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Environmental variability and network structure determine the optimal plasticity mechanisms in embodied agents

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Giannakakis,  E       
Institutional Guests, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Levina,  A
Institutional Guests, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Giannakakis, E., Khajehabdollahi, S., & Levina, A. (2023). Environmental variability and network structure determine the optimal plasticity mechanisms in embodied agents. In H. Iizuka, K. Suzuki, R. Uno, L. Damiano, N. Spychala, M. Aguilera, et al. (Eds.), ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference (pp. 157-166). MIT Press. doi:10.1162/isal_a_00606.


Cite as: https://hdl.handle.net/21.11116/0000-000D-ADDB-A
Abstract
The evolutionary balance between innate and learned behaviors is highly intricate, and different organisms have found different solutions to this problem. We hypothesize that the emergence and exact form of learning behaviors is naturally connected with the statistics of environmental fluctuations and tasks an organism needs to solve. Here, we study how different aspects of simulated environments shape an evolved synaptic plasticity rule in static and moving artificial agents. We demonstrate that environmental fluctuation and uncertainty control the reliance of artificial organisms on plasticity. Interestingly, the form of the emerging plasticity rule is additionally determined by the details of the task the artificial organisms are aiming to solve. Moreover, we show that co-evolution between static connectivity and interacting plasticity mechanisms in distinct sub-networks changes the function and form of the emerging plasticity rules in embodied agents performing a foraging task.