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  Locally adaptive cellular automata for goal-oriented self-organization

Khajehabdollahi, S., Giannakakis, E., Buendia, V., Martius, G., & Levina, A. (2023). Locally adaptive cellular automata for goal-oriented self-organization. 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. 410-419). MIT Press. doi:10.1162/isal_a_00663.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000D-6491-E Version Permalink: https://hdl.handle.net/21.11116/0000-000E-6C20-5
Genre: Conference Paper

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https://direct.mit.edu/isal/proceedings/isal/35/59/116924 (Publisher version)
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 Creators:
Khajehabdollahi, S, Author                 
Giannakakis, E1, Author                 
Buendia, V2, Author                 
Martius, G, Author
Levina, A1, Author           
Affiliations:
1Institutional Guests, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3505519              
2Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_3017468              

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 Abstract: The essential ingredient for studying the phenomena of emergence is the ability to generate and manipulate emergent systems that span large scales. Cellular automata are the model class particularly known for their effective scalability but are also typically constrained by fixed local rules. In this paper, we propose a new model class of adaptive cellular automata that allows for the generation of scalable and expressive models. We show how to implement computation-effective adaptation by coupling the update rule of the cellular automaton with itself and the system state in a localized way. To demonstrate the applications of this approach, we implement two different emergent models: a self-organizing Ising model and two types of plastic neural networks, a rate and spiking model. With the Ising model, we show how coupling local/global temperatures to local/global measurements can tune the model to stay in the vicinity of the critical temperature. With the neural models, we reproduce a classical balanced state in large recurrent neuronal networks with excitatory and inhibitory neurons and various plasticity mechanisms. Our study opens multiple directions for studying collective behavior and emergence.

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 Dates: 2023-072023
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1162/isal_a_00663
 Degree: -

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Title: ALIFE 2023: Ghost in the Machine: The 2021 Conference on Artificial Life
Place of Event: Sapporo, Japan
Start-/End Date: 2023-07-24 - 2023-07-28

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Title: ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference
Source Genre: Proceedings
 Creator(s):
Iizuka, H, Editor
Suzuki, K, Editor
Uno, R, Editor
Damiano, L, Editor
Spychala, N, Editor
Aguilera, M, Editor
Izquierdo, E, Editor
Suzuki, R, Editor
Baltieri, M, Editor
Affiliations:
-
Publ. Info: MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 410 - 419 Identifier: DOI: 10.1162/isal_a_00704