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5. Chanc´ an, M., Hernandez-Nunez, L., Narendra, A., Barron, A.B., Milford, M.:
A hybrid compact neural architecture for visual place recognition. IEEE Robot.
Autom. Lett. 5(2), 993–1000 (2020)
6. Funahashi, K., Nakamura, Y.: Approximation of dynamical systems by continuous
time recurrent neural networks. Neural Netw. 6(6), 801–806 (1993)
7. Graham, P., Cheng, K.: Ants use the panoramic skyline as a visual cue during
navigation. Curr. Biol. 19(20), R935–R937 (2009)
8. Graham, P., Mangan, M.: Insect navigation: do ants live in the now? J. Exp. Biol.
218(6), 819–823 (2015)
9. Graham, P., Philippides, A.: Vision for navigation: what can we learn from ants?
Arthropod Struct. Dev. 46(5), 718–722 (2017)
10. Green, B.: Canny edge detection tutorial (2002). Accessed 6 Mar 2005
11. Hausler, S., Jacobson, A., Milford, M.: Multi-process fusion: visual place recognition using multiple image processing methods. IEEE Robot. Autom. Lett. 4(2),
1924–1931 (2019)
12. Knight, J.C., et al.: Insect-inspired visual navigation on-board an autonomous
robot: real-world routes encoded in a single layer network. In: The 2018 Conference on Artificial Life: A Hybrid of the European Conference on Artificial Life
(ECAL) and the International Conference on the Synthesis and Simulation of Living Systems (ALIFE), pp. 60–67. MIT Press (2019)
13. Kodzhabashev, A., Mangan, M.: Route following without scanning. In: Wilson,
S.P., Verschure, P.F.M.J., Mura, A., Prescott, T.J. (eds.) LIVINGMACHINES
2015. LNCS (LNAI), vol. 9222, pp. 199–210. Springer, Cham (2015). https://doi.
org/10.1007/978-3-319-22979-9 20
14. Lin, T., Horne, B.G., Tino, P., Giles, C.L.: Learning long-term dependencies in
NARX recurrent neural networks. IEEE Trans. Neural Netw. 7(6), 1329–1338
(1996)
15. Mangan, M., Webb, B.: Spontaneous formation of multiple routes in individual
desert ants (cataglyphis velox). Behav. Ecol. 23(5), 944–954 (2012)
16. McKinney, W.: Data structures for statistical computing in python. In: van der
Walt, S., Millman, J. (eds.) Proceedings of the 9th Python in Science Conference,
pp. 56–61 (2010). https://doi.org/10.25080/Majora-92bf1922-00a
17. Milford, M.J., Wyeth, G.F.: SeqSLAM: visual route-based navigation for sunny
summer days and stormy winter nights. In: 2012 IEEE International Conference
on Robotics and Automation, pp. 1643–1649. IEEE (2012)
18. M¨ uller, J., Nawrot, M., Menzel, R., Landgraf, T.: A neural network model for
familiarity and context learning during honeybee foraging flights. Biol. Cybern.
112(1), 113–126 (2017). https://doi.org/10.1007/s00422-017-0732-z
19. M¨ uller, M., Wehner, R.: Path integration provides a scaffold for landmark learning
in desert ants. Curr. Biol. 20(15), 1368–1371 (2010)
20. Oliphant, T.: NumPy: A guide to NumPy. Trelgol Publishing, USA (2006). http://
www.numpy.org/
21. Philippides, A., Baddeley, B., Cheng, K., Graham, P.: How might ants use
panoramic views for route navigation? J. Exp. Biol. 214(3), 445–451 (2011)
22. Philippides, A., Graham, P., Baddeley, B., Husbands, P.: Using neural networks to
understand the information that guides behavior: a case study in visual navigation.
In: Cartwright, H. (ed.) Artificial Neural Networks. MMB, vol. 1260, pp. 227–244.
Springer, New York (2015). https://doi.org/10.1007/978-1-4939-2239-0 14
23. Schwarz, S., Mangan, M., Webb, B., Wystrach, A.: Route-following ants respond
to alterations of the view sequence. J. Exp. Biol. (2020)