Architecture for Neuronal Cell Control of a Mobile Robot

Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 44)


It is usually expected that the intelligent controlling mechanism of a robot is a computer system. Research is however now ongoing in which biological neural networks are being cultured and trained to act as the brain of an interactive real world robot – thereby either completely replacing or operating in a cooperative fashion with a computer system. Studying such neural systems can give a distinct insight into biological neural structures and therefore such research has immediate medical implications. In particular, the use of rodent primary dissociated cultured neuronal networks for the control of mobile ‘animats’ (artificial animals, a contraction of animal and materials) is a novel approach to discovering the computational capabilities of networks of biological neurones. A dissociated culture of this nature requires appropriate embodiment in some form, to enable appropriate development in a controlled environment within which appropriate stimuli may be received via sensory data but ultimate influence over motor actions retained. The principal aims of the present research are to assess the computational and learning capacity of dissociated cultured neuronal networks with a view to advancing network level processing of artificial neural networks. This will be approached by the creation of an artificial hybrid system (animat) involving closed loop control of a mobile robot by a dissociated culture of rat neurons. This ‘closed loop’ interaction with the environment through both sensing and effecting will enable investigation of its learning capacity This paper details the components of the overall animat closed loop system and reports on the evaluation of the results from the experiments being carried out with regard to robot behaviour.


Dissociated neurones robotic animats culture stimulation neural plasticity 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Shkolnik, A.C.: Neurally controlled simulated robot: applying cultured neurons to handle an approach / avoidance task in real time, and a framework for studying learning in vitro, Emory University. Mathematics and Computer Science (2003)Google Scholar
  2. 2.
    DeMarse, T.B., et al.: The Neurally Controlled Animat: Biological Brains Acting with Simulated Bodies. In: Autonomous Robots, vol. 11, pp. 305–310. Kluwer Academic Publishers, Dordrecht (2001)Google Scholar
  3. 3.
    Neurally-Controlled Animat. Potter Group. [Online] [Cited: 08 29, 07.],
  4. 4.
    DeMarse, T.B., Dockendorf, K.P.: Adaptive flight control with living neuronal networks on microelectrode arrays. In: Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN 2005), pp. 1548–1551 (2005)Google Scholar
  5. 5.
    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
  6. 6.
    Multi Channel Systems Homepage. [Online] [Cited: 09 16, 07.],
  7. 7.
    Miabot Pro Research Pack. Merlin Robotics - Creators of Robots for people. [Online] [Cited: 09 07, 07.] (1880),
  8. 8.
    Merlin Robotics - Creators of Robots for people. Merlin Robotics. [Online] [Cited: 10 31, 07.],
  9. 9.
    Measurement Computing Corp.: Product: ’PCI-DAS1200’. Data Acquisition from Measurement Computing. [Online] [Cited: 09 07, 07.],
  10. 10.
    Wagenaar, D.A.: Meabench. Wagenaar’s Meabench Multi-electrode data acquisition and analysis. [Online] [Cited: 07 21, 07.],
  11. 11.
  12. 12.
    The Robot Soccer Project. In: Robot Lab - University of Nottingham. [Online] [Cited: 09 10, 07.],
  13. 13.
    Miabot Plugin Manual (Wiki). Robot Lab - University of Nottingham. [Online] [Cited: 11 04, 2007.],
  14. 14.
    Virtual Reality Toolbox. The MathWorks - MATLAB and Simulink for Technical Computing. [Online] [Cited: 11 05, 2007.],
  15. 15.
    Autodesk 3ds Max. Autodesk. [Online] [Cited: 09 16, 07.],
  16. 16.
    Potter, S., et al.: NeuroLab. The Laboratory for Neuroengineering (NeuroLab). [Online] [Cited: 08 29, 07.],
  17. 17.
    Pinelab at Caltech. The Pine Lab. [Online] [Cited: 09 17, 07.],
  18. 18.
    Biomedical Engineering Homepage. Pruitt Family Department of Biomedical Engineering. [Online] [Cited: 09 17, 07.],
  19. 19.
    Francis, J.T., Gluckman, B.J., Schiff, S.J.: Sensitivity of Neurons to Weak Electric Fields. Part 19. Journal of Neuroscience 23, 7255–7280 (2003)Google Scholar
  20. 20.
    Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Computational Intelligence Society, IEEE Transactions on Neural Networks 11, 586–600 (2000)CrossRefGoogle Scholar
  21. 21.
    Bishop, C.M., Svensén, M., Williams, C.K.I.: GTM: The Generative Topographic Mapping. Neural Computation 10, 215–234 (1998)CrossRefGoogle Scholar
  22. 22.
    Watkins, C.J.C.H., Dayan, P.: Q-learning. In: Machine Learning, vol. 8, pp. 279–292. Springer, Netherlands (1992)Google Scholar
  23. 23.
    Tesauro, G.: Practical issues in temporal difference learning. In: Machine Learning, vol. 8, pp. 257–277. Springer, Netherlands (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  1. 1.School of Systems EngineeringUniversity of ReadingUK
  2. 2.School of PharmacyUniversity of ReadingUK

Personalised recommendations