Associative pattern recognition through macro-molecular self-assembly

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.

arXiv comment13 pages, 7 figures; reference added

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Cited by in corpus (9)

• Memory formation in matter
• Equilibrium mechanisms of self-limiting assembly
• Learning without neurons in physical systems
• Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly
• Temperature Protocols to Guide Selective Self-Assembly of Competing Structures
• Learning to self-fold at a bifurcation
• Learning about learning by many-body systems
• Tuning and jamming reduced to their minima
• Continuous-time multifarious systems -- Part I: equilibrium multifarious self-assembly