# Neosis: A Computational Framework for Biologically-Inspired Self-Modifying Organisms **Author:** Tooraj Helmi **Date:** 2025 ## Abstract Artificial neural networks have achieved remarkable success, but they remain fundamentally static—their architectures are fixed at design time and only weights are adjusted during training. This structural rigidity stands in contrast to biological cognition, where neural circuits develop, specialize, and reorganize throughout an organism's lifetime. Neosis addresses this limitation by proposing a foundation in which cognitive systems are **alive in a computational sense**. The basic organism, the *Neo*, is a self-modifying graph dynamical system whose nodes carry local computational rules, whose edges define information flow, and whose structure can reorganize over time through mutation and energy-driven selection. This document develops the formal model of Neosis, analyzes its computational and learning capabilities, and demonstrates how populations of Neos can evolve increasingly sophisticated adaptive behaviors. The framework bridges micro-level node dynamics with macro-level mathematical analysis, providing both mechanistic understanding and predictive power for large-scale evolutionary systems. *** **Copyright © 2025 Tooraj Helmi. All rights reserved.** This work is submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.neosis.science/readme.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.