# Scaling up the self-optimization model by means of on-the-fly computation of weights

arXiv: https://arxiv.org/abs/2211.01698 · HTML: https://mlsanity.com/paper/2211.01698/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 3 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Self-Organization and Artificial Life](https://mlsanity.com/paper/1903.07456/) (2019)
- [Modeling complex systems with adaptive networks](https://mlsanity.com/paper/1301.2561/) (2013)
- [Neural coordination can be enhanced by occasional interruption of normal firing patterns: A self-optimizing spiking neural network model](https://mlsanity.com/paper/1409.0470/) (2014)
- [The distribution of inhibitory neurons in the C. elegans connectome facilitates self-optimization of coordinated neural activity](https://mlsanity.com/paper/2010.15272/) (2020)

## Built on by (most cited first, 1 of 1)

- [On the use of associative memory in Hopfield networks designed to solve propositional satisfiability problems](https://mlsanity.com/paper/2307.16807/) (2023)

## Abstract (arXiv)

The Self-Optimization (SO) model is a useful computational model for investigating self-organization in "soft" Artificial life (ALife) as it has been shown to be general enough to model various complex adaptive systems. So far, existing work has been done on relatively small network sizes, precluding the investigation of novel phenomena that might emerge from the complexity arising from large numbers of nodes interacting in interconnected networks. This work introduces a novel implementation of the SO model that scales as Oleft(N2right) with respect to the number of nodes N, and demonstrates the applicability of the SO model to networks with system sizes several orders of magnitude higher than previously was investigated. Removing the prohibitive computational cost of the naive Oleft(N3right) algorithm, our on-the-fly computation paves the way for investigating substantially larger system sizes, allowing for more variety and complexity in future studies.