Skip to main content

A Framework for Coupled Simulations of Robots and Spiking Neuronal Networks


Bio-inspired robots still rely on classic robot control although advances in neurophysiology allow adaptation to control as well. However, the connection of a robot to spiking neuronal networks needs adjustments for each purpose and requires frequent adaptation during an iterative development. Existing approaches cannot bridge the gap between robotics and neuroscience or do not account for frequent adaptations. The contribution of this paper is an architecture and domain-specific language (DSL) for connecting robots to spiking neuronal networks for iterative testing in simulations, allowing neuroscientists to abstract from implementation details. The framework is implemented in a web-based platform. We validate the applicability of our approach with a case study based on image processing for controlling a four-wheeled robot in an experiment setting inspired by Braitenberg vehicles.


  1. Atkinson, C., Gerbig, R., Markert, K., Zrianina, M., Egurnov, A., Kajzar, F.: Towards a Deep, Domain Specific Modeling Framework for Robot Applications. In: Assmann, U., Wagner, G. (eds.) Proceedings of the 1st International Workshop on Model-Driven Robot Software Engineering (MORSE), no. 1319 in CEUR Workshop Proceedings, 1–12. Aachen. (2014)

  2. Bihlmaier, A., Wörn, H.: Robot Unit Testing. In: Simulation, Modeling, and Programming for Autonomous Robots, pp 255–266. Springer (2014)

  3. Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press, Oxford (1995)

    MATH  Google Scholar 

  4. Bordignon, M., Schultz, U.P., Stoy, K.: Model-based Kinematics Generation for Modular Mechatronic Toolkits. SIGPLAN Not. 46(2), 157–166 (2010). doi:10.1145/1942788.1868318

    Article  Google Scholar 

  5. Braitenberg, V.: Vehicles: Experiments in synthetic psychology MIT press (1986)

  6. Davison, A.P., Brüderle, D., Eppler, J.M., Kremkow, J., Muller, E., Pecevski, D., Perrinet, L., Yger, P.: Pynn: a common interface for neuronal network simulators. Front. Neuroinformatics 2 (11) (2009). doi:10.3389/neuro.11.011.2008,

  7. Davison, A.P., Hines, M.L., Muller, E.: Trends in programming languages for neuroscience simulations. Front. Neurosci. 3(3), 374 (2009)

    Article  Google Scholar 

  8. Di Ruscio, D., Malavolta, I., Pelliccione, P.: A Family of Domain-Specific Languages for Specifying Civilian Missions of Multi-Robot Systems. In: First Workshop on Model-Driven Robot Software Engineering-MORSE (2014)

  9. Fowler, M.: Domain-specific languages Pearson Education (2010)

  10. Frigerio, M., Buchli, J., Caldwell, D.G.: A Domain Specific Language for kinematic models and fast implementations of robot dynamics algorithms (2013)

  11. Gewaltig, M.O., Diesmann, M.: Nest (neural simulation tool). Scholarpedia 2(4), 1430 (2007)

    Article  Google Scholar 

  12. Gleeson, P., Crook, S., Cannon, R.C., Hines, M.L., Billings, G.O., Farinella, M., Morse, T.M., Davison, A.P., Ray, S., Bhalla, U.S., Barnes, S.R., Dimitrova, Y.D., Silver, R.A.: NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological. Detail. PLos Comput. Biol. 6(6), e1000,815 (2010). doi:10.1371/journal.pcbi.1000815

    Article  MathSciNet  Google Scholar 

  13. Hagras, H., Pounds-Cornish, A., Colley, M., Callaghan, V., Clarke, G.: Evolving spiking neural network controllers for autonomous robots. In: Robotics and Automation, 2004. Proceedings. ICRA ’04. 2004 IEEE International Conference on, vol. 5. doi:10.1109/ROBOT.2004.1302446, pp 4620–4626 (2004)

  14. Hines, M.: A program for simulation of nerve equations with branching geometries. Int. J. Biomed. Comput. 24(1), 55–68 (1989)

    Article  Google Scholar 

  15. Hinkel, G., Groenda, H., Vannucci, L., Denninger, O., Cauli, N., Ulbrich, S.: A Domain-Specific Language (DSL) for Integrating Neuronal Networks in Robot Control. In: 2015 Joint MORSE/VAO Workshop on Model-Driven Robot Software Engineering and View-Based Software-Engineering (2015)

  16. Kernighan, B.W., Pike, R.: The Unix Programming Environment, vol. 270. Prentice-Hall Englewood Cliffs, NJ (1984)

  17. Khan, M.M., Lester, D.R., Plana, L.A., Rast, A., Jin, X., Painkras, E., Furber, S.B.: SpiNNaker: Mapping Neural Networks onto a Massively-Parallel Chip Multiprocessor. In: Neural Networks, 2008. IJCNN 2008.(IEEE World Congress on Computational Intelligence). International Joint Conference On, pp. 2849–2856. (2008)

