---
layout: single
title: "Extended Self-Replication"
categories: research
tags: artificial-life complex-systems neural-networks self-organization dynamical-systems
excerpt: "Journal extension: self-replication, noise robustness, emergence, dynamical system analysis."
header:
teaser: /assets/figures/15_sr_journal_teaser.jpg
scholar_link: "https://scholar.google.de/citations?user=NODAd94AAAAJ&hl=en"
---
Analyzing the lineage and diversity in populations of self-replicating networks.
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.
Key extensions and analyses presented in this work include:
* **Robustness Analysis:** A systematic evaluation of the self-replicating networks' resilience and stability when subjected to various levels of **noise** during the replication process.
* **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.
* **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).
Investigating the influence of noise on the self-replication process.
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 gabor2022self %}