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Self-Replication in Neural Networks.

Thomas Gabor1, Steffen Illium2, Maximilian Zorn2

  • 1LMU Munich. thomas.gabor@ifi.lmu.de.

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Summary

Neural networks can self-replicate using backpropagation, demonstrating robustness to noise. Artificial chemistry environments with these networks show emergent behaviors and stable weight configurations.

Keywords:
Neural networkartificial chemistry systemself-replicationsoupweight space

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Theoretical computer science

Background:

  • Self-replication is fundamental to biological systems.
  • Neural networks are crucial for complex emergent behavior in computing.
  • Understanding self-replication in artificial systems is key to advancing AI.

Purpose of the Study:

  • To investigate self-replication capabilities across different neural network types.
  • To analyze the role of backpropagation in facilitating self-replication.
  • To explore emergent behaviors in artificial chemistry environments composed of neural networks.

Main Methods:

  • Analysis of various neural network architectures for self-replication.
  • Utilizing backpropagation for navigating network weight spaces.
  • In-depth robustness testing against noise.
  • Introduction and examination of artificial chemistry environments with multiple neural networks.
  • Analysis of fixpoint weight configurations and attractor basins.

Main Results:

  • Backpropagation naturally enables non-trivial self-replicators.
  • Self-replicating neural networks exhibit significant robustness to noise.
  • Artificial chemistry environments demonstrate complex emergent behaviors.
  • Extensive analysis of fixpoint weight configurations and their attractor basins was performed.

Conclusions:

  • Backpropagation is a key mechanism for achieving self-replication in neural networks.
  • Neural network self-replication is robust and can lead to emergent complexity in simulated environments.
  • Further analysis of weight space dynamics, including fixpoints and attractors, deepens understanding of these systems.