From Models of Cognition to Robot Control and Back Using SNNs
191
22. Shadmehr, R., Mussa-Ivaldi, F.A.: Adaptive representation of dynamics during
learning of a motor task. J. Neurosci. 14(5), 3208–3224 (1994)
23. Stramandinoli, F., Marocco, D., Cangelosi, A.: The grounding of higher order concepts in action and language: a cognitive robotics model. Neural Netw. 32, 165–
173 (2012). https://doi.org/10.1016/j.neunet.2012.02.012. Selected Papers from
IJCNN 2011
24. Thill, S.: Considerations for a neuroscience-inspired approach to the design of artificial intelligent systems. In: Schmidhuber, J., Th´ orisson, K.R., Looks, M. (eds.)
AGI 2011. LNCS (LNAI), vol. 6830, pp. 247–254. Springer, Heidelberg (2011).
https://doi.org/10.1007/978-3-642-22887-2 26
25. Thill, S., Vernon, D.: How to design emergent models of cognition for applicationdriven artificial agents. In: Twomey, K., Westermann, G., Monaghan, P., Smith,
A. (eds.) Neurocomputational Models of Cognitive Development and Processing: Proceedings of the 14th Neural Computation and Psychology Workshop, pp.
115–129. World Scientific Publishing, Singapore (2016). https://doi.org/10.1142/
9789814699341 0008
26. Tieck, J.C.V., Steffen, L., Kaiser, J., Roennau, A., Dillmann, R.: Controlling a
robot arm for target reaching without planning using spiking neurons. In: 2018
IEEE 17th International Conference on Cognitive Informatics Cognitive Computing (ICCI*CC), pp. 111–116 (2018)
27. Universal Robots: Ur5e (2019). https://www.universal-robots.com/products/ur5robot/
28. Vernon, D.: Artificial Cognitive Systems: A primer. MIT Press, Cambridge (2014)
29. Voelker, A.R.: A solution to the dynamics of the prescribed error sensitivity learning rule. Centre for Theoretical N Euro-Science, Waterloo (2015)
30. Wu, Y., Deng, L., Li, G., Zhu, J., Shi, L.: Spatio-temporal backpropagation for
training high-performance spiking neural networks. Front. Neurosci. 12, 331 (2018).
https://doi.org/10.3389/fnins.2018.00331. https://www.frontiersin.org/article/10.
3389/fnins.2018.00331
31. Ziemke, T., Jirenhed, D.A., Hesslow, G.: Internal simulation of perception: a minimal neuro-robotic model. Neurocomputing 68, 85–104 (2005). https://doi.org/10.
1016/j.neucom.2004.12.005
191
22. Shadmehr, R., Mussa-Ivaldi, F.A.: Adaptive representation of dynamics during
learning of a motor task. J. Neurosci. 14(5), 3208–3224 (1994)
23. Stramandinoli, F., Marocco, D., Cangelosi, A.: The grounding of higher order concepts in action and language: a cognitive robotics model. Neural Netw. 32, 165–
173 (2012). https://doi.org/10.1016/j.neunet.2012.02.012. Selected Papers from
IJCNN 2011
24. Thill, S.: Considerations for a neuroscience-inspired approach to the design of artificial intelligent systems. In: Schmidhuber, J., Th´ orisson, K.R., Looks, M. (eds.)
AGI 2011. LNCS (LNAI), vol. 6830, pp. 247–254. Springer, Heidelberg (2011).
https://doi.org/10.1007/978-3-642-22887-2 26
25. Thill, S., Vernon, D.: How to design emergent models of cognition for applicationdriven artificial agents. In: Twomey, K., Westermann, G., Monaghan, P., Smith,
A. (eds.) Neurocomputational Models of Cognitive Development and Processing: Proceedings of the 14th Neural Computation and Psychology Workshop, pp.
115–129. World Scientific Publishing, Singapore (2016). https://doi.org/10.1142/
9789814699341 0008
26. Tieck, J.C.V., Steffen, L., Kaiser, J., Roennau, A., Dillmann, R.: Controlling a
robot arm for target reaching without planning using spiking neurons. In: 2018
IEEE 17th International Conference on Cognitive Informatics Cognitive Computing (ICCI*CC), pp. 111–116 (2018)
27. Universal Robots: Ur5e (2019). https://www.universal-robots.com/products/ur5robot/
28. Vernon, D.: Artificial Cognitive Systems: A primer. MIT Press, Cambridge (2014)
29. Voelker, A.R.: A solution to the dynamics of the prescribed error sensitivity learning rule. Centre for Theoretical N Euro-Science, Waterloo (2015)
30. Wu, Y., Deng, L., Li, G., Zhu, J., Shi, L.: Spatio-temporal backpropagation for
training high-performance spiking neural networks. Front. Neurosci. 12, 331 (2018).
https://doi.org/10.3389/fnins.2018.00331. https://www.frontiersin.org/article/10.
3389/fnins.2018.00331
31. Ziemke, T., Jirenhed, D.A., Hesslow, G.: Internal simulation of perception: a minimal neuro-robotic model. Neurocomputing 68, 85–104 (2005). https://doi.org/10.
1016/j.neucom.2004.12.005