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Neural Cellular Automata Can Respond to Signals

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Neural Cellular Automata Can Respond to Signals. / Stovold, James.
ALIFE 2023: Ghost in the Machine: : Proceedings of the 2023 Artificial Life Conference. Cambridge, Mass.: MIT Press, 2023.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Stovold, J 2023, Neural Cellular Automata Can Respond to Signals. in ALIFE 2023: Ghost in the Machine: : Proceedings of the 2023 Artificial Life Conference. MIT Press, Cambridge, Mass. https://doi.org/10.1162/isal_a_00567

APA

Stovold, J. (2023). Neural Cellular Automata Can Respond to Signals. In ALIFE 2023: Ghost in the Machine: : Proceedings of the 2023 Artificial Life Conference MIT Press. https://doi.org/10.1162/isal_a_00567

Vancouver

Stovold J. Neural Cellular Automata Can Respond to Signals. In ALIFE 2023: Ghost in the Machine: : Proceedings of the 2023 Artificial Life Conference. Cambridge, Mass.: MIT Press. 2023 doi: 10.1162/isal_a_00567

Author

Stovold, James. / Neural Cellular Automata Can Respond to Signals. ALIFE 2023: Ghost in the Machine: : Proceedings of the 2023 Artificial Life Conference. Cambridge, Mass. : MIT Press, 2023.

Bibtex

@inproceedings{6023b0e441504abdbdf272a42b93f5e0,
title = "Neural Cellular Automata Can Respond to Signals",
abstract = "Neural Cellular Automata (NCAs) are a model of morphogenesis, capable of growing two-dimensional artificial organisms from a single seed cell. In this paper, we show that NCAs can be trained to respond to signals. Two types of signal are used: internal (genomically-coded) signals, and external (environmental) signals. Signals are presented to a single pixel for a single timestep.Results show NCAs are able to grow into multiple distinct forms based on internal signals, and are able to change colour based on external signals. Overall these contribute to the development of NCAs as a model of artificial morphogenesis, and pave the way for future developments embedding dynamic behaviour into the NCA model.Code and target images are available through GitHub: https://github.com/jstovold/ALIFE2023",
author = "James Stovold",
year = "2023",
month = jul,
day = "24",
doi = "10.1162/isal_a_00567",
language = "English",
booktitle = "ALIFE 2023: Ghost in the Machine:",
publisher = "MIT Press",

}

RIS

TY - GEN

T1 - Neural Cellular Automata Can Respond to Signals

AU - Stovold, James

PY - 2023/7/24

Y1 - 2023/7/24

N2 - Neural Cellular Automata (NCAs) are a model of morphogenesis, capable of growing two-dimensional artificial organisms from a single seed cell. In this paper, we show that NCAs can be trained to respond to signals. Two types of signal are used: internal (genomically-coded) signals, and external (environmental) signals. Signals are presented to a single pixel for a single timestep.Results show NCAs are able to grow into multiple distinct forms based on internal signals, and are able to change colour based on external signals. Overall these contribute to the development of NCAs as a model of artificial morphogenesis, and pave the way for future developments embedding dynamic behaviour into the NCA model.Code and target images are available through GitHub: https://github.com/jstovold/ALIFE2023

AB - Neural Cellular Automata (NCAs) are a model of morphogenesis, capable of growing two-dimensional artificial organisms from a single seed cell. In this paper, we show that NCAs can be trained to respond to signals. Two types of signal are used: internal (genomically-coded) signals, and external (environmental) signals. Signals are presented to a single pixel for a single timestep.Results show NCAs are able to grow into multiple distinct forms based on internal signals, and are able to change colour based on external signals. Overall these contribute to the development of NCAs as a model of artificial morphogenesis, and pave the way for future developments embedding dynamic behaviour into the NCA model.Code and target images are available through GitHub: https://github.com/jstovold/ALIFE2023

U2 - 10.1162/isal_a_00567

DO - 10.1162/isal_a_00567

M3 - Conference contribution/Paper

BT - ALIFE 2023: Ghost in the Machine:

PB - MIT Press

CY - Cambridge, Mass.

ER -