  18. Koenig, N., Howard, A.: Design and Use Paradigms for Gazebo, an Open-Source Multi-Robot Simulator. In: Intelligent Robots and Systems, 2004.(IROS 2004). Proceedings. 2004 IEEE/RSJ International Conference On, Vol. 3, pp. 2149–2154. (2004)

  19. Lier, F., Lütkebohle, I., Wachsmuth, S.: Towards Automated Execution and Evaluation of Simulated Prototype HRI Experiments. In: Proceedings of the 2014 ACM/IEEE International Conference on Human-robot Interaction, HRI’14, pp. 230–231. ACM, New York, NY, USA. doi:10.1145/2559636.2559841 (2014)

  20. Meyerovich, L.A., Rabkin, A.S.: Empirical Analysis of Programming Language Adoption. In: Proceedings of the 2013 ACM SIGPLAN International Conference on Object Oriented Programming Systems Languages & Applications, pp. 1–18. (2013)

  21. Moghadam, M., Christensen, D.J., Brandt, D., Schultz, U.P.: Towards Python-based Domain-specific Languages for Self-reconfigurable Modular Robotics Research (2013)

  22. Nichols, E., McDaid, L., Siddique, N.: Biologically inspired snn for robot control. IEEE Trans. Cybern. 43(1), 115–128 (2013)

    Article  Google Scholar 

  23. Nordmann, A., Hochgeschwender, N., Wrede, S.: A Survey on Domain-Specific Languages in Robotics. In: Simulation, Modeling, and Programming for Autonomous Robots, pp 195–206. Springer (2014)

  24. Plotnikov, D., Blundell, I., Ippen, T., Eppler, J.M., Morrison, A., Rumpe, B.: NESTML: a Modeling Language for Spiking Neurons. In: Modellierung. Accepted, to appear (2016)

  25. Pomerleau, D.A.: Neural Network Perception for Mobile Robot Guidance. Ph.D. Thesis. Carnegie Mellon University, Pittsburgh (1992)

    Google Scholar 

  26. Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an Open-Source Robot Operating System. In: ICRA Workshop on Open Source Software, p 5 (2009)

  27. Raikov, I., Cannon, R., Clewley, R., Cornelis, H., Davison, A., De Schutter, E., Djurfeldt, M., Gleeson, P., Gorchetchnikov, A., Plesser, H., Hill, S., Hines, M., Kriener, B., Le Franc, Y., Lo, C.C., Morrison, A., Muller, E., Ray, S., Schwabe, L., Szatmary, B.: NineML: the network interchange for neuroscience modeling language. BMC Neurosci. 12(Suppl 1), 330 (2011). doi:10.1186/1471-2202-12-S1-P330

    Article  Google Scholar 

  28. Roennau, A., Heppner, G., Nowicki, M., Dillmann, R.: LAURON V: a Versatile Six-Legged Walking Robot with Advanced Maneuverability. In: Advanced Intelligent Mechatronics (AIM), 2014 IEEE/ASME International Conference On, pp. 82–87. (2014)

  29. Schlegel, C., Haßler, T., Lotz, A., Steck, A.: Robotic Software Systems: From Code-Driven to Model-Driven Designs. In: Advanced Robotics, 2009. ICAR 2009. International Conference On, pp 1–8 (2009)

  30. Strey, A.: EpsiloNN - A specification language for the efficient parallel simulation of neural networks. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds.) Biological and Artificial Computation: From Neuroscience to Technology, Lecture Notes in Computer Science. doi:10.1007/BFb0032530, vol. 1240, pp 714–722. Springer, Berlin (1997)

  31. Vannucci, L., Ambrosano, A., Cauli, N., Albanese, U., Falotico, E., Ulbrich, S., Pfotzer, L., Hinkel, G., Denninger, O., Peppicelli, D., Guyot, L., Von Arnim, A., Deser, S., Maier, P., Dillman, R., Klinker, G., Levi, P., Knoll, A., Gewaltig, M.O., Laschi, C.: A visual tracking model implemented on the iCub robot as a use case for a novel neurorobotic toolkit integrating brain and physics simulation. In: Humanoid Robots (Humanoids), 2015 IEEE-RAS 15th International Conference on. doi:10.1109/HUMANOIDS.2015.7363512, pp 1179–1184 (2015)

Download references

Author information



Corresponding author

Correspondence to Georg Hinkel.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hinkel, G., Groenda, H., Krach, S. et al. A Framework for Coupled Simulations of Robots and Spiking Neuronal Networks. J Intell Robot Syst 85, 71–91 (2017).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Neurorobotics
  • Human brain
  • Spiking neuronal networks
  • Domain-specific languages
  • Model-driven engineering