Distributed information encoding and decoding using self-organized spatial patterns

Jia Lu; Ryan Tsoi; Nan Luo; Yuanchi Ha; Shangying Wang; Minjun Kwak; Yasa Baig; Nicole Moiseyev; Shari Tian; Alison Zhang; Neil Zhenqiang Gong; Lingchong You

doi:10.1016/j.patter.2022.100590

Distributed information encoding and decoding using self-organized spatial patterns

Patterns (N Y). 2022 Sep 23;3(10):100590. doi: 10.1016/j.patter.2022.100590. eCollection 2022 Oct 14.

Authors

Jia Lu¹, Ryan Tsoi¹, Nan Luo¹, Yuanchi Ha¹, Shangying Wang¹, Minjun Kwak², Yasa Baig³, Nicole Moiseyev², Shari Tian⁴, Alison Zhang⁵, Neil Zhenqiang Gong^{2

5}, Lingchong You^{1

6

7}

Affiliations

¹ Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
² Department of Computer Science, Duke University, Durham, NC 27708, USA.
³ Department of Physics, Duke University, Durham, NC 27708, USA.
⁴ Department of Statistical Science, Duke University, Durham, NC 27708, USA.
⁵ Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA.
⁶ Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA.
⁷ Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27708, USA.

Abstract

Dynamical systems often generate distinct outputs according to different initial conditions, and one can infer the corresponding input configuration given an output. This property captures the essence of information encoding and decoding. Here, we demonstrate the use of self-organized patterns that generate high-dimensional outputs, combined with machine learning, to achieve distributed information encoding and decoding. Our approach exploits a critical property of many natural pattern-formation systems: in repeated realizations, each initial configuration generates similar but not identical output patterns due to randomness in the patterning process. However, for sufficiently small randomness, different groups of patterns that arise from different initial configurations can be distinguished from one another. Modulating the pattern-generation and machine learning model training can tune the tradeoff between encoding capacity and security. We further show that this strategy is scalable by implementing the encoding and decoding of all characters of the standard English keyboard.

Keywords: data science; dynamical systems; information technology; innovation; pattern formation; technology transfer.