This journal article (Gabor et al., 2022) provides an extended and more in-depth exploration of self-replicating neural networks (Gabor et al., 2019), building upon earlier foundational work (Gabor et al., 2019).
The research further investigates the use of backpropagation-like mechanisms not for typical supervised learning, but as an effective means to enable non-trivial self-replication – where networks learn to reproduce their own connection weights.
Key extensions and analyses presented in this work include:
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Robustness Analysis: A systematic evaluation of the self-replicating networks' resilience and stability when subjected to various levels of noise during the replication process.
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Artificial Chemistry Environments: Further development and analysis of simulated environments where populations of self-replicating networks interact, leading to observable emergent collective behaviors and ecosystem dynamics.
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Dynamical Systems Perspective: A detailed theoretical analysis of the self-replication process viewed as a dynamical system. This includes identifying fixpoint weight configurations (networks that perfectly replicate themselves) and characterizing their attractor basins (the regions in weight space from which networks converge towards a specific fixpoint).
By delving deeper into the mechanisms, robustness, emergent properties, and underlying dynamics, this study significantly enhances the understanding of how self-replication can be achieved and analyzed within neural network models, contributing valuable insights to the fields of artificial life and complex systems.