinoia, L. Bonzano, M. Chiappalone, M. Tedesco, M. Marcoli, and G. Maura, “In vitro cortical neuronal networks as a new high-sensitive system for biosensing applications,” Biosensors and Bioelectronics, vol. 20, no. 10, pp. 2071–2078, 2005. View at Publisher · View at Google Scholar
  34. M. Chiappalone, M. Bove, A. Vato, M. Tedesco, and S. Martinoia, “Dissociated cortical networks show spontaneously correlated activity patterns during in vitro development,” Brain Research, vol. 1093, no. 1, pp. 41–53, 2006. View at Publisher · View at Google Scholar
  35. A. Vato, L. Bonzano, M. Chiappalone et al., “Spike manager: a new tool for spontaneous and evoked neuronal networks activity characterization,” Neurocomputing, vol. 58–60, pp. 1153–1161, 2004. View at Publisher · View at Google Scholar
  36. F. Rieke, D. Warland, R. de Ruyter van Steveninck, and W. Bialek, Spikes: Exploring the Neural Code, The MIT Press, Cambridge, Mass, USA, 1997.
  37. D. Eytan, N. Brenner, and S. Marom, “Selective adaptation in networks of cortical neurons,” Journal of Neuroscience, vol. 23, no. 28, pp. 9349–9356, 2003. View at Google Scholar
  38. D. A. Wagenaar and S. M. Potter, “A versatile all-channel stimulator for electrode arrays, with real-time control,” Journal of Neural Engineering, vol. 1, no. 1, pp. 39–45, 2004. View at Publisher · View at Google Scholar
  39. Y. Jimbo, H. P. C. Robinson, and A. Kawana, “Strengthening of synchronized activity by tetanic stimulation in cortical cultures: application of planar electrode arrays,” IEEE Transactions on Biomedical Engineering, vol. 45, no. 11, pp. 1297–1304, 1998. View at Publisher · View at Google Scholar
  40. Y. Jimbo, T. Tateno, and H. P. C. Robinson, “Simultaneous induction of pathway-specific potentiation and depression in networks of cortical neurons,” Biophysical Journal, vol. 76, no. 2, pp. 670–678, 1999. View at Google Scholar
  41. T. Tateno and Y. Jimbo, “Activity-dependent enhancement in the reliability of correlated spike timings in cultured cortical neurons,” Biological Cybernetics, vol. 80, no. 1, pp. 45–55, 1999. View at Publisher · View at Google Scholar
  42. P. Bonifazi, M. E. Ruaro, and V. Torre, “Statistical properties of information processing in neuronal networks,” European Journal of Neuroscience, vol. 22, no. 11, pp. 2953–2964, 2005. View at Publisher · View at Google Scholar
  43. M. E. Ruaro, P. Bonifazi, and V. Torre, “Toward the neurocomputer: image processing and pattern recognition with neuronal cultures,” IEEE Transactions on Biomedical Engineering, vol. 52, no. 3, pp. 371–383, 2005. View at Publisher · View at Google Scholar
  44. M. Chiappalone, P. Massobrio, M. Tedesco, and S. Martinoia, “Stimulus-induced synaptic changes in networks of cortical neurons,” in Proceedings of the 5th International Meeting on Substrate-Integrated Micro Electrode Arrays (SIMEA '06), pp. 32–33, BIOPRO Baden-Wurttemberg GmbH, Reutlingen, Germany, 2006.
  45. G. L. Shaw, Keeping Mozart in Mind, Academic Press, San Diego, Calif, USA, 2nd edition, 2003.
  46. T. G. Deliagina, “Vestibular compensation in lampreys: impairment and recovery of equilibrium control during locomotion,” The Journal of Experimental Biology, vol. 200, no. 10, pp. 1459–1471, 1997. View at Google Scholar
  47. T. G. Deliagina, P. V. Zelenin, P. Fagerstedt, S. Grillner, and G. N. Orlovsky, “Activity of reticulospinal neurons during locomotion in the freely behaving lamprey,” Journal of Neurophysiology, vol. 83, no. 2, pp. 853–863, 2000. View at Google Scholar
  48. D. A. Wagenaar and S. M. Potter, “Real-time multi-channel stimulus artifact suppression by local curve fitting,” Journal of Neuroscience Methods, vol. 120, no. 2, pp. 113–120, 2002. View at Publisher · View at Google Scholar
  49. I. Obeid and P. D. Wolf, “Evaluation of spike-detection algorithms for a brain-machine interface application,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 905–911, 2004. View at Publisher · View at Google Scholar
  50. D. Wagenaar, T. B. DeMarse, and S. M. Potter, “MeaBench: a toolset for multi-electrode data acquisition and on-line analysis,” in Proceedings of the 2nd International IEEE EMBS Conference on Neural Engineering, pp. 518–521, Arlington, Va, USA, March 2005. View at Publisher · View at Google Scholar