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<a href="http://localhost:4000/research/organism-network-emergence/" itemprop="url">Organism Network Emergence
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<ol class="bibliography"><li><span id="illium2022constructing"><span style="font-variant: small-caps">Illium, S., Zorn, M., Lenta, C., Kölle, M., Linnhoff-Popien, C., and Gabor, T.</span> 2022. Constructing organism networks from collaborative self-replicators. <i>2022 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, IEEE, 1268–1275.</span>
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<p>This research investigates the transition from simple self-replication to higher levels of organization by exploring how populations of basic, self-replicating neural network units can form <strong>“Organism Networks” (ONs)</strong> through <strong>collaboration and emergent differentiation</strong>. Moving beyond the replication of individual networks, the focus shifts to the collective dynamics and functional capabilities that arise when these units interact within a shared environment (akin to an “artificial chemistry”).</p>
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<img src="/assets/figures/16_on_architecture.jpg" alt="Diagram showing individual self-replicating units interacting to form a larger Organism Network structure" style="display:block; width:65%" />
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<figcaption>Conceptual architecture of an Organism Network emerging from interacting self-replicators.</figcaption>
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<p><br /></p>
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<p>The core hypothesis is that through local interactions and potentially shared environmental feedback, initially homogeneous populations of self-replicators can spontaneously develop specialized roles or structures, leading to a collective entity with capabilities exceeding those of individual units.</p>
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<p style="display:block; width:45%" class="align-right"><img src="\assets\figures\16_on_dropout.jpg" alt="Visualization potentially related to network robustness, differentiation, or communication channels." /></p>
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<p>Key aspects explored in this work include:</p>
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<ul>
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<li><strong>Mechanisms for Collaboration:</strong> Investigating how communication or resource sharing between self-replicating units can be established and influence collective behavior.</li>
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<li><strong>Emergent Differentiation:</strong> Analyzing scenarios where units within the population begin to specialize, adopting different internal states (weight configurations) or functions, analogous to cellular differentiation in biological organisms.</li>
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<li><strong>Formation of Structure:</strong> Studying how interactions lead to stable spatial or functional structures within the population, forming the basis of the Organism Network.</li>
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<li><strong>Functional Advantages:</strong> Assessing whether these emergent ONs exhibit novel collective functionalities or improved problem-solving capabilities compared to non-interacting populations. (The role of dropout, as suggested by the image, might relate to promoting robustness or specialization within this context).</li>
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<p>This study bridges the gap between single-unit self-replication and the emergence of complex, multi-unit systems in artificial life research, offering insights into how collaborative dynamics can lead to higher-order computational structures. For more detailed insights, refer to <a class="citation" href="/publications#illium2022constructing">[Illium et al. 2022]</a>.</p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2022-12-01T00:00:00+01:00">December 1, 2022</time></p>
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Aquarium: Open-source MARL environment for predator-prey studies.
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Managed LMU chair IT: Kubernetes, CI/CD, automation (2018-2023).
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<a href="/research/primate-subsegment-sorting/" rel="permalink">Primate Subsegment Sorting
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Binary subsegment presorting improves noisy primate sound classification.
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