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4. Bellec, G., Scherr, F., Hajek, E., Salaj, D., Legenstein, R., Maass, W.: Biologically
inspired alternatives to backpropagation through time for learning in recurrent
neural nets (2019)
5. Bing, Z., Meschede, C., R¨ ohrbein, F., Huang, K., Knoll, A.C.: A survey of robotics
control based on learning-inspired spiking neural networks. Front. Neurorobot.
12, 35 (2018). https://doi.org/10.3389/fnbot.2018.00035. https://www.frontiersin.
org/article/10.3389/fnbot.2018.00035
6. Blouw, P., Choo, X., Hunsberger, E., Eliasmith, C.: Benchmarking keyword spotting efficiency on neuromorphic hardware. In: Proceedings of the 7th Annual Neuroinspired Computational Elements Workshop, pp. 1–8 (2019)
7. Bouganis, A., Shanahan, M.: Training a spiking neural network to control a 4-dof
robotic arm based on spike timing-dependent plasticity. In: The 2010 International
Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2010)
8. da Lio, M., et al.: Exploiting dream-like simulation mechanisms to develop safer
agents for automated driving the “Dreams4Cars” EU research and innovation
action. In: Proceedings of the 20th IEEE International Conference on Intelligent
Transportation Systems (2017). https://doi.org/10.1109/ITSC.2017.8317649
9. Demiris, Y., Dearden, A.: From motor babbling to hierarchical learning by imitation: a robot developmental pathway (2005)
10. DeWolf, T., Stewart, T.C., Slotine, J.J., Eliasmith, C.: A spiking neural model of
adaptive arm control. Proc. R. Soc. B: Biol. Sci. 283(1843), 20162134 (2016)
11. Diehl, P., Cook, M.: Unsupervised learning of digit recognition using spike-timingdependent plasticity. Front. Comput. Neurosci. 9, 99 (2015). https://doi.org/10.
3389/fncom.2015.00099. https://www.frontiersin.org/article/10.3389/fncom.2015.
00099
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13. Eliasmith, C., Anderson, C.H.: Neural Engineering: Computation, Representation,
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14. Eliasmith, C., et al.: A large-scale model of the functioning brain. Science
338(6111), 1202–1205 (2012)
15. Hesslow, G.: Conscious thought as simulation of behaviour and perception. Trends
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1428, 71–79 (2012). https://doi.org/10.1016/j.brainres.2011.06.026
17. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8),
1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
18. Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Netw.
14(6), 1569–1572 (2003)
19. Jirenhed, D.A., Hesslow, G., Ziemke, T.: Exploring internal simulation of perception in mobile robots, vol. 86, pp. 107–113. Lund University Cognitive Studies
(2001)
20. Kheradpisheh, S.R., Ganjtabesh, M., Thorpe, S.J., Masquelier, T.: STDP-based
spiking deep convolutional neural networks for object recognition. Neural Netw.
99, 56–67 (2018)
21. Neftci, E.O., Mostafa, H., Zenke, F.: Surrogate gradient learning in spiking neural
networks. IEEE Signal Process. Mag. 36, 61–63 (2019)