Nest-Building Using Place Cells for Spatial Navigation in an Artificial Neural Network
Authors: Thomas E. Portegys
Abstract:
An animal behavior problem is presented in the form of a nest-building task that involves two cooperating virtual birds, a male and female. The female builds a nest into which she lays an egg. The male's job is to forage in a forest for food for both himself and the female. In addition, the male must fetch stones from a nearby desert for the female to use as nesting material. The task is completed when the nest is built, and an egg is laid in it. A goal-seeking neural network and a recurrent neural network were trained and tested with little success. The goal-seeking network was then enhanced with “place cells”, allowing the birds to spatially navigate the world, building the nest while keeping themselves fed. Place cells are neurons in the hippocampus that map space.
Keywords: Artificial animal intelligence, artificial life, goal-seeking neural network, nest-building, place cells, spatial navigation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 0References:
[1] Portegys, T.E. (2022). I want to play a game. https://www.researchgate.net/publication/364089546_I_WANT_TO_PLAY_A_GAME
[2] Portegys, T.E. (2001). Goal-Seeking Behavior in a Connectionist Model. Artificial Intelligence Review 16, 225–253 (2001). https://doi.org/10.1023/A:1011970925799
[3] Moser M.B., Rowland D.C., Moser E.I. Place cells, grid cells, and memory. (2015). Cold Spring Harbor Perspectives in Biology. Feb 2;7(2):a021808. doi: 10.1101/cshperspect.a021808. PMID: 25646382; PMCID: PMC4315928.
[4] Robinson, N. T. M., Descamps, L. A. L., Russell, L. E., Buchholz, M. O., Bicknell, B. A., Antonov, G. K., Lau, J. Y. N., Nutbrown, R., Schmidt-Hieber, C., Häusser, M. (2020). Targeted activation of hippocampal place cells drives memory-guided spatial behavior. Cell, 183, pp. 1586-1599.
[5] Xu, H., Baracskay, P., O’Neill, J., and Csicsvari, J. (2019). Assembly responses of hippocampal CA1 place cells predict learned behavior in goal-directed spatial tasks on the radial eight-arm maze. Neuron 101, 119–132.
[6] Milford, M. and Wyeth, G., (2010). Persistent navigation and mapping using a biologically inspired SLAM system, Int. J. Robot. Res. 29:1131–1153.
[7] Zhou, X., Weber, C. and Wermter, S., (2017). Robot localization and orientation detection based on place cells, Proc. ICANN 2017, Springer pp. 137–145.
[8] OpenAI. (2023). ChatGPT (Mar 14 version) Large language model. https://chat.openai.com/chat
[9] Zador, A. (2019). A critique of pure learning and what artificial neural networks can learn from animal brains. Nature Communications volume 10, Article number: 3770. https://www.nature.com/articles/s41467-019-11786-6
[10] Braitenberg, V. (1984). Vehicles: Experiments in synthetic psychology. Cambridge, MA: MIT Press. "Vehicles - the MIT Press". Archived from the original on 2010-01-29. Retrieved 2012-06-18.
[11] Dyer, M.G. (1993). Toward Synthesizing Artificial Neural Networks that Exhibit Cooperative Intelligent Behavior: Some Open Issues in Artificial Life. Artificial Life, vol. 1, no. 1_2, pp. 111-134, Oct. 1993, doi: 10.1162/artl.1993.1.1_2.111.
[12] Coleman S.L., Brown V.R., Levine D.S., Mellgren R.L. (2005). A neural network model of foraging decisions made under predation risk. Cogn Affect Behav Neurosci. 2005 Dec;5(4):434-51. doi: 10.3758/cabn.5.4.434. PMID: 16541813.
[13] Ericksen, J., Moses, M. and Forrest, S. (2017). Automatically evolving a general controller for robot swarms. 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA, 2017, pp. 1-8, doi: 10.1109/SSCI.2017.8285399
[14] Stanley, K. O. and R. Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary computation, vol. 10, no. 2, pp. 99–127.
[15] Lizier, J.T., Piraveenan, M., Pradhana, D., Prokopenko, M., Yaeger, L.S. Functional and Structural Topologies in Evolved Neural Networks. ECAL 2009.
[16] Enquist, M. and Ghirlanda, S. (2006). Neural Networks and Animal Behavior. Volume 33 in the series Monographs in Behavior and Ecology Published by Princeton University Press. https://doi.org/10.1515/9781400850785
[17] Wijeyakulasuriya, D.A., Eisenhauer, E.W., Shaby, B.A., Hanks E.M. (2020). Machine learning for modeling animal movement. PLoS ONE 15(7): e0235750. https://doi.org/10.1371/journal.pone.0235750
[18] Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
[19] Code with instructions: https://github.com/morphognosis/NestingBirds
[20] Moerland, T.M., Broekens, J., Plaat, A., Jonker, C.M. (2023). Model-based Reinforcement Learning: A Survey. Foundations and Trends in Machine Learning Series. Now Publishers. https://books.google.com/books?id=FimgzwEACAAJ
[21] Jenkins, H. M. (1979). Animal Learning & Behavior Theory. Ch. 5 in Hearst, E. The First Century of Experimental Psychology Hillsdale N. J., Earlbaum.
[22] Grieves R. M., Wood E. R., Dudchenko P. A. (2016). Place cells on a maze encode routes rather than destinations. Elife. Jun 10;5:e15986. doi: 10.7554/eLife.15986.
[23] Scenario video: https://youtu.be/d13hxhltsGg
[24] Lee, S.W., O’Doherty, J.P., Shimojo, S., (2015). Neural computations mediating one-shot learning in the human brain. PLoS biology 13.