JRM Vol.19 No.5 pp. 592-600
doi: 10.20965/jrm.2007.p0592


Biomodeling System - Interaction Between Living Neuronal Networks and the Outer World

Suguru N. Kudoh*, Chie Hosokawa*, Ai Kiyohara*,***,
Takahisa Taguchi*, and Isao Hayashi**

*Cell Engineering Research Institute (RICE), National Institute of Advanced Industrial Science and Technology (AIST), 1-8-31 Midorigaoka, Ikeda, Osaka 563-8577, Japan

**Faculty of Informatics, Kansai University, 2-1-1 Ryozenji-cho, Takatsuki, Osaka 569-1095, Japan

***Department of Chemistry, Osaka University, 1-1 Machikaneyama-cho, Toyonaka, Osaka, 563-0043, Japan

April 24, 2007
May 23, 2007
October 20, 2007
neuron, dissociated culture system, multielectrode array (MED), fuzzy logic, moving robot
Rat hippocampal neurons reorganized into complex networks in a culture dish with 64 planar microelectrodes and the electrical activity of neurons were recorded from individual sites. Multi-site recording system for extracellular action potentials was used for recording the activity of living neuronal networks and for applying input from the outer world to the network. The living neuronal network was able to distinguish among patterns of evoked action potentials based on different input, suggesting that the living neuronal network can express several pattern independently, meaning that it has fundamental mechanisms for intelligent information processing. We are developing a “biomodeling system,” in which a living neuronal network is connected to a moving robot with premised control rules corresponding to a genetically provided interface of neuronal networks to peripheral systems. Premised rules are described in fuzzy logic and the robot can generate instinctive behavior, avoiding collision. Sensor input from the robot body was sent to a neuronal network, and the robot moved based on commands from the living neuronal network. This is a good modeling system to analyze interaction between biological information processing and electrical devices.
Cite this article as:
S. Kudoh, C. Hosokawa, A. Kiyohara, T. Taguchi, and I. Hayashi, “Biomodeling System - Interaction Between Living Neuronal Networks and the Outer World,” J. Robot. Mechatron., Vol.19 No.5, pp. 592-600, 2007.
Data files:
  1. [1] J. McCarthy and P. Hays, “Some philosophical problems from the standpoint of artificial intelligence,” Machine Intelligence, 4, pp. 463-502, 1969.
  2. [2] Y. Jimbo, A. Kawana, P. Parodi, and V. Torre, “The dynamics of a neuronal culture of dissociated cortical neurons of neonatal rats,” Biol. Cybern., 83(1), pp. 1-20, 2000.
  3. [3] S. N. Kudoh, R. Nagai, K. Kiyosue, and T. Taguchi, “PKC and CaMKII dependent synaptic potentiation in cultured cerebral neurons,” Brain Research, 915(1), pp. 79-87, 2001.
  4. [4] S. N. Kudoh, A. Matsuo, K. Kiyosue, M. Kasai, and T. Taguchi, “Long-lasting enhancement of synaptic activity in dissociated cerebral neurons induced by brief exposure to Mg2+-free conditions,” Neurosci. Res., 28(4), pp. 337-344, 1997.
  5. [5] T. Bliss and T. Lomo, “Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path,” J. Physiol., 232(2), pp. 331-356, 1973.
  6. [6] D. O. Hebb, “The Organization of Behavior: a neuropsychological theory,” Wiley, 1949.
  7. [7] T. Sasaki, N. Matsuki, and Y. Ikegaya, “Metastability of active CA3 networks,” J. Neurosci., 27, pp. 517-528, 2007.
  8. [8] T. Tateno and Y. Jimbo, “Activity-dependent enhancement in the reliability of correlated spike timings in cultured cortical neurons,” Biol Cybern, 80(1), pp. 45-55, 1999.
  9. [9] 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, 11, pp. 305-310, 2001.
  10. [10] D. J. Bakkum, A. C. Shkolnik, G. Ben-Ary, P. Gamblen, T. B. De-Marse, and S. M. Potter, “Removing some ‘A’ from AI: Embodied Cultured Networks,” in F. Iida, R. Pfeifer, L. Steels, and Y. Kuniyoshi (Eds.), pp. 130-145, Springer New York, 2004.
  11. [11] S. N. Kudoh, K. Kiyosue, and T. Taguchi, “A synaptic potentiation by a protein factor distinct from those induced by neurotrophins,” Int. J. Dev. Neurosci., 20(1), pp. 55-62, 2002.
  12. [12] K. V. Gopal and G. W. Gross, “Auditory cortical neurons in vitro: cell culture and multichannel extracellular recording,” Acta Otolaryngol, 116(5), pp. 690-696, 1996.
  13. [13] H. Oka, K. Shimono, R. Ogawa, H. Sugihara, and M. Taketani, “A new planar multielectrode array for extracellular recording: application to hippocampal acute slice,” J. Neurosci. Methods, 93(1), pp. 61-67, 1999.
  14. [14] S. N. Kudoh and T. Taguchi, “A simple exploratory algorithm for the accurate and fast detection of spontaneous synaptic events,” Biosens Bioelectronics, 17(9), pp. 773-782, 2002.
  15. [15] I. Hayashi, H. Nomura, and N. Wakami, “Acquisition of inference rules by neural network driven fuzzy reasoning,” Japanese Journal of Fuzzy Theory and Systems, 12(4), pp. 453-469, 1995.
  16. [16] H. Nomura, I. Hayashi, and N. Wakami. “A self-tuning method of fuzzy reasoning by genetic algorithm,” Fuzzy Control Systems, A. Kandel, G. Langholz (Eds.), CRC Press, pp. 337-354, 1994.
  17. [17] “Using Data Socket Technology,” LabVIEW User Manual, pp. 18-2 - 18-11, National Instruments, 2003
  18. [18] G. Barrionuevo and T. H. Brown, “Associative long-term potentiation in hippocampal slices,” Proc. Natl. Acad. Sci. USA, 80(23), pp. 7347-7351, 1983.
  19. [19] S. N. Kudoh and T. Taguchi, “Operation of spatiotemporal patterns stored in living neuronal networks cultured on a microelectrode array,” J. Adv. Computational Intelligence & Intelligent Informatics (JACIII) ‘Special Issue on Pattern Recognition,’ 8(2), pp. 100-107, 2003.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024