# Information Fragmentation, Encryption and Information Flow in Complex Biological Networks

arXiv: https://arxiv.org/abs/2105.13585 · HTML: https://mlsanity.com/paper/2105.13585/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 46 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 1 of 1)

- [Markov Brains: A Technical Introduction](https://mlsanity.com/paper/1709.05601/) (2017)

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

- [Detecting Information Relays in Deep Neural Networks](https://mlsanity.com/paper/2301.00911/) (2023)

## Abstract (arXiv)

Assessing where and how information is stored in biological networks (such as neuronal and genetic networks) is a central task both in neuroscience and in molecular genetics, but most available tools focus on the network's structure as opposed to its function. Here we introduce a new information-theoretic tool: "information fragmentation analysis" that, given full phenotypic data, allows us to localize information in complex networks, determine how fragmented (across multiple nodes of the network) the information is, and assess the level of encryption of that information. Using information fragmentation matrices, we can also create information flow graphs that illustrate how information propagates through these networks. We illustrate the use of this tool by analyzing how artificial brains that evolved "in silico" solve particular tasks, and show how information fragmentation analysis provides deeper insights into how these brains process information and "think". The measures of information fragmentation and encryption that result from our methods also quantify complexity of information processing in these networks and how this processing complexity differs between primary exposure to sensory data (early in the lifetime) and later routine processing.