Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
 Lokale TagsFreigabegeschichteDetailsÜbersicht
  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.

Item is

 

einblenden: alle

Basisdaten

ausblenden:
Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-000D-6491-E Versions-Permalink: https://hdl.handle.net/21.11116/0000-000E-6C20-5
Genre: Konferenzbeitrag

Externe Referenzen

ausblenden:
externe Referenz:
https://direct.mit.edu/isal/proceedings/isal/35/59/116924 (Verlagsversion)
Beschreibung:
-
OA-Status:
Keine Angabe

Urheber

ausblenden:
 Urheber:
Khajehabdollahi, S, Autor                 
Giannakakis, E1, Autor                 
Buendia, V2, Autor                 
Martius, G, Autor
Levina, A1, Autor           
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              

Inhalt

ausblenden:
Schlagwörter: -
 Zusammenfassung: 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.

Details

ausblenden:
Sprache(n):
 Datum: 2023-072023
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1162/isal_a_00663
 Art des Abschluß: -

Veranstaltung

ausblenden:
Titel: ALIFE 2023: Ghost in the Machine: The 2021 Conference on Artificial Life
Veranstaltungsort: Sapporo, Japan
Start-/Enddatum: 2023-07-24 - 2023-07-28

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

ausblenden:
Titel: ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference
Genre der Quelle: Konferenzband
 Urheber:
Iizuka, H, Herausgeber
Suzuki, K, Herausgeber
Uno, R, Herausgeber
Damiano, L, Herausgeber
Spychala, N, Herausgeber
Aguilera, M, Herausgeber
Izquierdo, E, Herausgeber
Suzuki, R, Herausgeber
Baltieri, M, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: MIT Press
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 410 - 419 Identifikator: DOI: 10.1162/isal_a_00704