# Associative pattern recognition through macro-molecular self-assembly

## TL;DR

This paper demonstrates that macro-molecular self-assembly can perform high-dimensional pattern recognition and classification by utilizing dynamical attractors, revealing fundamental links between physical assembly parameters and recognition capabilities.

## Contribution

It introduces a novel perspective connecting self-assembly processes with associative neural network models, highlighting their similarities to continuous attractor networks.

## Key findings

- Self-assembly can recognize and classify high-dimensional patterns.
- Recognition performance relates to physical parameters like nucleation barriers.
- Self-assembly exhibits similarities to continuous attractor neural networks.

## 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.