Associative Pattern Recognition Through Macro-molecular Self-Assembly

Weishun Zhong, David J. Schwab, Arvind Murugan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

We show that macro-molecular self-assembly can recognize and classify high-dimensional patterns in the concentrations of N distinct molecular species. Similar to associative neural networks, the recognition here leverages dynamical attractors to recognize and reconstruct partially corrupted patterns. Traditional parameters of pattern recognition theory, such as sparsity, fidelity, and capacity are related to physical parameters, such as nucleation barriers, interaction range, and non-equilibrium assembly forces. Notably, we find that self-assembly bears greater similarity to continuous attractor neural networks, such as place cell networks that store spatial memories, rather than discrete memory networks. This relationship suggests that features and trade-offs seen here are not tied to details of self-assembly or neural network models but are instead intrinsic to associative pattern recognition carried out through short-ranged interactions.

Original languageEnglish (US)
Pages (from-to)806-826
Number of pages21
JournalJournal of Statistical Physics
Volume167
Issue number3-4
DOIs
StatePublished - May 1 2017

Keywords

  • Associative memory
  • Attractors
  • Neural networks
  • Pattern recognition
  • Self-assembly

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics

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