29 lines
2.7 KiB
Plaintext
29 lines
2.7 KiB
Plaintext
---
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title: "Extended Self-Replication"
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tags: [artificial-life, complex-systems, neural-networks, self-organization, dynamical-systems]
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excerpt: "Journal extension: self-replication, noise robustness, emergence, dynamical system analysis."
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teaser: "/figures/15_sr_journal_teaser.jpg"
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---
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# Extended Self-Replication
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<div className="my-6 text-center">
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<Image src="/figures/15_sr_journal_children.jpg" alt="Visualization showing the evolution or diversity of 'child' networks generated through self-replication" className="w-2/3 mx-auto rounded-lg" />
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<figcaption className="text-sm text-muted-foreground mt-2">Analyzing the lineage and diversity in populations of self-replicating networks.</figcaption>
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</div>
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This journal article provides an extended and more in-depth exploration of self-replicating neural networks, building upon earlier foundational work ([Gabor et al., 2019](link-to-previous-paper-if-available)). 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.
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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).
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<div className="my-6 text-center">
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<Image src="/figures/15_noise_levels.jpg" alt="Graph showing the impact of different noise levels on self-replication fidelity or population dynamics" className="w-2/3 mx-auto rounded-lg" />
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<figcaption className="text-sm text-muted-foreground mt-2">Investigating the influence of noise on the self-replication process.</figcaption>
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</div>
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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. <Cite bibtexKey="gabor2022self" /> |